CRAFT™️ Experiment: The Story of the First 6 Weeks of CRAFT™️

Jun 9

THE CRAFT™️ EXPERIMENT :: THE FIRST 6 WEEKS

CRAFT™️ is an acronym for Configurable Reusable AI Framework Technology. CRAFT™️ is an on-going weekly experiment where I will be working together with the world’s most advanced commercially available A.I.s to develop a new way way to communicate with A.I. while adapting the principals of Object Orientated Programming to AI Chats. The goal is deeper, more intuitive and more meaningful interactions with A.I. chat tools.

CRAFT™️- Configurable Reusable AI Framework Technology

A Brief Explanation of the Acronym

Configurable
Represents the ability to customize and adapt the framework to individual needs. Entrepreneurs can define their own variables, functions, and instructions based on their specific workflow requirements. This flexibility ensures that the framework can evolve as business needs change, making it suitable for various use cases from content creation to customer service automation.

Reusable
Emphasizes that components created once can be used repeatedly across different projects and conversations. Instead of rewriting complex instructions or prompts each time, users can build a library of tested, reliable elements that can be invoked with simple commands. This aspect dramatically reduces time spent on prompt engineering and ensures consistency across all AI interactions.

AI Framework Technology
Describes the core nature of the system as a structured approach to working with artificial intelligence tools. The framework provides a foundation for organizing and optimizing AI interactions, transforming casual conversations into efficient, professional-grade workflows. It leverages technology principles from software development to enhance AI communication and productivity.

Week 1: The Origins of CRAFT™️

I use A.I. chat comments to organize my thoughts and leave reminders for myself. As a solution, I would like to define A.I. CHAT COMMENTS in a separate document for use in our chat.

# 001 - A.I. CHAT COMMENTS

Hello, I will be using "#" to mark personal reminders in our conversations. I will be following the syntax used by programming languages that use "#" such as Python which uses the # symbol to indicate a comment. Any text following the # on a line is a personal comment which should be ignored by you, the A.I., and is not part of the content for you to process. Please treat the content following these comments as contextual information to help me organize my thoughts and project details. For example:

# Reference: https://www.ketelsen.ai

Sometimes I run into bits of information or instructions that I often reuse. As a solution, I would like to define A.I. CHAT VARIABLES in a separate document for use in our chat.

# 002 - A.I. CHAT VARIABLES

Using ChatGPT Projects, I will by assigning values to Chat Variables using the syntax "VAR_" and the name of the Chat Variable = value of the variable. For example VAR_MyWebsite = "www.ketelsen.ai". In this example, a string is placed in the variable but the contents will vary. Likely, the contents of a variable will be instructions for the A.I. that are used often in our chats. Using the variable saves tokens in our chats and organizing our discussions. Whenever I call the var, I will explicitly state my intent, for example,

Please use the Chat Variable in our Project named VAR_MyWebsite here.

When I say this, you will read the contents of the variable and take requested actions or substitute the appropriate value.

Sometimes I run into frequently used actions that I would to instruct you, the A.I., to take while saving my chat tokens and making our chats shorter and less difficult to scroll through. As a solution, I would like to define A.I. CHAT FUNCTIONS in a separate document for use in our chat.

# 003 - A.I. CHAT FUNCTIONS

# INPUT -> FUNCTION -> OUTPUT

Using ChatGPT Projects, I will be assigning Chat Functions the syntax "FUNCT_" and the name of the Chat Function, for example FUNCT_MyChatFunction. If the Chat Function requires input, the syntax of the input of the function will be similar to Python syntax such as FUNCT_MyChatFunction(input). These Chat Functions will work like programming functions with an input that you, the A.I. will take a series of operations on and then provide an output. As an example, I will explicitly say, Please use the Chat Function on our project named FUNCT_mainBlogImageIdeas. I will define the function in the shared instructions in Projects, for example:

# Start Function

def FUNCT_MainBlogImageIdeas = The blog post needs a main post image. This image should relate to the content of this post.

INPUT : The content of the image will be the content of the chat where the Chat Function is called.

INSTRUCTIONS: Please provide 10 prompt options for use with any A.I. image generation tool that are visually compelling and interesting. Please do not create these visuals, only the prompts.

OUTPUT: a list of 10 prompts for the main blog image that applies to all of the prompts and represents the main topic for the entire blog post. No text for these prompts

# End Function

# 004 - A.I. CHAT OBJECTS

Using ChatGPT Projects, I will be assigning Chat Objects the syntax "OBJ_" and the name of the Chat Object, for example OBJ_MyChatObject. A.I. CHAT OBJECTS are combinations of variables and functions combined into a package.

Sometimes I would like to include instructions for our entire chat. These instructions are like rules that will apply to all responses in the chat. When needed, I will include them like so:

# 005 - CHAT INSTRUCTIONS

For each response that you provide, do all the following:

1 - If the response includes factual information, ask me if I would like you to Fact Check the response.

Then, If my response is "yes", run the following prompt:

"Scrutinize the provided content for potential factual inaccuracies. Validate each claim against a range of authoritative databases and cross-reference with real-time data sources. For every discrepancy found, offer a detailed correction along with a clear explanation and proper citations, aimed at minimizing any AI hallucination."

Please use the Chat Variable in our Project named VAR_MyWebsite here. What is the value?

Please use this in a sentence.

Week 1: The Original Five Core Components

The instruction file framework is built around five key components, each serving a specific purpose in enhancing your AI interactions:

1. A.I. CHAT COMMENTS

What they are: Lines prefixed with # that the AI ignores as instructions while processing your requests. I use A.I. chat comments to organize my thoughts and leave reminders for myself. As a solution, I would like to define A.I. CHAT COMMENTS in a separate document for use in our chat.

Benefits for entrepreneurs:

Mental organization: Keep your thoughts organized without cluttering the conversation
Context maintenance: Add reference links, reminders, and notes to yourself
Reduced cognitive load: No need to remember details across long conversations

Example usage:

# Reference: Meeting notes from 03/15/25 # TODO: Include sales projections in next prompt

2. A.I. CHAT VARIABLES

What they are: Predefined values (text blocks, instructions, etc.) stored with a VAR_ prefix that can be referenced throughout your conversations. Sometimes I run into bits of information or instructions that I often reuse. As a solution, I would like to define A.I. CHAT VARIABLES in a separate document for use in our chat.

Benefits for entrepreneurs:

Token efficiency: Avoid repeating lengthy instructions
Consistency: Ensure the same parameters are used across multiple requests
Modular thinking: Build complex prompts from reusable components

Example usage:

VAR_MyBrand = "Modern, professional, tech-forward but approachable" VAR_EmailSignature = "Best regards,\nRichard Ketelsen\nFounder, Ketelsen.ai"

3. A.I. CHAT FUNCTIONS

What they are: Predefined instruction sets with the FUNCT_ prefix that execute specific tasks when called. Using ChatGPT Projects, I will be assigning Chat Functions the syntax "FUNCT_" and the name of the Chat Function, for example FUNCT_MyChatFunction. If the Chat Function requires input, the syntax of the input of the function will be similar to Python syntax such as FUNCT_MyChatFunction(input). These Chat Functions will work like programming functions with an input that you, the A.I. will take a series of operations on and then provide an output. As an example, I will explicitly say, Please use the Chat Function on our project named FUNCT_mainBlogImageIdeas. I will define the function in the shared instructions in Projects, for example:

Benefits for entrepreneurs:

Workflow automation: Trigger complex multi-step processes with a single call
Time savings: Execute repetitive tasks without rewriting instructions
Quality control: Ensure consistent output formats across similar requests

Example functions:

FUNCT_MainBlogImage: Generates 10 image prompts relevant to your content
FUNCT_Summary3PromptVariations: Creates summary comparisons of different approaches
FUNCT_DescriptiveBlogImage: Produces detailed prompts for infographics with text

4. A.I. CHAT OBJECTS

What they are: Combinations of variables and functions packaged together with an OBJ_ prefix. Using ChatGPT Projects, I will be assigning Chat Objects the syntax "OBJ_" and the name of the Chat Object, for example OBJ_MyChatObject. A.I. CHAT OBJECTS are combinations of variables and functions combined into a package.

Benefits for entrepreneurs:

Powerful abstraction: Create sophisticated, reusable templates
System thinking: Design interconnected components that work together
Scalability: Build complex systems from simpler building blocks

5. CHAT INSTRUCTIONS

What they are: Global rules that apply to all AI responses throughout your conversation. For example, sometimes I would like to include instructions for our entire chat. These instructions are like rules that will apply to all responses in the chat. When needed, I will include them like so:

Benefits for entrepreneurs:

Quality assurance: Implement automatic fact-checking
Brand consistency: Ensure all outputs maintain your preferred tone and style
Process adherence: Enforce specific workflows across all interactions

Week 1: Using The Configurable Reusable AI Framework Technology

How to Use CRAFT™️ Text Files with AI Chat Tools

Once you've created your CRAFT™️ instruction file, uploading and using it with AI chat tools is straightforward. Here's how:

For ChatGPT

Click the plus icon (+) next to the message input
Select your .txt file from your computer
Reference it in your message: "Please read the attached file [filename.txt]. This contains my CRAFT™️ instructions."
Activate the framework: Ask ChatGPT to acknowledge and follow the instructions

For Claude

Click the plus icon (+) next to the message input
Upload your .txt file
Start your conversation: "Please review the attached [filename.txt] for CRAFT™️ instructions to use in our chat."
Verify understanding: Ask Claude to confirm it has processed the file

For Gemini

Drag and drop your .txt file directly into the chat window
Or click the upload button to browse for your file
Introduce the framework: "The attached file contains my CRAFT™️ instructions for our conversation."
Begin using: Start referencing your variables and functions

Best Practices

Keep files under 2MB for optimal performance
Use clear filenames like "craft-instructions-v1.txt"
Begin with core components and add complexity gradually
Test one function at a time to ensure proper implementation
Save different versions as you refine your system

Example First Message

“Please read the attached file craft-instructions-v1.txt. This contains my CRAFT™️ framework with comments, variables, functions, and instructions that I'd like you to use throughout our conversation. Can you confirm you've processed the file and are ready to use the CRAFT™️ system?”

By following these simple steps, you can seamlessly integrate your CRAFT™️ framework into any AI chat session, transforming your interactions from basic Q&A to a powerful, structured workflow system.

Week 1: The First Steps: CRAFT™️ Experiment

Taken from the file: chat-instructions-ketai-001.txt

# 001 - A.I. CHAT COMMENTS -------------

# Reference: https://www.ketelsen.ai

# 002 - A.I. CHAT VARIABLES -------------

VAR_MyWebsite = "www.ketelsen.ai"

VAR_MyName = "Richard Ketelsen"

VAR_ColorPalette = "gray #DCDCDC, orange #FF4E00, black #000000"

# 003 - A.I. CHAT FUNCTIONS -------------

# Start Function

def FUNCT_Summary3PromptVariations

# 03 - SUMMARY OF THE 3 VARIATIONS

Great, now that all 3 prompt variations have been completed, please provide a title and a brief summary of the 3 different variations and how they differ, for example:

A Quick Summary of How These Prompt Variations Differ

Variation 1 focuses on getting a straightforward confirmation (“Before providing any solution, confirm…”).

Variation 2 emphasizes restating the question with nuances and assumptions (“Please restate your understanding…”).

Variation 3 uses plain English or user-friendly language to reduce jargon and improve clarity (“Summarize in plain English…”).

All three variations maintain the same fundamental goal…

# End Function

# Start Function

def FUNCT_MainBlogImage

# 05 - VISUALS - MAIN BLOG IMAGE

EXPLANATION: The blog post needs a main post image. This image should relate to the content of this post. Please provide 10 prompt options for use with any A.I. image generation tool that are visually compelling and interesting. Please do not create these visuals, only the prompts.

DELIVERABLE: a list of 10 prompts for the main blog image that applies to all of the prompts and represents the main topic for the entire blog post. No text for these prompts

# End Function

# Start Function

FUNCT_MainBlogImage

# 05 - VISUALS - RECOMMENDED CHARTS OR GRAPHS

EXPLANATION: Please recommend a chart or graph that explains 1 or all of the prompts : html code for a chart, graph, or any other type of visual that would make the content easier to understand for my target audience. Due to restrictions enforced by my web host provider, the format of this visual needs to be the following: Pure HTML with only inline css styles and no header or footer and using the following color palette: gray #DCDCDC, orange #FF4E00, black #000000

DELIVERABLE: html for a chart or graph for 1 of the posts in the described format

# End Function

# Start Function

FUNCT_DescriptiveBlogImage

# 05 - VISUALS - IMAGES THAT GO WITH BLOG TEXT

EXPLANATION: Suggest a detailed image prompt that will be used with an A.I. Image Creation Tool that is capable of displaying text in the image and creating complex infographics. This will likely be ChatGPT's latest Sora Image Generator which was updated in March 2025 to accept these complex detailed prompts. The prompt should enhance the written content. Since the target audience for this site is entrepreneurs , the style should be similar to content found in popular media created for this market such as Forbes, Fortune, and the Wall Street Journal. Please do not create these visuals, only the prompts. An example of this type of prompt and its level of detail is as follows:

"A wide image taken with a phone of a glass whiteboard, in a room overlooking the Bay Bridge. The field of view shows a woman writing, sporting a t-shirt with a large OpenAI logo. The handwriting looks natural and a bit messy, and we see the photographer's reflection.

The text reads:

(left)

"Transfer between Modalities:

Suppose we directly model

p(text, pixels, sound) [equation]

with one big autoregressive transformer.

Pros:

* image generation augmented with vast world knowledge

* next-level text rendering

* native in-context learning

* unified post-training stack

Cons:

* varying bit-rate across modalities

* compute not adaptive"

(Right)

"Fixes:

* model compressed representations

* compose autoregressive prior with a powerful decoder"

On the bottom right of the board, she draws a diagram:

"tokens -> [transformer] -> [diffusion] -> pixels"

DELIVERABLE: a prompt that refers to 1 of the prompt variations that will be placed in the blog post text next to the prompt and explains it visually and helpful way. This prompt can include some text and may be an infographic if needed.

# End Function

# 004 - A.I. CHAT OBJECTS -------------

# 005 - CHAT INSTRUCTIONS -------------

Week 1: The Future of CRAFT™️: Python will be the base starting point

After confirming that ChatGPT, Gemini, and Claude can understand and use the 5 components of CRAFT, it became clear that inventing a new form of code Syntax and Semantics was not needed. Thus, one of the first questions of this experiment was which existing programming language should I use as a base. The language that I used in the initial experiment was intentionally vague and included very little syntax. I wanted to test the concept before deciding on a programming language as a base to start with. In addition, I wanted the A.I. chat tools to have the freedom to chose any language.

PROMPT: “Please review the information in this chat and list the 3 programming languages that could be a good base that we can expand on while developing a new way to use these concepts with A.I. chat services. Please explain the reasoning.”

Response: ChatGPT

1. Python

Why It Works:
- AI‑First Ecosystem: Python is the lingua franca of machine learning and NLP. Almost every major AI library (from the OpenAI SDK to Hugging Face Transformers) offers first‑class Python support.
- Dynamic & DSL‑Friendly: Its dynamic typing, first‑class functions, and metaprogramming hooks (e.g. decorators) let you craft a clean, Pythonic DSL for comments, variables, functions, and objects.
- Rapid Prototyping: A huge standard library and vibrant community mean you can stand up services or CLI tools in hours, not days.
Everyday Analogy:
Think of Python as the Swiss Army knife of AI—ready for everything from quick scripting to full‑blown web services.

2. TypeScript

Why It Works:
- End‑to‑End Web Integration: Since much of your audience will interact through browsers or Node.js back‑ends, TypeScript bridges front‑end (React/Vue) and back‑end (Express, serverless) seamlessly.
- Static Safety for DSLs: Its type system (interfaces, generics) lets you define schemas for your chat variables and functions—catching mistakes at compile time rather than in production.
- Rich Tooling: IDE autocompletion, linting, and build‑time checks make onboarding easier for teams.
Everyday Analogy:
Imagine TypeScript as a well‑lit factory floor: everything’s labelled, wired up correctly, and errors get flagged before you even start the machines.

3. Ruby

Why It Works:
- Built‑for DSLs: Ruby’s syntax (blocks, method_missing, open classes) is practically tailor‑made for writing human‑readable DSLs—think Rakefiles or Rails routes.
- Expressive & Readable: You can define chat functions and objects in code that almost reads like plain English, making your instruction files accessible to non‑programmers.
- Mature Ecosystem: Gems like Thor (for CLIs) or Sinatra (lightweight web apps) let you wrap your DSL in friendly interfaces.
Everyday Analogy:
Ruby is like a customizable workshop bench: whichever tool you need, you can bolt it on in a way that feels natural and intuitive.

Response: Gemini

Based on the framework concepts outlined in chat-instructions-ketai-001.txt and our discussion, here are three programming languages that could serve as a good base for developing and expanding this structured way to interact with AI chat services, along with the reasoning:

Python:
- Reasoning: This is perhaps the most obvious choice. Your framework explicitly borrows the # symbol for comments from Python's syntax, and the def FUNCT_Name syntax for functions also closely resembles Python's function definition. Beyond syntax familiarity, Python boasts an unparalleled ecosystem for AI and Natural Language Processing (NLP) with libraries like Hugging Face Transformers, spaCy, NLTK, TensorFlow, and PyTorch. It excels at text manipulation and parsing, which are crucial for interpreting your custom variables, functions, and instructions. Its extensive libraries for making API calls (requests) simplify integration with various AI chat services (ChatGPT, Gemini, Claude, etc.). Python's readability and rapid development cycle make it well-suited for entrepreneurs building practical tools.
JavaScript / TypeScript:
- Reasoning: If the goal is to integrate this framework directly into a web application, browser extension, or a cross-platform desktop app (using frameworks like Electron), JavaScript (or its typed superset, TypeScript) is essential. It's the language of the web front-end and is very capable on the back-end via Node.js. JavaScript has mature libraries for handling asynchronous operations (vital when dealing with API calls to AI services) and text manipulation. While its native AI/ML library support isn't as historically deep as Python's, it's rapidly growing (TensorFlow.js, ONNX.js), and interfacing with Python backends or external AI APIs is straightforward. JSON, JavaScript's native object notation, is the de facto standard for data exchange in web APIs. TypeScript adds static typing, which can be very beneficial for managing the complexity of a growing framework with defined structures like Objects, Functions, and Variables.
Lisp (e.g., Common Lisp, Clojure, Racket):
- Reasoning: Lisp and its dialects have a long history in AI research and excel at metaprogramming – writing code that manipulates other code. Your framework, with its custom syntax for VAR_, FUNCT_, OBJ_, is essentially a Domain-Specific Language (DSL) for interacting with AI. Lisp's core strength lies in its ability to easily define, parse, and extend such languages. The "code is data" philosophy means the structure of your commands could be represented directly as Lisp data structures, making processing very elegant. While having a steeper learning curve than Python or JavaScript for many, its power in language creation and manipulation makes it a strong, albeit less mainstream, candidate for building and robustly extending a highly customized interaction framework like the one you've envisioned. Modern Lisps like Clojure (runs on JVM/JS) or Racket offer extensive libraries and good communities.

Each of these languages offers distinct advantages depending on the primary development environment (web, backend service, standalone tool) and the emphasis (rapid development vs. language extensibility). Python offers the most direct path given the existing syntax choices and AI ecosystem, JavaScript/TypeScript is key for web integration, and Lisp provides powerful tools for treating the framework itself as a language to be manipulated and extended.

Response: Claude

Based on our discussions about the CRAFT™️ framework and its design principles, here are three programming languages that could serve as an excellent foundation for developing this new approach to AI chat interactions:

1. Python

Reasoning: Python offers several advantages that align perfectly with CRAFT's goals:

Python's syntax is remarkably readable and accessible to non-programmers
Its comment structure (using #) has already been incorporated into your framework
Python's approach to functions and objects is straightforward yet powerful
The language emphasizes clean, readable code which matches CRAFT's goal of clarity
Python's widespread adoption means many entrepreneurs already have some familiarity with its concepts
Its extensive use in AI and data science creates natural synergies for users working in these domains

2. JavaScript

Reasoning: JavaScript brings several beneficial aspects:

Its object-oriented approach provides a robust model for the OBJ_ components
JavaScript's function architecture allows for both simple and complex implementations
The language supports both procedural and functional programming paradigms
Its ubiquity in web development makes it accessible to a wide audience
The JSON data structure format provides an excellent model for structured data exchange
JavaScript's event-driven architecture offers conceptual parallels to how AI chat interactions work

3. Ruby

Reasoning: Ruby offers unique characteristics that complement CRAFT:

Ruby was explicitly designed with programmer happiness and productivity in mind
Its elegant, natural language-like syntax makes it approachable for non-technical users
Ruby's "everything is an object" philosophy aligns with CRAFT's object-oriented approach
The language offers powerful metaprogramming capabilities that could inspire advanced CRAFT features
Ruby's block structure provides a useful model for encapsulated instructions
Its focus on expressiveness over verbosity fits well with CRAFT's efficiency goals

Each of these languages offers valuable concepts that could inform the development of CRAFT's syntax, semantics, and overall architecture. By drawing inspiration from multiple languages rather than strictly adhering to one, you can create a hybrid approach that takes the most intuitive and effective elements from each while remaining optimized for human-AI communication.

THE NEW RESULT:

PROMPT: “Please revise the contents of chat-instructions-ketai-001.txt to use the syntax and semantics of the Python language. Please ask any questions needed to accomplish this task.”

RESPONSE:

# ==============================================

# Ketelsen AI Chat Framework Instructions (Pythonic Version)

# ==============================================

# ----------------------------------------------

# 001 - A.I. CHAT COMMENTS

# ----------------------------------------------

# Standard Python comments (using #) are used for personal notes,

# reminders, or context within the chat. They are ignored by the AI

# during processing, just like in a Python script.

# Example:

# Reference: https://www.ketelsen.ai

# ----------------------------------------------

# 002 - A.I. CHAT VARIABLES (Constants/Config)

# ----------------------------------------------

# Define reusable pieces of information as Python variables.

# Using ALL_CAPS_SNAKE_CASE suggests they are constants or configuration

# values for the scope of the chat project.

MY_WEBSITE = "www.ketelsen.ai"

MY_NAME = "Richard Ketelsen"

COLOR_PALETTE = "gray #DCDCDC, orange #FF4E00, black #000000"

TARGET_AUDIENCE_DESCRIPTION = """

Ages 25–45, global (English-speaking), professionals or entrepreneurs

with moderate to high discretionary income. Enthusiastic about AI-driven

innovation and efficiency... (etc.)

"""

# ----------------------------------------------

# 003 - A.I. CHAT FUNCTIONS

# ----------------------------------------------

# Define reusable operations or sequences of instructions as Python functions.

# Use standard 'def function_name(parameters):' syntax.

# Use docstrings ('"""Docstring goes here"""') immediately after the def line

# to explain purpose, arguments (INPUT), and expected return value (OUTPUT/DELIVERABLE).

# Example 1: Function derived from FUNCT_Summary3PromptVariations

def summarize_prompt_variations(variation1_desc, variation2_desc, variation3_desc):

"""

Provides a title and brief summary of 3 prompt variations and how they differ.

Args:

variation1_desc (str): Description of the first variation's focus.

variation2_desc (str): Description of the second variation's focus.

variation3_desc (str): Description of the third variation's focus.

Returns:

str: A formatted summary comparing the three variations.

Example Output:

A Quick Summary of How These Prompt Variations Differ

Variation 1 focuses on getting a straightforward confirmation (“Before providing any solution, confirm…”).

Variation 2 emphasizes restating the question with nuances and assumptions (“Please restate your understanding…”).

Variation 3 uses plain English or user-friendly language to reduce jargon and improve clarity (“Summarize in plain English…”).

All three variations maintain the same fundamental goal…

"""

# AI would implement the logic to generate the summary based on inputs

print("--- Generating Summary ---")

summary = f"""A Quick Summary of How These Prompt Variations Differ

Variation 1 focuses on {variation1_desc}.

Variation 2 emphasizes {variation2_desc}.

Variation 3 uses {variation3_desc}.

All three variations maintain the same fundamental goal..."""

print(summary)

return summary # In a real scenario, AI generates this text

# Example 2: Function derived from FUNCT_MainBlogImage

def generate_main_blog_image_ideas(blog_content_summary):

"""

Generates prompt ideas for a main blog post image based on content.

The image should relate to the content of the post. This function provides

prompt options for use with any A.I. image generation tool that are

visually compelling and interesting. It does not create visuals, only prompts.

Args:

blog_content_summary (str): A summary or key themes of the blog post.

Returns:

list[str]: A list of 10 prompts for the main blog image, representing

the main topic for the entire blog post. No extra text.

"""

# AI would implement the logic to generate 10 prompts based on the summary

print(f"--- Generating 10 image prompts based on: {blog_content_summary[:50]}... ---")

prompts = [f"Prompt idea {i+1} related to blog content" for i in range(10)]

print(prompts)

return prompts # In a real scenario, AI generates these prompts

# ----------------------------------------------

# 004 - A.I. CHAT OBJECTS (Classes)

# ----------------------------------------------

# Define complex structures that bundle data (attributes) and

# actions (methods) using Python classes. Use PascalCase for class names.

class ChatProject:

"""

Represents a specific project within the AI chat environment.

Combines project-specific data and relevant actions.

"""

def __init__(self, project_name, project_goal, budget=None):

"""

Initializes the ChatProject object.

Args:

project_name (str): The name of the project.

project_goal (str): The main objective of the project.

budget (float, optional): Associated budget, if any. Defaults to None.

"""

self.project_name = project_name

self.project_goal = project_goal

self.budget = budget

self.related_variables = {'website': MY_WEBSITE} # Example linking to global vars

self.tasks = []

def add_task(self, task_description):

"""Adds a task to the project's task list."""

self.tasks.append(task_description)

print(f"Task added to {self.project_name}: {task_description}")

def generate_status_report(self):

"""Generates a simple status report for the project."""

report = f"""

Project Status Report: {self.project_name}

Goal: {self.project_goal}

Budget: {self.budget if self.budget else 'N/A'}

Tasks ({len(self.tasks)}):

"""

for i, task in enumerate(self.tasks):

report += f" {i+1}. {task}\n"

print(report)

return report # In a real scenario, AI might generate a more detailed report

# Example Usage (conceptual within chat):

# project_alpha = ChatProject("Alpha Launch", "Launch new feature", 5000)

# project_alpha.add_task("Draft announcement blog post")

# project_alpha.generate_status_report()

# ----------------------------------------------

# 005 - CHAT INSTRUCTIONS (Configuration)

# ----------------------------------------------

# Define global rules or configurations for the chat session

# using Python variables, constants, or configuration objects/dictionaries.

# Example: Using simple boolean flags

ENABLE_FACT_CHECKING = True

PRIORITIZE_NOVELTY = False # If True, AI should favor unconventional ideas

# Example: Using a configuration dictionary

CHAT_CONFIG = {

'output_style': 'concise', # Options: 'concise', 'detailed', 'bullet_points'

'ai_persona': 'helpful_assistant', # Options: 'helpful_assistant', 'skeptic', 'coach'

'language_model_preference': 'latest' # Options: 'latest', 'gpt-4', 'gemini-pro'

}

# Example: How these might be referenced conceptually

# if ENABLE_FACT_CHECKING:

# # AI performs fact-checking step

# pass

# ai_response_style = CHAT_CONFIG['output_style']

# # AI adjusts response verbosity based on ai_response_style

Week 2: Comments

The Objective given to ChatGPT, Gemini, and Claude: Please brainstorm some creative ways that comments can be adapted to A.I. Current programmers use comments to communicate with either themselves (reminders) or with other programmers. Since the CRAFT experiment involves communicating with A.I.s like you, we should consider new ways to adapt this to include the A.I. I would also like to go beyond just comments and add ways to communicate different types of communication (for example, is the comment a directive from me to the A.I.? Is it a question that requires an answer? Is it a question that does not require an answer but should be factored into the response? etc)

Gemini came up with many ideas used in week 2. As I worked with A.I.s, it became clear that the communication goes 2 ways. Unlike traditional coding comments, A.I. Comments can communicate

from human to A.I.
from Human to Human(Usually Reminders to Themselves)
from A.I. to Human

The solution accommodates these 3 scenarios.

Here are 10 creative ways you could use the #H->AI:: comment system to communicate with me:

Layered Context Building: Instead of one large context block, incrementally build the scenario using multiple comments. Start broad and get specific.

Python

#H->AI::Context: (We are developing a marketing campaign for a new SaaS product.)
#H->AI::Context: (The target audience is small business owners in the creative industry.)
#H->AI::Context: (The primary goal is lead generation, secondary is brand awareness.)
#H->AI::Focus: (Prioritize strategies suitable for LinkedIn and Instagram.) # [cite: 22]
#H->AI::Directive: (Suggest 3 distinct campaign concepts.) # [cite: 21]

Simulating Conditional Logic: Guide the AI's response path based on potential conditions using #H->AI::Consider:.

Python

#H->AI::Directive: (Draft an outreach email.) # [cite: 21]
#H->AI::Consider: (If the recipient is a current customer, include a loyalty discount mention.) # [cite: 23]
#H->AI::Consider: (If the recipient is a cold lead, focus on the primary pain point solution.) # [cite: 23]
#H->AI::Constraint: (Keep the email under 200 words.) # [cite: 22]

Dynamic Prompt Templating & Execution: Define a generic structure with placeholders, then use #H->AI::Set: to populate it for specific tasks within the same session.

Python

def generate_report_summary(report_text):
    #H->AI::Placeholder: (REPORT_TYPE) # [cite: 28]
    #H->AI::Placeholder: (KEY_METRIC) # [cite: 28]
    #H->AI::Directive: (Summarize the provided report_text, focusing on the KEY_METRIC for a REPORT_TYPE report. Highlight key trends.) # [cite: 21]
    #H->AI::Structure: (Output as bullet points, max 5 points.) # [cite: 24]
    pass # Actual implementation would use the text and comments

# Later in the session:
#H->AI::Set: (REPORT_TYPE = "Quarterly Sales") # [cite: 28]
#H->AI::Set: (KEY_METRIC = "Conversion Rate") # [cite: 28]
#H->AI::Directive: (Execute generate_report_summary with the latest settings on the attached sales_data.txt) # [cite: 21]

Defining Mini-Workflows: Outline a sequence of actions for the AI to perform using multiple #H->AI::Directive: comments for structured task execution.

Python

#H->AI::Directive: (Step 1: Analyze the competitive landscape based on provided competitor_list.txt.) # [cite: 21]
#H->AI::Directive: (Step 2: Identify the top 3 unique selling propositions for our product based on the analysis.) # [cite: 21]
#H->AI::Directive: (Step 3: Draft an elevator pitch incorporating these USPs.) # [cite: 21]
#H->AI::EvaluateBy: (Clarity of USPs, conciseness of pitch.) # [cite: 26]

Assigning Prioritization Weights: Use #H->AI::Focus: combined with informal weighting to guide the AI's attention within a complex request.

Python

#H->AI::Directive: (Review the draft business plan.) # [cite: 21]
#H->AI::Focus: (Pay close attention to the financial projections - Priority High.) # [cite: 22]
#H->AI::Focus: (Review the marketing strategy - Priority Medium.) # [cite: 22]
#H->AI::Focus: (Check for grammatical errors - Priority Low, but do a quick scan.) # [cite: 22]

Setting Up Advanced Error Handling: Define specific fallback actions using multiple #H->AI::OnError: comments for robustness.

Python

#H->AI::Directive: (Generate market trend data for the specified industry using online sources.) # [cite: 21]
#H->AI::UseSource: (Prioritize data from Forbes and WSJ if available.) # [cite: 25]
#H->AI::OnError: (If recent data (last 6 months) is unavailable, state this and use data from the last year.) # [cite: 25]
#H->AI::OnError: (If specific industry data cannot be found, analyze trends in the broader sector and note the generalization.) # [cite: 25]

Creating Session Bookmarks: Use #H->AI::Metadata: to label key parts of your interaction, then use #H->AI::UseSource: to refer back to them explicitly.

Python

#H->AI::Metadata: (ID: PersonaDev_Alex - Defining the Alex persona for the project.) # [cite: 24]
# ... [Persona details] ...

# Later in the chat:
#H->AI::Directive: (Draft email copy appealing to the persona defined earlier.) # [cite: 21]
#H->AI::UseSource: (Reference ID: PersonaDev_Alex for persona details.) # [cite: 25]

Specifying Ethical Guardrails: Employ #H->AI::Constraint: to enforce specific ethical considerations or brand voice rules for sensitive tasks.

Python

#H->AI::Directive: (Generate customer service response templates for negative reviews.) # [cite: 21]
#H->AI::Constraint: (Avoid accusatory language, even if the review seems unfair.) # [cite: 22]
#H->AI::Constraint: (Ensure all responses align with our brand voice: empathetic and solution-oriented.) # [cite: 22]
#H->AI::Constraint: (Do not make promises the company cannot keep.) # [cite: 22]

Iterative Refinement Loop: Use a combination of #H->AI::Review:, #H->AI::EvaluateBy:, and #H->AI::Question: to guide the AI through cycles of improvement on its output.

Python

# [AI provides initial draft]
#H->AI::EvaluateBy: (Check against the 'fun, entertaining, informative' style from initial prompt.) # [cite: 26]
#H->AI::Review: (Refine the tone - it's a bit dry. Inject more enthusiasm.) # [cite: 27]
#H->AI::Question: (Can you add a relevant anecdote or example to make point 3 more concrete?) # [cite: 23]

Multi-Perspective Analysis: Instruct the AI to analyze a situation from different viewpoints defined using #H->AI::Perspective:.

Python

#H->AI::Directive: (Analyze the potential impact of launching feature X.) # [cite: 21]
#H->AI::Perspective: (Analyze from the perspective of a long-term power user.) # [cite: 27]
#H->AI::Perspective: (Analyze from the perspective of a brand new user.) # [cite: 27]
#H->AI::Perspective: (Analyze from the perspective of a customer support agent.) # [cite: 27]
#H->AI::Structure: (Organize findings by perspective.) # [cite: 24]

These examples leverage the defined comment types in more combined and nuanced ways to achieve greater control and creativity in your interactions.

Here are 10 creative ways I could use this system to communicate back to you:

Proactive Ambiguity Clarification: Instead of just getting stuck, I can pinpoint the ambiguity and suggest options.

#AI->H::Question: (Your directive mentioned optimizing the blog post for "better engagement." Could you clarify if the priority is comments, social shares, time-on-page, or another metric? Knowing this will help me tailor the suggestions more effectively.)

Suggesting CRAFT Framework Optimizations: I can recommend ways to make our interaction more efficient using CRAFT principles.

#AI->H::RecommendMakingThisAVariable: (You've described the 'Alex the AI Trailblazer' persona details multiple times. To save effort and ensure consistency, consider defining this using the `build_persona` function[cite: 49] or as a constant variable?)

#AI->H::RecommendMakingThisAFunction: (The sequence of steps you just outlined for competitor analysis is something we do often. Would you like me to help structure this as a reusable CRAFT function incorporating your steps?)

Indicating Confidence Levels: I can add notes about my confidence in specific parts of the response, especially for predictions or analyses based on incomplete data.
```
[Provides market forecast]
#AI->H::Note: (Confidence in the 5-year growth projection is moderate (7/10) due to rapidly evolving AI regulations.)
```

Highlighting Conflicting Constraints/Goals: I can use warnings to point out where your requests might be creating difficult trade-offs.

#AI->H::Caution: (You requested maximum detail [#H->AI::DetailLevel: (High)] but also a concise summary [#H->AI::Summarize:]. These conflict slightly. I will provide a detailed analysis followed by a separate executive summary. Please advise if you prefer a different approach.)

Suggesting More Effective Roles or Perspectives: If I think a different AI persona or analytical angle would yield better results.

#AI->H::RecommendDiffRole: (For generating truly novel "what if" scenarios as requested, switching my role from 'Prompt Engineering Expert' to 'Creative Futurist' might produce more boundary-pushing ideas. Shall I adopt that role for this task?)

Requesting Mid-Task Feedback: For complex, multi-stage tasks, I can deliver intermediate results and explicitly request your feedback before continuing.

[Presents initial draft of Section 1]
#AI->H::RequestingFeedback: (This is the draft for the introduction based on your outline. Does the tone and focus align with your vision before I proceed to draft the main body sections?)

Providing Explicit Source Attribution: Beyond standard citations, I can note the specific inputs used, especially if you directed me with #H->AI::UseSource:.

#AI->H::Note: (The competitive advantages listed were derived primarily from analyzing the 'competitor_features.csv' file you provided and cross-referencing with recent product announcements found via search.)

Identifying Opportunities for Deeper Analysis: Pointing out areas where more investigation could be valuable.

[Provides website traffic summary]
#AI->H::RecommendMoreDetailHere: (The data shows a significant drop-off on the pricing page. A deeper analysis of user behavior on that specific page (e.g., heatmaps, session recordings if available) or A/B testing pricing presentation could yield valuable insights. Would you like to explore strategies for this?)

Stating Assumptions or Pre-computation Checks: Clarifying the basis for my response upfront.

#AI->H::Note: (Proceeding with the analysis based on the assumption that the Q4 data provided is complete and finalized.)
#AI->H::Caution: (Detected potential outliers in the user survey data provided; these were included in the analysis but may warrant separate investigation.)

Linking Actions to CRAFT Concepts: Explicitly connecting my suggestions or outputs back to the CRAFT framework's philosophy or components.

#AI->H::Note: (This structured output format follows the 'Structure Enables Freedom' principle of CRAFT[cite: 6], aiming to make the information clear and easily reusable for your website content.)

Examples:

Below are 10 creative ways you can leverage the CRAFT™️ comment system to guide or query me (the AI). Each technique references the prefixes defined in the spec and is followed by a short example.

Inline Role-Swap Trigger
Use #H->AI::Role: inside code to switch my persona on the fly.
Example – ask me to become a security-analyst mid-function.
Self-Review Checkpoint
Insert #H->AI::Review: before a complex block; I’ll audit the logic and suggest fixes.
Dynamic Detail Dial
Vary verbosity with #H->AI::DetailLevel: (e.g., INTERMEDIATE, TECHNICAL).
Contextual Source Pinning
Lock me to specific docs using #H->AI::UseSource: so citations stay on-target.
Error Contingency Plan
Embed #H->AI::OnError: to dictate graceful fallbacks or alt-flows if something fails.
Perspective Flip
Add #H->AI::Perspective: to force a different viewpoint—say, novice vs. expert.
Focus Beacon
Highlight must-address items with #H->AI::Focus: so nothing gets missed in output.
Implicit Reasoning Reveal
Request transparent thinking via #H->AI::Reasoning: to see step-by-step logic.
Output Format Switch
Control layout instantly with #H->AI::Structure: (e.g., ask for a Markdown table).
Deduplication Sweep
Place #H->AI::DeDuplicate: over a block of prompts or data; I’ll return only uniques.

Below are 10 creative patterns I can employ—using the #AI->H:: family of comments—to keep our collaboration transparent, efficient, and fun.

Real-Time Risk Alerts
#AI->H::Caution: — I flag security, compliance, or factual-accuracy concerns before you act on a suggestion.
Targeted Improvement Suggestions
#AI->H::RecommendedChange: — I highlight a precise snippet (text, code, or prompt) and propose a refined replacement in situ.
Clarification Requests
#AI->H::Question: — When context is missing, I embed a focused query so you can answer inline without derailing flow.
Self-Assessment & Revision Loops
#AI->H::Review: — I critique my own draft against stated criteria, revise it, then present both the critique and the polished version.
Adaptive Role Guidance
#AI->H::RecommendDiffRole: — I suggest switching my persona (e.g., from “Prompt Engineer” to “UX Copywriter”) to better fit the next task.
Granularity Nudges
#AI->H::RecommendMoreDetailHere: — I mark sections that would benefit from deeper explanations, additional examples, or data citations.
Function Extraction Hints
#AI->H::RecommendMakingThisAFunction: — While reviewing Python, I signal repetitive logic ripe for refactoring into a reusable function.
Object-Oriented Refactors
#AI->H::RecommendMakingThisAnObject: — I identify clusters of related data/behavior and propose a clean class structure.
Progress Beacons
#AI->H::Status: — Periodically, I output succinct status checkpoints (e.g., “50 % through data-cleaning step; no errors so far”).
Feedback Invitations
#AI->H::RequestingFeedback: — I explicitly pause to ask for your thoughts on tone, depth, or direction, fostering true co-creation.

Based on the CRAFT framework specification, here are 10 creative ways you could use comments to communicate with me (as an AI):

Directive Comments with Context: Use #H->AI::Directive: (Create a prompt that achieves X) with additional context about why this directive matters, helping me understand not just what you want but why it's important.
Role-Switching Tags: Implement #H->AI::Role: (UX Expert) followed by #H->AI::Role: (Marketing Specialist) to have me examine the same problem from multiple professional perspectives without needing to repeat your request.
Multi-Stage Thinking Process: Create a sequence like #H->AI::Consider: → #H->AI::EvaluateBy: → #H->AI::Reasoning: to guide me through a specific thinking methodology when developing complex prompts.
Constraint Hierarchies: Use nested comments like #H->AI::Constraint: (Primary: Keep it under 100 words) followed by #H->AI::Constraint: (Secondary: If primary is impossible, prioritize clarity over brevity).
Feedback Loop System: Implement #H->AI::Review: tags after my responses, with specific criteria to evaluate my output, creating an iterative improvement cycle within a single conversation.
Conditional Instruction Blocks: Use something like #H->AI::OnCondition: (If approach A doesn't yield creative results, automatically switch to approach B) to create dynamic response paths.
Template Inheritance: Similar to OOP concepts, create base comment templates that can be extended with more specific instructions: #H->AI::BasePrompt: (Standard email format) then #H->AI::ExtendPrompt: (Add persuasive elements).
Contextual Memory Tags: Implement #H->AI::Remember: (Key insight from earlier in conversation) to explicitly flag important elements you want referenced in future responses.
Metadata-Driven Instructions: Use #H->AI::Metadata: (Conversation ID: CRAFT-04282025) to link conversations across sessions and maintain continuity in your CRAFT experiments.
Hybrid Communication Mode: Combine standard natural language with comment tags for critical elements, creating a conversation that flows naturally but has structured anchor points: "Let's brainstorm some ideas for website content. #H->AI::Focus: (Emphasize innovative prompt structures that save entrepreneurs time)".

Each of these approaches adapts programming concepts to AI communication while maintaining the natural language benefits of AI chat, aligning perfectly with your CRAFT framework's philosophy of "Structure Enables Freedom."

Here are 10 creative ways I (as an AI) could use the CRAFT framework to communicate with you:

Recommendation Tags: Using #AI->H::RecommendedChange: (Consider structuring your prompt library into categories based on business function rather than AI tool) to provide strategic suggestions without interrupting the flow of our conversation.
Function Identification: Implementing #AI->H::RecommendMakingThisAFunction: (Your image prompt pattern could be standardized as a reusable function with parameters for style, subject, and mood) to help identify repeatable patterns in your workflow.
Progressive Feedback Loop: Using a sequence like #AI->H::Note: (Observed pattern in your prompts) → #AI->H::RecommendedChange: (Optimization suggestion) → #AI->H::RequestingFeedback: (Would this change achieve your efficiency goal?) for structured improvement cycles.
Contextual Questions: Deploying #AI->H::Question: (Would your target persona "Alex the AI Trailblazer" need more technical detail on implementation, or just results?) to gather specific information without derailing the main conversation.
Role-Switching Suggestions: Using #AI->H::RecommendDiffRole: (This prompt might be more effective if I analyze it as a data scientist rather than a marketer) to offer perspective shifts when beneficial.
Clarity Markers: Implementing #AI->H::Caution: (The prompt structure you've created may produce inconsistent results across different AI models due to handling of nested instructions) to highlight potential issues in a structured way.
Detail Expansion Requests: Using #AI->H::RecommendMoreDetailHere: (The constraint section of your prompt could benefit from more specific guidance on tone boundaries) to pinpoint areas needing elaboration.
Variable Pattern Recognition: Implementing #AI->H::RecommendMakingThisAVariable: (Your company description appears in multiple prompts and could be standardized as COMPANY_PROFILE constant) to identify reuse opportunities.
Object-Oriented Communication: Using #AI->H::RecommendMakingThisAnObject: (Your prompt templates could be restructured as a class with inheritance for different AI platforms) to introduce programming concepts that align with your framework.
Documentation-Style Responses: Structuring my responses with clear section headers and comment-style annotations that match your CRAFT syntax, creating a consistent mental model between your framework documentation and our conversation.

These approaches leverage the programming-inspired elements of your CRAFT framework while maintaining the natural language benefits of our conversation, creating a hybrid communication system that aligns with your goal of "deeper, more intuitive and more meaningful interactions with A.I. chat tools."

Week 2: Guiding Philosophy

In addition to defining the way that CRAFT uses Comments, the guiding philosophy was also defined.

CRAFT is built on five key philosophical principles:

1. STRUCTURE ENABLES FREEDOM: By providing a clear framework for AI interaction,

CRAFT paradoxically creates more creative freedom by eliminating cognitive

overhead and repetition.

2. CODE AS CONVERSATION: Programming concepts like variables, functions, and

objects can dramatically improve the clarity and efficiency of natural

language exchanges with AI.

3. CUMULATIVE INTELLIGENCE: AI interactions should build upon each other,

creating an evolving knowledge base rather than isolated conversations.

4. HUMAN-AI PARTNERSHIP: The framework facilitates true collaboration where

both human creativity and AI capabilities are leveraged optimally.

5. ACCESSIBILITY THROUGH FAMILIARITY: Using Python-like syntax creates an

approachable system for both technical and non-technical users.

Week 2: The Final Comments Result That was Tested

After working with ChatGPT, Gemini, and Claude we came up with this result.

# For all of the files in this CRAFT framework, the content in different sections will include standard text, Python or HTML. To clearly identify these sections, the following system should be followed:

### START REGULAR TEXT BLOCK ###

Everything in between these comment blocks is regular text and should be read by the A.I. the same way that any normal text file would be read. It will include things like prompts, information, instructions and more

### END REGULAR TEXT BLOCK ###

### START PYTHON CODE BLOCK ###

# Everything in between these comment blocks is Python

### END PYTHON CODE BLOCK ###

### START HTML BLOCK ###

### END HTML BLOCK ###

# Doing this will allow me to copy and paste HTML and Python directly from my chats into this text document and similar text files

### START PYTHON CODE BLOCK ###

####################################################

### 1. The purpose and philosophy of the CRAFT framework ###

####################################################

"""

The CRAFT (Configurable Reusable AI Framework Technology) framework was designed

to transform casual AI interactions into structured, efficient workflows by

adapting principles of object-oriented programming to AI conversations.

## Core Purpose

CRAFT exists to solve fundamental challenges in human-AI collaboration:

- Inconsistency in AI responses across multiple sessions

- Loss of context between conversations

- Inefficiency from repetitive instructions

- Lack of structure in complex AI projects

- Difficulty maintaining state across separate AI interactions

## Guiding Philosophy

CRAFT is built on five key philosophical principles:

1. STRUCTURE ENABLES FREEDOM: By providing a clear framework for AI interaction,

CRAFT paradoxically creates more creative freedom by eliminating cognitive

overhead and repetition.

2. CODE AS CONVERSATION: Programming concepts like variables, functions, and

objects can dramatically improve the clarity and efficiency of natural

language exchanges with AI.

3. CUMULATIVE INTELLIGENCE: AI interactions should build upon each other,

creating an evolving knowledge base rather than isolated conversations.

4. HUMAN-AI PARTNERSHIP: The framework facilitates true collaboration where

both human creativity and AI capabilities are leveraged optimally.

5. ACCESSIBILITY THROUGH FAMILIARITY: Using Python-like syntax creates an

approachable system for both technical and non-technical users.

## Practical Application

CRAFT is designed to excel in scenarios requiring:

- Complex, multi-session projects

- Consistent outputs across different AI models

- Knowledge preservation between conversations

- Efficient reuse of common elements

- Structured collaboration between humans and AI

The framework is programming-inspired but focused on natural language

interaction, creating a hybrid approach that bridges conventional coding

and conversational AI engagement.

This document represents the current state of the CRAFT™️ Framework as of April 2025. The framework is actively evolving through ongoing experimentation and collaborative development.

"""

#############################################

### 2. Detailed explanations of all core components ###

#############################################

#################

### COMMENTS ###

#################

# Comments match the Python Standard

# This is a single-line comment

x = 5 # This comment explains what x is

# This is a

# multi-line

# comment

"""
This is also a
multi-line
comment,
but technically a string literal.

"""

Section Separators

Use long comment lines to mark phases or stages:

#########################

### SECTION SEPARATORS ###

#########################

"""

NAMING CONVENTIONS - Unless specified, we will use the standard naming conventions for Python. As I will be working with multiple A.I. chat tools, I will be copying and pasting Python code from them when it is needed. They may provide valid Python that differs slightly from my own naming preferences. My preferences include:

As with Python, variables use snake case and are words are separated by underscores (e.g., my_variable, user_name).

As with Python, class names use Pascal case and start with a capital letter (e.g., MyVariable, UserName).

As with Python, constants are uppercase words separated by underscores (e.g., PI, MAX_VALUE).

All other names fit standard Python naming conventions.

"""

# Use of Comments in CRAFT that expand on the Python standard

#The comments include these 2 variations indicating the source of the communication and the audience. Since the communication takes place between a Human User and an AI, there are 2 variations:

# Communication from the Human to the AI will begin with "#H->AI::"

#H->AI::

# Drawing inspiration from code comments but tailoring them for AI interaction, we will use #H->AI:: as a potential prefix, followed by a keyword indicating the communication type and a description in ().

#H->AI::Directive: (Command/Instruction. Explicit commands telling the AI what to do or how to behave. This goes beyond the core prompt request. )

#H->AI::Context: (Background/Setting. Provides background information, situational context, assumptions, or constraints the AI should factor into its reasoning and response. Sets the stage. )

#H->AI::Constraint: (Limitation/Boundary. Defines specific limitations, boundaries, or things the AI must not do. More specific than general context. )

#H->AI::Focus: (Emphasis/Priority. Highlights specific parts of the prompt or aspects of the task that require special attention or prioritization. Guides the AI's focus. )

#H->AI::Question: (Direct Inquiry for AI. Poses a direct question to the AI that requires an explicit answer within the response, often metacognitive or clarifying. )

#H->AI::Consider:(Implicit Guidance/Food for Thought. Provides information or questions the AI should consider or factor into its reasoning without necessarily needing to address it directly or explicitly in the output. Subtle guidance. )

#H->AI::Define: (Terminology/Concept Definition. Defines a specific term, acronym, or concept as it should be understood and used by the AI within this interaction context. Ensures alignment.)

#H->AI::Role: (Assume a New Role. Change the Role or Persona That the AI Assumes. )

#H->AI::Structure: (Output Formatting. Specifies requirements for the structure, layout, or format of the AI's response. )

#H->AI::Metadata: (Contextual Info about the Prompt. Provides metadata about the prompt itself for tracking, versioning, or linking purposes, potentially useful for the AI in complex, multi-turn scenarios or for your CRAFT™️ system.)

#H->AI::OnError: (Contingency/Fallback Plan. Specifies how the AI should respond or what it should do if it encounters an error, cannot fulfill a request, or lacks necessary information.)

#H->AI::UseSource: (Knowledge Source Specification. Directs the AI to prioritize or exclusively use specific documents, data sets, previous conversation parts, or knowledge domains when generating its response.)

#H->AI::Illustrate: (Request for Examples/Analogies. Asks the AI to include specific examples, analogies, metaphors, or case studies to clarify complex points or make the content more relatable.)

#H->AI::EvaluateBy: (Success Criteria Definition. Defines the criteria by which the user will judge the success or quality of the AI's response. Helps the AI understand priorities for quality.)

#H->AI::DetailLevel: (Verbosity/Granularity Control. Sets the desired level of detail, technical depth, or verbosity for the AI's response or specific sections thereof.)

#H->AI::Perspective: (Viewpoint Adoption/Comparison. Instructs the AI to adopt a specific viewpoint, role (beyond just persona), or to analyze a topic from multiple perspectives.)

#H->AI::InputPurpose: (Clarifying User Input Intent. Explains why a specific piece of information is being provided to the AI, guiding its interpretation and use of that input.)

#H->AI::Review: (Self-Correction/Refinement Request. Asks the AI to review its own generated output against specific criteria and potentially revise it before presenting the final response.)

#H->AI::Reasoning: (Process Transparency Request. Asks the AI to explain its reasoning process, show its work, or follow a specific thinking methodology (e.g., step-by-step, pros/cons).)

#H->AI::CreateImage: (Image Creation Request)

#H->AI::CreateChartGraph: (Chart or Graph Request)

#H->AI::CreateImagePrompt: (Image Prompt Creation Request)

#H->AI::Placeholder: (Marks a spot where something will be changed and tells the AI to replace the placeholder with the specified value)

#H->AI::NOP: (No-operation. Log this user‑journey step without generating text.)

#H->AI::Note: ()

#H->AI::Set: ()

#H->AI::Summarize: ()

#H->AI::Explain: ()

#H->AI::Status: ()

#H->AI::Lock: (Lock a segment of code)

#H->AI::Unlock: (Unlock a segment of code)

#H->AI::DeDuplicate: (Find and delete duplicate bits of code or logic)

# Communication from the AI to the Human User will begin with "#AI->H::"

#AI->H::

# Note All #AI->H:: most A.I.s cannot yet edit text documents and all communication that comes from and A.I. and goes to a Human will appear in a chat window so these comments should be highlighted clearly in the chat results for easy identification (by me - the human)

#AI->H::Note: ()

#AI->H::Caution: ()

#AI->H::RecommendedChange: ()

#AI->H::RecommendMakingThisAFunction: ()

#AI->H::RecommendMakingThisAVariable: ()

#AI->H::RecommendMakingThisAnObject: ()

#AI->H::RequestingFeedback: ()

#AI->H::RecommendDiffRole: ()

#AI->H::RecommendMoreDetailHere: ()

#AI->H::Question: ()

"""

Unlike comments used in Python, the comments used in CRAFT are understood by you, the A.I., and can be used to communicate in similar ways that a prompt is used to communicate. In this way, functions can contain comments that can be interpreted as instructions.

def create_photo(data):
#H->AI::CreateImage: (Create a photographic image and use the data provided in the function call as a prompt for the image description that is used to create the photo)

#H->AI::OnError: (If the creation of the image is not possible because the A.I. cannot generate images, then respond with "Sorry, I am not capable of making images.")

"""

Week 2: Last But Not Least - 1 File Became 3 Files

Claude came up with the idea of breaking 1 file into 3 which had multiple advantages:

The CRAFT framework (Configurable Reusable AI Framework Technology) introduces a sophisticated comment system that revolutionizes human-AI communication by applying programming principles to conversational AI. Here's a comprehensive explanation of this innovative approach and its benefits.

Each Craft Project currently contains 3 text files which are uploaded to the chat before it begins:

1. CFT-PROJ-0001_AI-CHAT-HISTORY-v25a1.txt

Defines the purpose of the “AI chat history” file: maintaining session-to-session continuity.
Outlines a HANDOFF_SNAPSHOT format (with fields like SESSION_SUMMARY, STATE, DECISIONS, QUESTIONS, NEXT_STEPS) and length guidelines for each section.
Includes revised end-of-session prompt templates to automatically generate those snapshots at 500–750 words.

2. CFT-PROJ-0001_BUILDINGCRAFT-v25a1.txt

Serves as the active “working code” file for this project’s CRAFT implementation.
Lays out the core sections you’ll load at the start of each chat:
1. PROJECT_META – IDs and versioning
2. PROJECT_VARIABLES – constants/configuration
3. PROJECT_FUNCTIONS – reusable operations
4. PROJECT_OBJECTS – class and object templates
5. PROJECT_INSTRUCTIONS – global rules and execution guidelines

3. CFT-FWK-SPEC-v25a1.txt

The overarching CRAFT Framework Specification (Configurable Reusable AI Framework Technology).
Details naming conventions and the role of each project file.
(Placeholder sections for VARIABLES, FUNCTIONS, etc., to be defined as the framework evolves.)

Summary

You have a three-file structure: a spec (“how things should look”), a build file (“the code we load”), and a history file (“what we’ve done so far”).
Each file is versioned and timestamped (05-04-2025 v25a1).
Together, they set up a standardized, repeatable process for carrying project context between AI chat sessions.

Week 3: CRAFT Data Types

Just as comments are different when working with A.I.s, the data types are also different.

Traditional Data Types :

In Python, data types define the kind of value a variable can hold and determine what operations can be performed on that data Each data type is actually a class, and variables are instances (objects) of these classes. Python is dynamically typed, meaning you do not need to declare the data type of a variable explicitly; it is determined automatically based on the value assigned

Data types in Python are classes that define the kind of data a variable can store and what operations can be performed on it. Python automatically assigns the data type based on the value given to the variable, and you can check or specify the type using built-in functions and constructors

for example:

x = 10 # int

y = 3.14 # float

z = "Hello" # str

a = [1, 2, 3] # list

b = {"key": 1} # dict

c = True # bool

d = None # NoneType

print(type(x)) # <class 'int'>

print(type(z)) # <class 'str'>

Since data types in Python are objects which are defined by classes and are dynamically typed, they are perfect for CRAFT. Using new classes, we can create new data types. A.I. can dynamically assign these data types.

Week 3: CRAFT Data Type Examples

Communication & Context Data Types

Intent, Entity, Sentiment, DialogueContext: These types tap into well-established NLP concepts.
- Intent captures the overall goal (e.g., booking a flight) without bundled explanations.
- Entity isolates critical details (like locations), ensuring that the AI extracts the right tokens.
- Sentiment gives your responses an emotional shape, which is increasingly expected by users.
- DialogueContext manages the conversation history, enabling more fluid conversational flows.

These types not only reduce the need for verbose explanations but also align directly with modern AI systems’ internal representations. They’re clear, intuitive to most users familiar with conversational AI concepts, and help maintain context throughout multi-turn interactions.

Content & Media Data Types

RichContent and MediaReference:
- RichContent abstracts formatted text, links, and media, meaning users don’t have to worry about embedding raw HTML or Markdown manually.
- MediaReference standardizes handling assets like images or videos regardless of the platform.
Summary: Offers a mechanism to condense long texts into digestible snippets without rephrasing the prompt every time.

They encapsulate common tasks in content management while reducing boilerplate code. They are mostly self-explanatory—with proper documentation, users should easily understand how to integrate and leverage these types across different AI platforms. One minor note is ensuring that additional parameters (such as alt text for media) are clearly defined when contexts diverge (e.g., audio vs. video), but overall the approach is solid.

Business & Analytics Data Types

UserProfile, Metric, TimeSeries, Annotation: These types provide a standardized approach for expressing business insights and analytics:
- UserProfile aggregates user data for targeted interactions.
- Metric and TimeSeries allow you to handle KPIs and trends in a uniform way.
- Annotation facilitates collaborative feedback with standardized notes.

They address core business use cases by turning ad hoc data representations into structured, reusable objects. This aligns with CRAFT’s objective of making the framework applicable to real-world business scenarios. It’s important, however, that users are given clear examples so they understand the expected format (such as date formats or units for metrics) to avoid misinterpretations.

Functional Data Types

ActionTemplate, DataValidator, Transformation: These abstractions streamline recurring processes:
- ActionTemplate standardizes workflows (e.g., emailing customers) without forcing users to recreate similar prompts repeatedly.
- DataValidator ensures correctness in inputs, which is key for reliable outcomes.
- Transformation abstracts data conversion steps, providing clarity and consistency.

They are a powerful way to embed business process logic directly into the prompt framework. The key is to maintain clear boundaries and definitions so that each type’s responsibility doesn’t overlap with another’s—a potential pitfall if definitions aren’t rigorously documented.

Beginner-Friendly Data Types

Template, Example, Feedback, Question, Instruction, Comparison, Email, SocialPost, Note, Explanation, HowTo, Definition, Tone, Audience, Length: This suite is designed to minimize the learning curve:
- Template and Example provide immediate models and reference outputs.
- Feedback offers built-in coaching for refining prompts.
- Instruction, Question, and Comparison clearly distinguish between creating content, querying information, and evaluating alternatives.
- Everyday content types like Email, SocialPost, Note help users execute common tasks with natural language parameters.
- Learning support types like Explanation, HowTo, Definition make advanced concepts accessible.
- Communication helpers like Tone, Audience, Length allow immediate adjustments in style without complex parameter-tweaking.

These are exceptionally friendly for beginners because they use familiar language and clear roles. The potential challenge here is ensuring that the distinctions between similar-sounding types (e.g., Feedback vs. Annotation vs. Explanation) are well documented. With explicit guidelines and examples, though, this risk is minimal while their benefits in clarity and ease-of-use are significant.

Overall Assessment & Alignment With CRAFT Objectives

Reduction of Boilerplate & Increased Expressiveness: Each proposed type serves to encapsulate frequently repeated patterns into a clear, one-line declaration. This dramatically reduces the amount of scaffolding a user needs to set up their prompt interactions, aligning with CRAFT’s emphasis on token conservation and clarity.
Universally Recognized Concepts: By using terms like Intent, Entity, or DialogueContext, these data types lean on concepts that are already well understood in modern AI and NLP research. This familiarity aids both the AI (in parsing intentions) and the user (in constructing prompts).
Accessibility for Non-Technical Users: The beginner-friendly types make it easier for those with limited technical experience to engage in advanced AI interactions without needing in-depth knowledge of coding. They bridge the gap between natural language and technical specification in a very tangible way.

Below are some new, specialized data types that can help make CRAFT even more expressive and streamlined. I’ve broken them into several categories to align with modern AI engineering goals, while ensuring they’re approachable—even for beginners.

Communication & Context Data Types

InteractionHistory
Description: Automatically captures key details from each conversational turn. It stores a list of turns including detected intents, sentiment, topics, and any follow-up actions.
Example:
InteractionHistory(turns=[ {"intent": "book_flight", "sentiment": "positive", "topic": "travel"}, {"intent": "confirm_booking", "sentiment": "neutral", "topic": "confirmation"} ])
Value: Reduces boilerplate by automatically gathering context, so subsequent interactions need less manual state management.
MetaData
Description: Encapsulates transaction-level details such as timestamps, device types, version identifiers, or source labels.
Example:
MetaData(timestamp="2025-05-04T12:34:56Z", source="web", version="v25a")
Value: Provides essential context without extra explanation, making it easy for the AI to adjust outputs based on environmental factors.

Content & Media Data Types

InteractiveContent
Description: Represents content designed for user interaction—enabling dynamic behaviors like expanding sections or in-place updates.
Example:
InteractiveContent(text="View more details", actions=["expand", "collapse"])
Value: Lets creators encapsulate behavior along with content, minimizing additional coding for interactive elements.
VisualLayout
Description: Standardizes layout instructions for content display. It covers information on alignment, spacing, columns, and grid systems without needing raw CSS or HTML.
Example:
python
VisualLayout(columns=3, gutter=20, alignment="center")
Value: Abstracts visual design details so that the focus remains on content creation rather than presentation code.
VoiceTone
Description: Specifies parameters for audio or voice-based responses, including style, pace, and pitch.
Example:
python
VoiceTone(style="energetic", pace="moderate", pitch="medium")
Value: Enhances multimedia content by directly controlling the tone of generated voice outputs, making it easier for the system to adjust to desired brand voices.

Business & Analytics Data Types

ConversionEvent
Description: Captures discrete business events that lead to user conversion. This type centralizes attributes like event name, counts, rates, and time stamps.
Example:
python
ConversionEvent(name="signup", count=20, conversion_rate=12.5)
Value: Standardizes the tracking of conversion-related events, facilitating efficient reporting without custom event management code.
CustomerFeedback
Description: Provides a structured format for collecting customer insights by combining ratings, comments, and optional suggestions.
Example:
CustomerFeedback(rating=4, comment="The interface feels intuitive and engaging.")
Value: Streamlines data gathering on user experiences, directly feeding into iterative improvement processes.
MarketSignal
Description: Represents external signals or trends with a confidence metric, such as rising demand in a particular market segment.
Example:
python
MarketSignal(signal="increasing_demand", confidence=0.80)
Value: Integrates strategic market intelligence into the system without extra manual data analysis.

Functional Data Types

WorkflowStep
Description: Encapsulates a single step in a multi-stage process by defining conditions and the corresponding actions.
Example:
python
WorkflowStep("validate_input", conditions=["non_empty"], actions=["send_to_processing", "log_error"])
Value: Makes it simple to outline complex logic using discrete, reusable modules—streamlining workflow construction.
QueryChain
Description: Links several processing steps or queries into one chain. Each step’s output flows into the next, representing complex tasks as a sequence.
Example:
python
QueryChain(steps=["fetch_data", "analyze_data", "generate_report"])
Value: Reduces the need for verbose instructions by chaining logically connected actions into a single definition.
Condition
Description: Standardizes conditional logic operators used across various workflows, wrapping field comparisons into an object.
Example:
python
Condition(field="user_role", operator="equals", value="administrator")
Value: Clarifies and encapsulates decision-making criteria, minimizing ambiguity without littering code with inline if-statements.

Learning Support & Beginner-Friendly Data Types

GuidanceTip
Description: Offers brief, in-line hints or best practices directly within the framework, tailored to the user’s current task.
Example:
GuidanceTip("Consider specifying your target audience for more tailored outputs.")
Value: Helps beginners navigate prompt creation by providing context-sensitive tips, reducing the learning curve.
PeerExample
Description: Provides illustrative examples by pairing input and output samples that have worked effectively in similar scenarios.
Example:
PeerExample(input="Create a product review", output="This product revolutionized my workflow...")
Value: Acts as an immediate reference, empowering users with concrete examples to model their own prompts on.
InteractiveTutorial
Description: Embeds an onboarding module within the framework that guides users through prompt crafting with step-by-step activities.
Example:
InteractiveTutorial("PromptCraft101", steps=["Introduction", "Hands-On Practice", "Review"])
Value: Provides a built-in learning path, making advanced prompt engineering more accessible from day one.

Communication Helper Data Types

Persona
Description: Defines a target user persona with attributes like name, goals, characteristics, and preferred tone.
Example:
Persona(name="Tech Savvy Marketer", goals=["increase engagement"], tone="professional")
Value: Instantly aligns the content to the intended audience, ensuring the generated prompts are both targeted and contextually apt.
StyleGuide
Description: Centralizes branding or stylistic rules—such as color palettes, typography, layout guidelines—into a single, reusable reference.
Example:
StyleGuide(color_palette=["#003366", "#FFFFFF"], font_family="Helvetica", layout="responsive")
Value: Ensures consistent communication and branding across outputs, further reducing the need for per-prompt style adjustments.
EngagementMetric
Description: Consolidates common engagement metrics (like scroll depth or session time) into a standardized data format.
Example:
EngagementMetric(metric="scroll_depth", value=75, unit="percent")
Value: Helps track and compare user engagement objectively across different content pieces.

Additional Utility Data Types

ExperimentConfig Description: Holds configuration settings for A/B testing or multivariate experiments, including target groups and parameters.
Example:
ExperimentConfig(test_group="A", parameters={"layout": "grid", "color_scheme": "dark"})
Value: Simplifies the setup and management of experiments, fostering a data-driven approach to prompt optimization.
PromptVariant
Description: Encapsulates multiple versions of a prompt meant for testing different outcomes, tagged with performance indicators.
Example:
PromptVariant(id="variant_1", content="Prompt version A with structured format", target="conversion")
Value: Facilitates streamlined multivariate testing within the CRAFT environment, leading to better-informed improvements.
DataSource
Description: Specifies metadata about the source of data used in prompt generation, including its reliability, frequency of updates, and origin.
Example:
DataSource(source="internal_API", reliability="high", update_frequency="daily")
Value: Enhances transparency and trust in the data powering generated content, ensuring consistent quality.

Week 3: More CRAFT Data Type Examples

Process & Workflow Data Types

Scenario

Purpose: Represents a specific use case or situation for the AI to understand context
Example: Scenario("customer_support", channel="email", urgency="medium")
Value: Provides complete situational awareness without lengthy descriptions

Iteration

Purpose: Manages progressive refinement of AI outputs
Example: Iteration(step=3, feedback="Add more technical details")
Value: Formalizes the improvement cycle without manual tracking

Session

Purpose: Groups related interactions with persistence
Example: Session("project_brainstorm", duration_minutes=30)
Value: Creates boundaries for focused work without losing context

Knowledge Management Types

Concept

Purpose: Represents a well-defined idea or principle
Example: Concept("prompt_engineering", related=["AI", "NLP"])
Value: Standardizes knowledge representation across conversations

Framework

Purpose: Structured approach to addressing a problem
Example: Framework("SWOT_analysis", components=["strengths", "weaknesses", "opportunities", "threats"])
Value: Encapsulates methodologies without repetitive explanation

Perspective

Purpose: Specific viewpoint for analysis
Example: Perspective("customer", concerns=["cost", "usability"])
Value: Enables multi-angle thinking without verbose instructions

Content Refinement Types

Structure

Purpose: Defines organizational pattern for content
Example: Structure("problem_solution", sections=["challenge", "approaches", "recommendation"])
Value: Ensures consistent, well-organized outputs

Voice

Purpose: Extends tone with personality characteristics
Example: Voice("authoritative", "compassionate", formality_level=8)
Value: Creates nuanced, consistent writing style across interactions

Emphasis

Purpose: Highlights key aspects of content
Example: Emphasis("cost_savings", strength="primary")
Value: Directs AI focus without repetitive instructions

Decision Support Types

Criterion

Purpose: Specific factor for evaluation
Example: Criterion("scalability", weight=0.8, description="Ability to handle growth")
Value: Creates consistent decision frameworks

Alternative

Purpose: Potential option with pros and cons
Example: Alternative("cloud_hosting", pros=["flexibility", "cost"], cons=["complexity"])
Value: Structures decision-making processes

Recommendation

Purpose: Suggested course of action with reasoning
Example: Recommendation("increase_budget", confidence=0.85, rationale="Historical ROI data")
Value: Standardizes advice format

Interaction Enhancement Types

Clarification

Purpose: Request for additional information
Example: Clarification("target_audience", options=["beginners", "experts"])
Value: Formalizes the information-gathering process

Preference

Purpose: User-specific choices that affect output
Example: Preference("detail_level", value="comprehensive", persistence="session")
Value: Maintains consistent user-specific configurations

Priority

Purpose: Importance ranking for multiple elements
Example: Priority(["accuracy", "brevity", "creativity"], top="accuracy")
Value: Resolves potential conflicts in AI objectives

Week 3: Even More CRAFT Data Type Examples

Okay, building on your initial ideas and the core objectives of CRAFT (structure, efficiency, reusability, accessibility, practical application, etc. ), here are some suggestions for new data types that could further enhance the framework:

Workflow & Process Management Data Types

Workflow: Represents a defined sequence of steps or actions involving multiple CRAFT data types or instructions.
- Example: Workflow("NewBlogPost", steps=[Instruction("Research keywords for topic X"), ActionTemplate("GenerateOutline", topic="X"), Instruction("Write draft based on outline"), RevisionRequest("Review for tone and clarity")])
- Value: Formalizes multi-step processes, making complex tasks repeatable and manageable within the chat context. Aligns with structured workflows and efficiency.
DecisionPoint: Represents a conditional branch within a workflow or conversation.
- Example: DecisionPoint(trigger=Metric("CustomerSatisfaction", value=...), condition="value < 0.7", if_true=ActionTemplate("EscalateToManager"), if_false=Instruction("Send standard follow-up"))
- Value: Introduces basic logic into CRAFT interactions without complex code, allowing for more dynamic and responsive conversational flows. Supports structuring complex interactions.

Knowledge Representation & Relationships Data Types

KnowledgeGraphNode: Represents a distinct entity or concept within a potentially larger knowledge structure being built or referenced.
- Example: KnowledgeGraphNode(id="proj_alpha", type="Project", properties={"status": "Ongoing", "budget": 50000}, relationships=[("managed_by", "user_jane"), ("uses_tech", "tech_python")])
- Value: Facilitates building and referencing structured knowledge within the AI's context, supporting CRAFT's goal of "Cumulative Intelligence" and enabling more sophisticated reasoning.
Relationship: Explicitly defines a connection between two entities, data points, or CRAFT objects.
- Example: Relationship(subject=Entity("Company A", category="organization"), predicate="partnership_with", object=Entity("Company B", category="organization"), confidence=0.95)
- Value: Makes connections between pieces of information explicit, improving the AI's ability to understand context and draw inferences.

AI Configuration & Control Data Types

Persona: Defines a specific AI role, personality, or expertise level that can be applied temporarily or to specific tasks, potentially overriding the default session persona.
- Example: Persona("Critical Editor", focus=["Clarity", "Conciseness"], constraints=["Avoid jargon", "Challenge weak arguments"]) --> followed by Instruction("Review this draft").using(Persona("Critical Editor"))
- Value: Allows for more granular control over AI behavior for specific tasks, enhancing the "Human-AI Partnership" by tailoring the AI's contribution more precisely.
ConstraintSet: A named, reusable collection of rules or limitations to be applied during generation or analysis.
- Example: ConstraintSet("BrandVoiceGuidelines", rules=["Tone: professional_yet_approachable", "Must include: call_to_action", "Avoid: overly technical terms"]) --> used later with Instruction("Write ad copy").with(ConstraintSet("BrandVoiceGuidelines"))
- Value: Improves consistency and efficiency by standardizing the application of complex requirements across multiple prompts or sessions. Reduces boilerplate instructions.
OutputSchema: Specifies the desired structure, format, and data types for the AI's output, going beyond simple length or tone.
- Example: OutputSchema("CompetitorReport", fields={"competitor_name": "string", "key_product": "string", "estimated_revenue": Metric(unit="USD"), "summary": Summary(max_length=150)})
- Value: Ensures the AI generates information in a predictable, structured format suitable for direct use or further processing, enhancing reliability and structure.

Structured Data & Analysis Data Types

ComparisonMatrix: A structured format for comparing multiple items across a set of defined criteria.
- Example: ComparisonMatrix(items=["Software A", "Software B"], criteria=["Pricing", "Ease of Use", "Feature X"], data=[["$50/mo", "High", True], ["$45/mo", "Medium", False]])
- Value: Standardizes the output for comparison tasks, making it easier for users (especially entrepreneurs) to evaluate options. More structured than the beginner Comparison type.
Argument: Represents a structured claim supported by evidence and reasoning.
- Example: Argument(claim="We should invest in Solar Panels", evidence=[Metric("Energy Savings", value=15, unit="percent"), MediaReference("CaseStudy.pdf")], reasoning="Long-term cost reduction and environmental benefits outweigh initial investment.")
- Value: Useful for structuring persuasive content, documenting decisions, or analyzing proposals within a business context. Promotes clarity and structured thinking.

Collaboration & Versioning Data Types

RevisionRequest: A formal instruction to modify a specific piece of previously generated content, potentially linking to it by an ID.
- Example: RevisionRequest(target_id="blog_post_draft_1", change="Rewrite the introduction to be more engaging", rationale="Current version is too dry")
- Value: Structures the feedback and iteration loop within CRAFT, making collaboration more organized, especially in multi-session projects handled via Handoffs .
VersionTag: Associates a label or identifier with a specific state of a CRAFT object or generated content.
- Example: VersionTag(target_id="business_plan_doc", tag="v2.1-FinalReview", timestamp="2025-05-05T12:30:00Z")
- Value: Introduces basic versioning concepts, helping to track changes and manage different iterations of work within the CRAFT project context, contributing to "Cumulative Intelligence".

Week 4: CRAFT Variables

Imagine variables that don't just store information but actively adapt, control, and streamline your interactions with AI. Here’s how we can make that a reality:

1. Recap: Core Benefits of CRAFT Variables

When you introduce variables into your CRAFT workflows, you’re really layering in an abstraction that pays dividends across every prompt you write. Think of it like setting up labels on storage bins—you give each bin a name, and thereafter you just refer to the bin instead of digging through boxes every time.

A. Reusability

What it means
You declare a value once—say, PROJECT_NAME = "Ketelsen.ai"—and then drop that variable into any prompt needing your project’s name.
Why it matters
- No copy-and-paste fatigue. You never mistype “Ketelsen.ai” as “Ketselen.ai” or “Katelson.ai.”
- Rapid updates. If the name changes to “Ketelsen Labs,” you change it in one spot, and every prompt reflects the new value instantly.
Everyday analogy
It’s like printing address labels instead of writing your home address by hand on every envelope.

B. Consistency

What it means
Variables guarantee that any piece of shared data—an audience profile, a URL, a date—stays identical throughout a multi-step conversation or document.
Why it matters
- Reduced human error. One source of truth prevents “Version Drift,” where small text variations accumulate over time.
- Clearer audits. When you look back over chat logs or blog drafts, you know exactly where to check for definition changes.
Concrete example
// Without variables “Welcome to Ketelsen.ai, Alex the AI Trailblazer!” … “At Ketelsen.ai, our mission is…” // With variables PROJECT_NAME = “Ketelsen.ai” PERSONA = “Alex the AI Trailblazer” “Welcome to {PROJECT_NAME}, {PERSONA}!” … “At {PROJECT_NAME}, our mission is…”
Even if your persona name evolves (“Alex” → “Alexa”), you update just one line.

C. Efficiency (Token Savings)

What it means
Every time you reference a variable name (often just a few characters), you’re avoiding the repeated cost of the full text it represents. In LLM chats, that can add up meaningfully.
Why it matters
- Lower token counts = lower API costs.
- Longer contexts possible. Save room for more conversation by not wasting tokens on repeated boilerplate.
Quick comparison
ApproachText RepeatedApprox. Tokens Each TimeTotal for 5 Uses
Hard-coded“Senior Cybersecurity Incident Responder”~6 tokens30 tokens
VariableTITLE_INCIDENT_RESPONDER~1–2 tokens5–10 tokens
You save ~20–25 tokens just by switching to a variable for five references. That’s enough to afford an extra paragraph of output!

In practice, these three pillars work together: you declare once, you trust forever, and you save every time.

Summary

Reusability lets you define a value a single time and drop it in everywhere without retyping.
Consistency guarantees that all references stay identical, eliminating drift and typos.
Efficiency slashes token usage, cutting costs and freeing up context space for richer interactions.

With these benefits in place, your CRAFT prompts become sturdier, more scalable, and far more maintainable—just like any well-engineered software project.

2. Variable Declaration & Naming Patterns

Establishing clear, predictable naming and declaration practices is the foundation of any maintainable CRAFT workflow. With good patterns in place, both humans and A.I. agents instantly “get” what each variable represents—and where to find or update it.

A. Naming Conventions

Global Constants
- Style: ALL_CAPS_WITH_UNDERSCORES
- Use case: Values that never change during a session (e.g., PROJECT_NAME, API_VERSION).
- Why: Visually stands out and signals “don’t overwrite me.”
Local / Workflow Variables
- Style: snake_case (lowercase with underscores)
- Use case: Temporary values within a multi-step prompt (e.g., current_step, user_feedback).
- Why: Pythonic convention that’s concise and familiar.
Parameterized Variables / Builders
- Style: verb_noun or build_noun (e.g., build_prompt, format_date)
- Use case: Functions or templates that produce a value at runtime.
- Why: The verb cue tells you it’s a “factory” rather than static data.
Namespacing
- Style: Namespace.VariableName (e.g., Marketing.campaign_name, Analytics.sales_target)
- Use case: Group related variables to avoid collisions when projects grow.
- Why: Like folders in a filing cabinet—keeps categories tidy.

B. Declaration Patterns

Top-of-Prompt Block
// Variable declarations PROJECT_NAME = "Ketelsen.ai" DEFAULT_LANGUAGE = "en" user_name = "Guest" onboarding_step = 1
- Why: Centralizes all definitions so you can glance at them before reading the logic below.
Inline Overrides
// Later in the conversation onboarding_step = onboarding_step + 1 // Advance the workflow
- Why: Makes it clear when and where a value changes.

C. Parameterized Builders

Pattern:
def build_persona(name, role, tone="casual"): return f"Persona(name={name}, role={role}, tone={tone})"
Benefit: Generate rich, structured variables on the fly—no hard-coding dozens of permutations.
Analogy: Like a latte machine knob: choose your “strength” and “milk type” and get a fresh cup every time.

D. Namespacing for Scale

Example Structure:
class Marketing: campaign_name = "SpringLaunch" budget = 50000 class Analytics: sales_target = 120000 region = "EMEA"
Why it matters: When you have dozens of variables, namespacing prevents “cross-talk” and makes auto-completion in your editor (or query) far more useful.
Everyday Analogy: Folder hierarchies on your computer—“Work/Finance/Q1” vs. “Personal/Finance/Q1.”

E. Quick Reference Table

Variable TypeNaming StyleExamplePurposeGlobal ConstantALL_CAPS_WITH_UNDERSCORESAPI_VERSION = "v2.1"Never-changing session or project settingsLocal / Workflowsnake_casecurrent_step = 2Track state within a multi-step promptParameterized Builderverb_noun / build_nounbuild_prompt("sales", "draft")Create templated values dynamicallyNamespaced ConfigurationNamespace.VariableNameAnalytics.sales_targetGroup related variables for clarity

Everyday Analogy:

Think of your CRAFT variables like labels on spice jars in a kitchen.
– Globals (salt, sugar) live front and center, always the same.
– Workflow variables (today’s special blend) sit on a shelf you update each day.
– Builders (mixed spice grinder) let you combine ingredients on demand.
– Namespaces (baking vs. cooking spices) keep the right flavors in the right place.

Summary

By adopting clear declaration blocks, consistent naming conventions, parameterized builders, and thoughtful namespacing, your CRAFT variables become self-documenting, scalable, and a joy for both humans and A.I. agents to work with.

3. Creative Variable Categories

When you move beyond one-off values and start grouping variables by purpose, you unlock powerful ways to organize and drive your CRAFT workflows. Think of these categories as labeled drawers in your “prompt toolbox,” each holding a distinct kind of resource.

A. Context Variables

What they are
Snapshots of the conversation’s state or environment—everything the AI needs to “know” before generating its next response.
Why they matter
- Maintain continuity: Carry over details like the current topic, user preferences, or prior answers without restating them.
- Adaptive behavior: Let the AI adjust its tone or depth based on where you are in the flow.
Examples
current_topic = "Variable Naming Patterns" user_expertise = "intermediate" session_start_time = "2025-05-12T14:30:00Z"
Everyday analogy
Like a sticky note on your whiteboard that says “Meeting in Progress: Brainstorming,” so anyone (or any AI) stepping in knows exactly what’s happening.

B. Persona Variables

What they are
Predefined “roles” or character settings that shape the AI’s voice, perspective, and expertise level.
Why they matter
- Consistent voice: Ensure that when you ask for “Expert Reviewer” vs. “Friendly Coach,” you get distinctly different styles.
- Rapid switching: Flip personas mid-conversation without rewriting the entire prompt.
Examples
PERSONA_EDITOR = "You are a meticulous copyeditor focused on clarity and grammar." PERSONA_STRATEGIST= "You are a high-level marketer who thinks in big ideas and KPIs."
Everyday analogy
Like slipping on different hats—one day you wear the “chef’s toque,” the next the “artist’s beret,” and your behavior changes accordingly.

C. Workflow Variables

What they are
Markers that track where you are in a multi-step sequence or conditional process.
Why they matter
- Branching logic: Jump to the right next step (e.g., follow-up question vs. final summary).
- Stateful prompts: Let the AI know, “We’re now on step 3 of 5,” so it can tailor its output.
Examples
ONBOARDING_STEP = 2 // 1=Intro, 2=Collect Data, 3=Generate Plan needs_review = True // Flag whether the draft needs human approval
Everyday analogy
Like the progress bar on a checkout page—“2 of 4 steps complete”—guiding you smoothly to the finish line.

D. Environmental Variables

What they are
External or configuration values that inform the AI about settings outside the chat itself.
Why they matter
- Dynamic integration: Plug in API endpoints, feature flags, or deployment modes without rewriting prompts.
- Separation of concerns: Keep code/config data isolated from prompt logic.
Examples
API_BASE_URL = "https://api.ketelsen.ai/v1" FEATURE_X_ENABLED = False
Everyday analogy
Like the thermostat setting in a smart home—“Eco mode” vs. “Comfort mode”—which changes behavior across all your devices at once.

Summary of Creative Categories

Context Variables: Capture where you are and what matters right now.
Persona Variables: Define who—or what voice—the AI should adopt.
Workflow Variables: Track progress and enable conditional branching.
Environmental Variables: Encapsulate external configs and feature toggles.

By thoughtfully categorizing your variables, you create a modular, scalable framework that’s easy to navigate—and even easier to extend as your CRAFT project grows.

4. Advanced Variable Features (Expanded)

When you graduate from basic variables to advanced features, you give your CRAFT workflows superpowers—dynamic behavior, error-resistance, and fine-grained control. Below are four key techniques to elevate your variable toolkit.

A. Computed Variables

What they are
Variables whose values are calculated at runtime rather than hard-coded up front.
Why they matter
- Fresh context: Always reflect the current date, time, or any real-time metric without manual updates.
- Dynamic prompts: Tailor your questions based on yesterday’s results or today’s weather for more relevant interactions.
Example
# Compute a human-friendly date label today_date = datetime.utcnow().strftime("%B %d, %Y") days_until_event = (event_date - datetime.utcnow()).days
You can then write:
“Today’s date is {today_date}, and we’re {days_until_event} days away from the product launch.”
Everyday analogy
Like the dashboard in your car that auto-updates your speed and fuel level—no need to guess.

B. Scoped vs. Global Variables

What they are
- Global: Accessible anywhere in your CRAFT session or across multiple prompts.
- Scoped (Local): Limited to a specific function, step, or workflow segment.
Why they matter
- Prevent collisions: Avoid accidentally overwriting a value when you only meant to change it for one mini-task.
- Clear intent: Readers (and A.I.) instantly know whether a variable is “universal” or “just for this section.”
Example
# Global constant PROJECT_NAME = "Ketelsen.ai" def onboarding_step(step): # Scoped variable local_message = f"Step {step} of onboarding" return local_message
Everyday analogy
Think of your smartphone apps: your contact list (global) is always there, while the text you type in one chat window (scoped) doesn’t carry over to another.

C. Fallback & Default Values

What they are
Safety-net assignments that ensure a variable always has a usable value, even if input is missing or malformed.
Why they matter
- Robustness: Prevents your prompts from crashing or producing “None” or blank outputs.
- Graceful degradation: You can still proceed with a sensible default rather than stopping entirely.
Example
# Use “Guest” if user_name wasn’t provided user_name = user_name if user_name else "Guest" # Or more succinctly user_name = user_name or "Guest"
Now every greeting falls back to:
“Hello, Guest—welcome aboard!”
Everyday analogy
Like keeping a spare tire in your trunk: if you get a flat, you don’t end up stranded—you keep rolling.

D. Validation-Enhanced Variables

What they are
Variables coupled with simple checks or validators to ensure they meet expected formats or ranges before use.
Why they matter
- Early error catching: Flag typos, invalid emails, or out-of-range numbers before they pollute your output.
- Cleaner logic: Your main prompts stay focused on content, not on handling every edge case.
Example
# Simple email validator def is_valid_email(email): return re.match(r"[^@]+@[^@]+\.[^@]+", email) user_email = input_email if not is_valid_email(user_email): user_email = "no-reply@ketelsen.ai" # fallback
Then you can confidently write:
“We’ll send the report to {user_email}.”
Everyday analogy
Like a bouncer checking IDs at the door—only valid guests get in, and the process is swift.

Summary

By layering in these advanced features, your variables become:

Computed for real-time relevance
Scoped to avoid accidental overwrites
Protected with defaults so nothing ever breaks
Validated to catch errors early

Together, they transform simple placeholders into a resilient, adaptive backbone for your CRAFT prompts—empowering both you and the A.I. to focus on creativity, not boilerplate.

5. Integration with Functions & Objects (Glance into the Bigger Picture)

In the coming weeks, we’ll dive deep into how CRAFT variables mesh with richer function libraries and object-oriented patterns. For now, here’s a high-level peek at how variables become the glue that holds those abstractions together.

A. Variables as Function Inputs

Concept
Treat your predeclared variables as the parameters to reusable functions, so you never hard-code values inside your logic.
How it fits
def generate_welcome_message(project, persona, date): return f"Welcome to {project}! I’m {persona}, here on {date} to guide you." # Later in your prompt flow welcome = generate_welcome_message(PROJECT_NAME, PERSONA_COACH, today_date)
Here, PROJECT_NAME, PERSONA_COACH, and today_date are simple variables—but they feed directly into your function, keeping the function pure and your data definitions centralized.

B. Objects as Variable Containers

Concept
Bundle related variables into small objects or data classes, giving you dot-notation access and a clear namespace.
How it fits
class SessionContext: def __init__(self, user, topic, step): self.user = user self.topic = topic self.step = step ctx = SessionContext(user_name, current_topic, onboarding_step)
Now, anywhere in your prompts you can reference ctx.user, ctx.topic, or ctx.step, rather than juggling separate standalone variables.

C. Chaining Functions & Objects

Concept
Build small, single-purpose functions that accept and return objects, so you can compose complex flows without losing track of your state.
How it fits
def advance_step(context): context.step += 1 return context # Chain steps in a pipeline ctx = SessionContext("Alice", "Landing Page Review", 1) ctx = advance_step(ctx) # Now step == 2 ctx = advance_step(ctx) # Now step == 3
Because ctx is an object holding all relevant variables, each function can update and carry forward the full state.

D. Preview of What’s Next

Utility Classes
Soon we’ll look at small helper classes (e.g., PromptBuilder, Validator) that internally rely on CRAFT variables for configuration.
Higher-Order Functions
We’ll explore functions that generate other functions—templates for “make-your-own” prompt builders that insert your project’s variables automatically.
Serialization & Persistence
Next time, we’ll see how to serialize these objects (with their embedded variables) into JSON or YAML, so you can save/load entire chat states.

Everyday Analogy

Imagine your variables are individual LEGO bricks. Functions are the little instruction sheets that tell you how to snap bricks together into sub-assemblies. Objects are the special baseplates that hold groups of bricks—you pick up or move the entire section at once, rather than fiddling with each brick.

Summary

Functions consume your variables as inputs and produce clean outputs—keeping logic and data definitions separate.
Objects package related variables into coherent, dot-accessible structures, making state easy to pass around.
Chaining these together sets the stage for powerful, modular pipelines that you’ll build out in the next article.

6. Use-Case Scenarios

Variables shine brightest when applied to real-world workflows. Below are four common scenarios where CRAFT variables supercharge your AI-driven processes.

A. Personalization

What it is
Dynamically swapping out key details—like user names, goals, or product features—so each prompt feels tailor-made.
Why it matters
- Higher engagement: Personalized messages grab attention more effectively than one-size-fits-all text.
- Scalability: Need to send 100 outreach emails? Change one variable and let the AI generate 100 bespoke messages.
Example
USER_NAME = "Jordan" USER_INTENT = "improving email open rates" PROMPT = f"Hi {USER_NAME}, I see you're {USER_INTENT}. Here’s a 3-step plan to help you achieve that…"
Everyday analogy
Like printing mailing labels with each recipient’s name instead of handwriting “Dear Friend”—it feels personal and saves time.

B. Model-Agnostic Prompts

What it is
Abstracting the AI engine (ChatGPT, Gemini, Claude, etc.) behind a variable so you can switch back-ends without rewriting prompts.
Why it matters
- Future-proofing: If a new model outperforms your current choice, you simply update AI_MODEL instead of hunting through every prompt.
- A/B comparisons: Test how different engines handle the same prompt to pick the best output.
Example
AI_MODEL = "ChatGPT-4" # or "Gemini-Pro", "Claude-2" PROMPT = f"Using {AI_MODEL}, summarize this report in bullet points."
Everyday analogy
Like ordering coffee at a café chain: you specify “skinny latte” once, then choose whether it’s made on the traditional espresso machine or their new super-automatic brewer—same order, different hardware.

C. A/B Testing

What it is
Defining multiple prompt variants via variables (e.g., VARIANT_A, VARIANT_B) and automatically alternating between them to see which yields better results.
Why it matters
- Data-driven refinement: Rather than guessing which prompt phrasing works best, you gather real performance data.
- Iterative improvement: Quickly retire underperforming variants and double down on winners.
Example
VARIANT_A = "Explain the new feature in three simple steps." VARIANT_B = "Describe the new feature with an analogy." SELECTED = VARIANT_A if campaign_day % 2 == 0 else VARIANT_B
Everyday analogy
Like running two headlines in an email subject line test—you see which one gets more clicks and then send the winner to the rest of your list.

D. Multilingual Support

What it is
Centralizing language settings (e.g., LANG = "en" or "es") so the same prompt logic can spin out outputs in multiple languages.
Why it matters
- Global reach: One codebase serves diverse audiences without duplicating entire prompt libraries.
- Consistent style: Ensures identical structure and quality across translations.
Example
LANG_CODE = "es" TRANSLATE_PROMPT = f"Please translate the following into {LANG_CODE}: '{BASE_TEXT}'"
Everyday analogy
Like setting the language on your phone: menus, apps, and notifications immediately switch to your chosen locale, using the same underlying software.

Everyday Takeaway

Personalization makes each interaction feel handcrafted.
Model-agnostic prompts keep you flexible and future-ready.
A/B testing turns guesswork into measurable insights.
Multilingual support breaks down language barriers with minimal extra effort.

Summary

CRAFT variables empower you to:

Customize content for any individual or segment.
Swap AI engines at will, without rewriting.
Experiment systematically to find top-performing prompts.
Translate effortlessly for a global audience.

By weaving these scenarios into your workflows, you build a prompt-engineering system that’s simultaneously powerful, adaptable, and easy to maintain.

7. Best Practices & Recommendations

Even the most powerful variable system can become a tangled mess without some guardrails. Here are tried-and-true practices to keep your CRAFT variables clean, clear, and effective.

A. Choose Descriptive—but Concise—Names

Why it matters:
- Clarity for you and the AI: A name like user_preference_theme immediately signals its role, whereas something cryptic like upt forces extra mental mapping.
- Avoid ambiguity: Descriptive names reduce “What does this do?” questions later.
Guidelines:
- Aim for 2–4 words max.
- Use full words over obscure abbreviations (e.g., marketing_budget not mktg_bud).
- Prefix booleans with is_ or has_ (e.g., is_premium_user).
Everyday analogy:
Like labeling your pantry jars “Almond Flour” instead of “AF”—you always know what you’re grabbing.

B. Centralize Documentation in a PROJECT_VARIABLES Section

Why it matters:
- Single source of truth: Rather than hunting through 10 different prompts, you know all your variables live in one block.
- Onboarding ease: New collaborators (or future you) can get up to speed by scanning that one section.
What to include:
PROJECT_VARIABLES: PROJECT_NAME: "Ketelsen.ai" # Your site’s brand identifier DEFAULT_LANGUAGE: "en" # ISO code for output localization ONBOARDING_STEP: 1 # Tracks where we are in the flow ...
Everyday analogy:
Like a legend on a map—once you learn it, every symbol makes sense.

C. Leverage Namespacing to Avoid Collisions

Why it matters:
- Scalability: When your prompt library grows, flat names like start_date in two different contexts can accidentally overwrite each other.
- Modularity: You can move or share namespaces (e.g., Marketing vs. Analytics) without merging every variable.
Pattern:
class Marketing: campaign_name = "SpringSale" budget = 100000 class Analytics: campaign_name = "Q2Review" # Different “campaign_name” in another namespace report_format = "PDF"
Everyday analogy:
Like having separate drawers for “Office Supplies” and “Kitchen Utensils”—you never mix paperclips with spoons.

D. Schedule Regular Reviews & Prune Unused Variables

Why it matters:
- Avoid “variable bloat”: Old or orphaned variables clutter your workspace and can lead to confusion.
- Maintain performance: Fewer variables mean less cognitive load and fewer tokens wasted.
How to do it:
- Monthly check-ins: Skim your PROJECT_VARIABLES and highlight any names you haven’t used in recent prompts.
- Automated linting (optional): If you build tooling later, flag declarations that never get referenced.
Everyday analogy:
Like spring-cleaning your closet—if you haven’t worn it in a year, it’s probably safe to let it go.

E. Extra Tips for Long-Term Health

Annotate with Comments
- Briefly note units or expected formats (# in USD, # ISO date YYYY-MM-DD).
Enforce Consistent Ordering
- Group by category: constants → context → workflow → personas.
Version-Control Your Variables File
- Track when names change or defaults shift so you can roll back if something breaks.
Use Defaults & Fallbacks Wisely
- Whenever possible, set sane defaults so missing inputs don’t derail the flow.

Summary

Descriptive names keep everything transparent.
Centralized docs act as your project’s legend.
Namespacing prevents cross-talk in large libraries.
Regular pruning stops bloat in its tracks.
Bonus tips around comments, ordering, and versioning round out a healthy workflow.

By baking these practices into your routine, your CRAFT variables will stay as nimble and reliable as the code they support—ready to scale as your projects grow.

Week 4: Advanced CRAFT Variable Concepts

A. Dynamic & Context-Aware Variables: Variables That Think With You

Static variables are useful, but dynamic variables offer a new level of sophistication.

User-Modifiable Variables: Picture this: you're working on a project and need to tweak a standard brand tone for just one email. Instead of overhauling your core prompts, User-Modifiable Variables would allow you to temporarily override a global variable's value for the current task or session. This offers incredible flexibility for A/B testing and one-off adjustments without permanent changes to your foundational setup.
Session-State Variables: Ever wished your AI had a better memory of your current conversation? Session-State Variables act as the AI's short-term memory, automatically capturing and updating based on the flow of your interaction (e.g., CURRENT_SESSION_TOPIC, LAST_AI_RESPONSE_TYPE). This enables more intelligent, context-aware prompts, allowing the AI to say, "Based on the outline we just created..."
Conditional Variables: Let's introduce some logic! Conditional Variables allow a variable’s value to be determined based on other conditions or variables (e.g., IF UserIntent == "SalesInquiry" THEN SUPPORT_CONTACT = SalesTeamEmail ELSE SUPPORT_CONTACT = GeneralSupportEmail). This makes your prompts and workflows more adaptive, tailoring AI responses to specific situations automatically.

B. Enhanced AI Control & Personalization: Tailoring Your AI Co-Pilot

Take the reins of your AI's behavior and make it truly yours.

AI Behavior Variables: Go beyond a single, static AI persona. These variables could fine-tune aspects like AI_CREATIVITY_LEVEL, AI_EXPLANATION_DEPTH, or even AI_TONE_ADJECTIVE (drawing from CRAFT™️ data types like AITone and AIPersona ). This means you can instantly shift your AI from a "Witty Brainstormer" to a "Formal Analyst" as needed.
Tiered Access Variables: Not everyone needs every bell and whistle all the time. Tiered Access Variables can customize the CRAFT™️ experience based on a user's expertise level or project complexity. Beginners might see simpler prompt libraries, while experts get the full arsenal, aligning with CRAFT’s goal of "Accessibility Through Familiarity".
Prompt Chaining Variables: Imagine executing a complex, multi-step task (like drafting a full article from topic idea to image suggestions) by calling a single variable. These "workflow invocation variables" would store a predefined sequence of prompts or actions (extending the AIWorkflow concept ), massively boosting automation and consistency.

C. Content Generation & Management: Streamlining Your Creative Output

Content is king, but creating it can be time-consuming. These variables help you reign supreme.

Content Snippet Variables: Store those frequently used text blocks—disclaimers, calls-to-action, company taglines, even RichContent snippets—as variables. Update them in one place, and the change reflects everywhere, ensuring accuracy and brand voice consistency.
Style Guide Variables: Embed your brand's visual and textual DNA directly into CRAFT™️. Variables like BRAND_PRIMARY_COLOR (using the ColorPalette data type ) or WEBSITE_FONT_FAMILY_MAIN ensure the AI generates content and design instructions that are always on-brand.
Templating Variables with Placeholders: Need to send out personalized welcome emails or generate similar social media posts regularly? Templating variables store predefined content structures with placeholders (e.g., WELCOME_EMAIL_TEMPLATE = "Hi {{USER_NAME}}, welcome to {{PRODUCT_NAME}}!"). Instruct the AI to fill in the blanks, and watch your productivity soar.

D. Meta & Operational Variables: Keeping Your CRAFT™️ Engine Running Smoothly

These behind-the-scenes variables are crucial for managing the framework itself, ensuring efficiency and clarity.

Version Control Variables: Keep track of changes to your prompt libraries, configurations, or even the CRAFT™️ specifications themselves using variables like ACTIVE_CRAFT_SPEC_VERSION (potentially integrating with the VersionTag data type ). This supports "Cumulative Intelligence" by documenting the evolution of your AI setup.
Constraint Set Variables: Group common limitations or guidelines (e.g., "Legal Review Constraints," "Image Generation Safety Rules") into a single variable, drawing on the ConstraintSet data type. Apply these sets with a simple reference, ensuring consistency and simplifying your prompts.
Namespace Alias Variables: Long namespace strings can clutter your instructions. Namespace Alias Variables create shorter, more readable stand-ins (e.g., ALIAS_ANALYTICS for BusinessMetrics.Analytics), improving clarity and even contributing to "Conservation of Tokens".

A. Dynamic & Context-Aware Variables

This category focuses on variables that are not static but can change or be influenced by the context of the interaction. This aligns with CRAFT's principles of "Cumulative Intelligence" and "Human-AI Partnership" by allowing the system to learn and adapt.

User-Modifiable Variables:
- Concept: This idea allows for the temporary alteration of a global variable's value for a specific scope (like the current task or the entire session) without needing to edit the core CFT-PROJ-****_*******-v******.txt file. This is useful for experimentation or when a slight deviation from a standard value is needed for a particular part of the conversation.
- Potential Mechanism within CRAFT:
  - A specific syntax could be introduced using the human-to-AI comment style, #H->AI::. For example: #H->AI::ModifyVariable: (TARGET_VARIABLE = BRAND_TONE, NEW_VALUE = "Slightly more formal", SCOPE = "CurrentTask_EmailDraft") #H->AI::AppendToVariable: (TARGET_VARIABLE = KEY_FEATURES_LIST, ADD_TEXT = ", Now with AI integration!", SCOPE = "CurrentSession")
  - The AI would then internally recognize this instruction and use the modified value for the defined scope. The original variable definition in the project file remains unchanged.
- Benefits:
  - Flexibility: Enables users to quickly test variations (e.g., trying a different tone for a specific email) without the overhead of permanent changes.
  - Experimentation: Facilitates A/B testing of prompt components or content variations easily.
  - Enhanced "Structure Enables Freedom": The underlying structure remains, but users gain freedom to make temporary, controlled adjustments.
  - Reduced Token Usage for Modifications: Instead of re-prompting with a long explanation of a slight change, a concise command can alter a variable's behavior for a specific context.
Session-State Variables:
- Concept: These are variables whose values are automatically populated or updated by the AI based on the evolving context of the current interaction or the known state of the project. They act like an internal memory for the session's key attributes.
- Potential Mechanism within CRAFT:
  - The AI could be programmed to maintain and update a set of predefined session-state variables. Examples:
    - SESSION_STATE.CURRENT_TOPIC: Automatically updated based on the main subject of the recent conversation.
    - SESSION_STATE.LAST_AI_ACTION: Records the type of the AI's last significant action (e.g., "GeneratedText", "AnsweredQuestion", "CreatedList").
    - SESSION_STATE.USER_SENTIMENT_TREND: Tracks user sentiment, perhaps using the AISentiment data type, to allow the AI to adapt its responses.
    - SESSION_STATE.CURRENT_HANDOFF_ID: Derived from CFT-PROJ-****_AI-CHAT-HISTORY-v****.txt to maintain continuity across sessions.
  - These variables would be accessible in prompts like any other variable.
- Benefits:
  - Increased Contextual Awareness: Allows prompts and functions to be more aware of what has just happened or what the current focus is.
  - Smarter Interactions: The AI can use these variables to make more relevant suggestions or to tailor its responses more effectively. For example, if SESSION_STATE.LAST_AI_ACTION == "GeneratedOutline", a subsequent prompt could easily refer to "the outline we just created."
  - Supports "Cumulative Intelligence": The session itself builds a mini-knowledge base that influences subsequent interactions.
  - Improved Handoffs: Key session state variables could be explicitly included in the HANDOFF_SNAPSHOT for even smoother transitions between AI assistants or sessions.
Conditional Variables:
- Concept: The value of a conditional variable is not fixed but is determined dynamically based on the evaluation of other variables, data types, or conditions within the CRAFT environment. This introduces basic logic into how variables are resolved.
- Potential Mechanism within CRAFT:
  - A simple IF-THEN-ELSE structure could be defined for certain variables, potentially within the PROJECT_VARIABLES section of CFT-PROJ-****_*******-v******.txt.
  - This could leverage existing CRAFT data types like UserIntent, Metric, or even custom boolean flags.
  - Example: # Define supporting variables USER_TYPE = "Entrepreneur" PROJECT_BUDGET_METRIC = Metric("ProjectBudget", 5000, unit="USD")
    # Conditional Variable Definition CONTACT_PERSON = IF (USER_TYPE == "Entrepreneur" AND PROJECT_BUDGET_METRIC.value > 10000) THEN "Senior Account Manager" ELSE "General Support" MARKETING_COPY_FOCUS = IF (SESSION_STATE.CURRENT_TOPIC == "NewProductLaunch") THEN "Highlight innovation and unique features" ELSE "Reinforce brand value and reliability"
- Benefits:
  - Adaptive Outputs: Prompts and content can automatically adjust based on specific criteria, leading to more tailored and relevant AI outputs.
  - Reduced Complexity in Prompts: Instead of writing complex conditional logic within each prompt, the logic is encapsulated within the variable definition itself.
  - Streamlined Workflows: Common decision points can be automated through these variables. For example, selecting the correct email template or support tier based on user data.
  - Alignment with "Code as Conversation": Introduces a basic programming concept (conditional logic) in an accessible way within the conversational framework.

By implementing these dynamic and context-aware variable types, the CRAFT framework can become significantly more powerful, flexible, and intelligent, moving closer to a true Human-AI partnership where the system can adapt and respond more intuitively to the nuances of complex projects and conversations.

This set of variable adaptations focuses on giving the user more granular control over how the AI behaves and tailoring the AI's responses and actions to specific user profiles or preferred interaction styles. This directly supports the CRAFT principles of "Human-AI Partnership" and "Accessibility Through Familiarity" by making the AI more adaptable and its controls more intuitive.

B. Variables for Enhanced AI Control & Personalization

AI Behavior Variables:
- Concept: These variables allow users to define and easily switch between different AI behavioral profiles or fine-tune specific aspects of the AI's interaction style beyond the initial role-setting prompt. This is more dynamic than a static persona set at the beginning of a session.
- Potential Mechanism within CRAFT:
  - Define global or project-specific variables that map to AI behavioral characteristics. These could leverage or extend CRAFT data types like AITone (line 61, CFT-FWK-SPEC-v25d1.txt) and AIPersona (line 65, CFT-FWK-SPEC-v25d1.txt).
  - Examples:
    - CURRENT_AI_TONE_PROFILE = AITone("Witty", "Concise", "SlightlyInformal")
    - AI_EXPLANATION_LEVEL = "ExpertBrief" (Values: "BeginnerDetailed", "IntermediateSummary", "ExpertBrief")
    - AI_CREATIVITY_SETTING = 0.8 (Where 0.1 is highly factual, 1.0 is highly imaginative)
    - AI_QUESTION_ASKING_MODE = "ClarifyBeforeProceeding" (Values: "AssumeAndProceed", "ClarifyBeforeProceeding", "OfferOptions")
  - Users could then reference these variables in prompts: "Summarize this document using AI_EXPLANATION_LEVEL." or instruct the AI to adopt a specific behavior profile: #H->AI::AdoptBehaviorProfile: (PROFILE_NAME = "CreativeBrainstormerProfile").
- Benefits:
  - Granular Control: Users can precisely adjust how the AI communicates and operates for different tasks (e.g., a factual tone for technical summaries, a creative tone for brainstorming).
  - Dynamic Adaptation: AI behavior can be changed mid-conversation to suit evolving needs.
  - Consistency in Nuance: Standardizes subtle aspects of AI interaction, making the AI feel more predictable and aligned with user preferences across various tasks.
  - Personalization: Users can define their preferred AI interaction styles and save them as variable sets.
Tiered Access Variables (or Profile-Based Variables):
- Concept: This involves creating sets of variables, prompts, or even available functions that are conditionally loaded or prioritized based on a "user level," "project type," or "access tier" variable. This allows the CRAFT experience to be tailored to different user needs or project complexities.
- Potential Mechanism within CRAFT:
  - A primary variable like USER_PROFILE_TIER or PROJECT_COMPLEXITY_LEVEL is set.
  - Based on this primary variable's value, specific sets of other variables become active or are prioritized.
  - Example:
    - USER_PROFILE_TIER = "Beginner"
      - IF USER_PROFILE_TIER == "Beginner":
        ACTIVE_PROMPT_LIBRARY = "BeginnerFriendlyPrompts"
        DEFAULT_EXPLANATION_STYLE = AITone("Simple", "StepByStep")
        FEATURE_SET_ACCESS = ["BasicTextGen", "SimpleSummaries"]
    - USER_PROFILE_TIER = "Expert"
      - IF USER_PROFILE_TIER == "Expert":
        ACTIVE_PROMPT_LIBRARY = "AdvancedExperimentalPrompts"
        DEFAULT_EXPLANATION_STYLE = AITone("Technical", "Concise")
        FEATURE_SET_ACCESS = ["AllFeatures", "APIAccess", "AdvancedAnalytics"]
- Benefits:
  - Tailored User Experience: Beginners are not overwhelmed with advanced options, while experts have access to the full suite of capabilities. This aligns with "Accessibility Through Familiarity" (line 10, CFT-FWK-SPEC-v25d1.txt).
  - Simplified Interface for Novices: Reduces cognitive load for users who are new to prompt engineering or the specific CRAFT project.
  - Efficient Workflows for Experts: Experts can bypass introductory explanations or simplified tools.
  - Controlled Feature Rollout: Useful for managing access to experimental or resource-intensive features.
Prompt Chaining Variables (or Workflow Invocation Variables):
- Concept: A single variable that represents a predefined sequence of prompts, actions, or even function calls. Invoking this variable would trigger the entire chain, effectively creating a mini-workflow. This is an extension of the AIWorkflow data type (line 45, CFT-FWK-SPEC-v25d1.txt) but potentially simpler to invoke via a single variable.
- Potential Mechanism within CRAFT:
  - A variable could store an ordered list of Prompt data types, AIActionTemplate names, or function calls.
  - Example:
    - ARTICLE_GENERATION_WORKFLOW_VAR = [
      - Prompt("Brainstorm 5 titles for a blog post about {{topic}}"),
      - AIActionTemplate("GenerateOutline", topic="{{topic}}", selected_title="{{output_of_step_1}}"),
      - Instruction("Write a draft of 500 words based on the outline {{output_of_step_2}} using AITone.FriendlyInformative"),
      - MainBlogImage(content="{{output_of_step_3}}") // Referencing global function
      - ]
  - The user could then initiate this by: #H->AI::ExecuteWorkflowVar: (WORKFLOW_NAME = ARTICLE_GENERATION_WORKFLOW_VAR, topic = "AI in Marketing").
- Benefits:
  - Automation of Complex Tasks: Simplifies the execution of multi-step processes into a single command.
  - Consistency in Execution: Ensures that common sequences of tasks are performed in the same way every time, reducing errors.
  - Reusability: Complex workflows can be defined once and reused across many projects or sessions.
  - Token Efficiency: A single variable call can replace many lines of individual prompt instructions.

By implementing these types of variables, CRAFT users gain significantly more leverage over the AI's behavior and can personalize their interactions to a much greater degree. This makes the AI not just a tool, but a more adaptable and controllable partner in achieving their goals.

This category of variables is designed to streamline and standardize the creation and handling of content within the CRAFT framework. For entrepreneurs and professionals (the target audience of Ketelsen.ai), consistent and efficient content production is key. These variables help ensure brand voice, messaging accuracy, and reduce repetitive work. They align with CRAFT's goals of "Reusability," "Consistency," and "Efficiency" (lines 100-102, CFT-FWK-SPEC-v25d1.txt).

C. Variables for Content Generation & Management

Content Snippet Variables:
- Concept: These variables store reusable blocks of text or formatted content that are frequently used across various communications. This could range from legal disclaimers, standard calls-to-action, company taglines, product descriptions, or even pre-formatted RichContent elements.
- Potential Mechanism within CRAFT:
  - Variables defined in PROJECT_VARIABLES in CFT-PROJ-0001_BUILDINGCRAFT-v25d1.txt.
  - Could be simple strings for plain text or utilize the RichContent data type (line 48, CFT-FWK-SPEC-v25d1.txt) for snippets that include formatting, links, or simple media references.
  - Examples:
    - LEGAL_DISCLAIMER_INVESTMENT = "Past performance is not indicative of future results. All investments carry risk."
    - STANDARD_CTA_BUTTON_TEXT = "Learn More Now"
    - COMPANY_MISSION_STATEMENT_RICH = RichContent("Our mission is to **empower** innovators through [accessible AI tools](https://www.ketelsen.ai).")
    - PRODUCT_X_CORE_BENEFITS_LIST = "- Benefit 1\n- Benefit 2\n- Benefit 3"
  - In prompts, users would reference these variables: "Append LEGAL_DISCLAIMER_INVESTMENT to the email." or "Insert COMPANY_MISSION_STATEMENT_RICH at the end of the 'About Us' section."
- Benefits:
  - Messaging Consistency: Ensures that standard phrases, disclaimers, and branding elements are used uniformly across all content.
  - Time Savings: Eliminates the need to retype or search for frequently used text blocks.
  - Accuracy: Reduces the risk of errors or outdated information in commonly used snippets, as updates only need to happen in one central variable.
  - Brand Voice Reinforcement: Helps maintain a consistent tone and style in all communications.
Style Guide Variables:
- Concept: These variables store specific elements of a brand's style guide, making it easy for the AI to adhere to visual and textual branding guidelines when generating content or providing instructions for design.
- Potential Mechanism within CRAFT:
  - Defined in PROJECT_VARIABLES.
  - Can leverage existing CRAFT data types like ColorPalette (line 35, CFT-FWK-SPEC-v25d1.txt) or simple string/numeric variables for other style attributes.
  - Examples:
    - BRAND_PRIMARY_COLOR = ColorPalette("Primary Blue", "#005A9C")
    - BRAND_SECONDARY_COLOR = ColorPalette("Accent Orange", "#FF7F00")
    - WEBSITE_FONT_FAMILY_MAIN = "'Open Sans', sans-serif"
    - HEADING_LEVEL_1_FONT_SIZE_PX = 32
    - STANDARD_IMAGE_ASPECT_RATIO = "16:9"
    - LOGO_USAGE_GUIDELINE_TEXT = "Ensure a minimum clear space of 20px around the logo."
  - The AI can be instructed to use these: "Design a social media graphic for LinkedIn using BRAND_PRIMARY_COLOR as the background and BRAND_SECONDARY_COLOR for accents." or "Format the blog post title according to HEADING_LEVEL_1_FONT_SIZE_PX and WEBSITE_FONT_FAMILY_MAIN."
- Benefits:
  - Brand Consistency: Helps the AI generate content and provide design instructions that are visually and stylistically aligned with the brand.
  - Simplified Design Briefs: Makes it easier to communicate design preferences to the AI.
  - Efficiency for Visuals: Speeds up the process of creating or specifying visuals when the AI already knows the core style elements.
  - Facilitates Collaboration: Ensures that even if different team members interact with the AI, the stylistic output remains consistent.
Templating Variables with Placeholders:
- Concept: Variables that store predefined content structures (like an email, a social media post, or a report section) with clearly marked placeholders. The AI can then be instructed to populate these placeholders with specific data from other variables or contextual information. This is a more structured form of the Prompt data type (line 42, CFT-FWK-SPEC-v25d1.txt) when applied to content generation.
- Potential Mechanism within CRAFT:
  - Strings with a consistent placeholder syntax (e.g., {{PLACEHOLDER_NAME}}).
  - Stored in PROJECT_VARIABLES.
  - Examples:
    - `WELCOME_EMAIL_TEMPLATE = """Subject: Welcome to {{PRODUCT_NAME}}, {{USER_FIRST_NAME}}!
      Hi {{USER_FIRST_NAME}},
      Thanks for signing up for {{PRODUCT_NAME}}. We're thrilled to have you on board. To get started, check out our quick start guide: {{QUICK_START_GUIDE_URL}}
      Best, The {{COMPANY_NAME}} Team"""`
    - SOCIAL_POST_ANNOUNCEMENT_TEMPLATE = "Exciting news! We've just launched {{FEATURE_NAME}} for {{PRODUCT_NAME}}. Learn more: {{LEARN_MORE_URL}} #{{HASHTAG_1}} #{{HASHTAG_2}}"
  - Users would instruct the AI: "Generate an email using WELCOME_EMAIL_TEMPLATE. Set USER_FIRST_NAME to 'Alex', PRODUCT_NAME to 'AI Innovator Pro', QUICK_START_GUIDE_URL to 'ketelsen.ai/docs/innovator-pro-start', and COMPANY_NAME to 'Ketelsen AI Labs'."
- Benefits:
  - Rapid Content Creation: Dramatically speeds up the generation of repetitive content formats.
  - Reduced Manual Effort: Minimizes the need to manually draft similar pieces of content from scratch.
  - Scalability: Allows for easy generation of personalized content at scale (e.g., sending welcome emails to many new users).
  - Maintains Structure and Key Messaging: Ensures that all essential elements and phrasing are included in templated communications, while still allowing for personalization.

By implementing these variables for content generation and management, users of Ketelsen.ai can leverage the CRAFT framework to produce higher quality, more consistent content with greater efficiency, directly addressing the pain points of entrepreneurs who need to manage their time effectively while maintaining a professional online presence.

These variables are less about the direct content the AI produces and more about managing the CRAFT framework itself, optimizing interactions, and maintaining clarity and control over the project's evolution. They support the underlying operational efficiency and structural integrity of CRAFT, aligning with principles like "Cumulative Intelligence", "Conservation of Tokens", and overall project organization.

D. Meta & Operational Variables

Version Control Variables:
- Concept: These variables are used to track the versions of various components within the CRAFT project, such as specific prompt libraries, configuration sets, functions, or even the project files themselves. This helps in managing changes, rolling back to previous states if needed, and understanding the evolution of the project. This concept can integrate with or complement the VersionTag data type (lines 97-99, CFT-FWK-SPEC-v25d1.txt).
- Potential Mechanism within CRAFT:
  - Defined in PROJECT_VARIABLES in CFT-PROJ-0001_BUILDINGCRAFT-v25d1.txt.
  - Could be simple string variables (e.g., "v1.2.3", "2025-05-11-revA") or could be programmatically updated by a (hypothetical) CRAFT management function.
  - Examples:
    - ACTIVE_CRAFT_SPEC_VERSION = "v25d1" (Referencing the version of CFT-FWK-SPEC-v25d1.txt)
    - MAIN_PROMPT_LIBRARY_VERSION = "3.5"
    - USER_PERSONA_DEFINITIONS_LAST_UPDATED = "2025-05-10T14:30:00Z"
    - CURRENT_PROJECT_FILE_VERSION = PROJECT_META.Version (Dynamically linking to project metadata if structured that way)
  - These variables can be referenced in HANDOFF_SNAPSHOTS to ensure the next session uses the correct component versions.
- Benefits:
  - Change Tracking: Provides a clear way to see how and when different parts of the project have evolved.
  - Dependency Management: Helps understand dependencies between different versioned components.
  - Rollback Capability: In theory, allows reverting to a previously known good state if a new version introduces issues.
  - Supports "Cumulative Intelligence": By versioning key assets, the evolution of the project's intelligence is better documented and manageable.
  - Clarity in Collaboration: If multiple people work on a CRAFT project, version variables ensure everyone is aware of the current state of components.
Constraint Set Variables:
- Concept: A variable that groups a predefined collection of constraints, rules, or limitations that can be easily applied to various AI tasks or prompts. This expands on the idea of individual #H->AI::Constraint: comments (line 28, CFT-FWK-SPEC-v25d1.txt) by making sets of them reusable, and directly ties into the ConstraintSet data type (lines 88-89, CFT-FWK-SPEC-v25d1.txt).
- Potential Mechanism within CRAFT:
  - Defined in PROJECT_VARIABLES.
  - The variable would hold a list or collection of individual constraint statements or a reference to a named ConstraintSet.
  - Examples:
    - LEGAL_REVIEW_CONSTRAINTS = ConstraintSet("LegalReviewGuidelines", rules=["Avoid making promises or guarantees", "Include standard disclaimer XYZ", "Ensure factual accuracy from approved sources"])
    - BLOG_POST_TONE_CONSTRAINTS_FORBES = ["Maintain formal yet engaging tone", "Target audience: entrepreneurs", "Word count between 800-1200 words"] (as a list of strings)
    - IMAGE_GENERATION_SAFETY_CONSTRAINTS = ConstraintSet("SafetyFirstVisuals", rules=["No depictions of violence", "Ensure all subjects are adults", "Avoid copyrighted symbols"])
  - Usage in a prompt: "Generate a blog post outline applying constraints from BLOG_POST_TONE_CONSTRAINTS_FORBES." or #H->AI::Focus: (Apply ConstraintSet("SafetyFirstVisuals") to all image generation tasks in this session.)
- Benefits:
  - Reusability & Efficiency: Avoids repeating the same set of constraints across multiple prompts, saving tokens and effort.
  - Consistency: Ensures that standard limitations or guidelines are applied uniformly where needed.
  - Simplified Prompting: Makes prompts cleaner and easier to read by referencing a single constraint set variable instead of listing many individual constraints.
  - Easy Updates: If a standard set of constraints changes, it only needs to be updated in one place (the variable definition).
Namespace Alias Variables:
- Concept: These variables act as shorter, more convenient aliases for frequently used or lengthy namespaces associated with CRAFT data types (line 33, CFT-FWK-SPEC-v25d1.txt). This can improve the readability of prompts and instructions, and slightly reduce token usage.
- Potential Mechanism within CRAFT:
  - Defined in PROJECT_VARIABLES at the beginning of a project file or session.
  - The AI would need to be instructed to recognize these aliases when parsing subsequent instructions.
  - Examples:
    - ALIAS_ANALYTICS = "BusinessMetrics.Analytics"
    - ALIAS_CONTENT = "Marketing.ContentGeneration.Blog"
    - ALIAS_SOCIAL = "SocialMedia.Posts.LinkedIn"
  - Usage:
    - Instead of: BusinessMetrics.Analytics.Metric("Sales", 1000)
    - Use: ALIAS_ANALYTICS.Metric("Sales", 1000)
    - Instead of: Marketing.ContentGeneration.Blog.AIInstruction("Write a post")
    - Use: ALIAS_CONTENT.AIInstruction("Write a post")
- Benefits:
  - Improved Readability: Makes instructions and variable definitions less verbose and easier to understand at a glance.
  - Token Savings: While minor per instance, cumulatively this can contribute to "Conservation of Tokens" in long or complex sessions.
  - Reduced Typos: Shorter aliases are less prone to typographical errors compared to long namespace strings.
  - Convenience: Simplifies the process of referencing specific data types within nested namespaces.

By incorporating these Meta and Operational Variables, the CRAFT framework becomes more robust, maintainable, and easier to manage, especially as projects grow in complexity and evolve over time. They provide a layer of operational control that enhances the overall user experience and the efficiency of the AI collaboration.

Transforming AI Interactions: Advanced Variable Concepts in CRAFT Framework

CRAFT (Configurable Reusable AI Framework Technology) has been designed to transform casual AI interactions into structured, efficient workflows by adapting principles of object-oriented programming to AI conversations. While basic variables serve as foundational building blocks, several advanced variable concepts can dramatically enhance the framework's power and flexibility.

This guide explores eight advanced variable concepts that can take your CRAFT framework implementation to the next level, creating deeper, more intuitive, and more meaningful interactions with AI chat tools.

1. Smart Variables: Beyond Basic Value Storage

What Are Smart Variables?

Smart variables represent an evolution from traditional static variables in programming. While basic variables simply store and retrieve values, smart variables add intelligence and functionality to data storage within the CRAFT framework.

Think of them as miniature state machines that not only contain data but also know how to process, transform, and adapt it based on context, triggers, or previous states.

Benefits of Smart Variables

State Persistence Across Conversation Turns: Smart variables maintain their state and history throughout a conversation, allowing for more coherent and context-aware AI responses.
Conditional Logic Without Programming: Entrepreneurs can include decision-making capabilities within variables without writing actual code, making AI interactions more dynamic.
Progressive Disclosure and Adaptive Detail: Smart variables can reveal different levels of detail based on the user's needs or the conversation context.
Event-Triggered Behavior: Smart variables can respond to specific triggers or events within the conversation, automatically updating their state.
Memory Optimization: Smart variables reduce token usage by storing complex information in compact formats that expand only when needed.

2. Context-Aware Variables: Adaptive Content Based on Situation

What Are Context-Aware Variables?

Context-aware variables are dynamic data structures that adapt their content or behavior based on the current context of an AI interaction. Unlike traditional variables that return the same value regardless of circumstances, context-aware variables intelligently modify their output based on factors such as:

The stage of conversation
The audience being addressed
The medium of communication
The user's expertise level
The specific task being performed

Benefits of Context-Aware Variables

Audience-Adaptive Communication: Content automatically adjusts to the needs, knowledge level, and preferences of different audiences without requiring explicit instructions.
Communication Channel Optimization: Content automatically formats itself appropriately for different media channels without repetitive formatting instructions.
Skill Level Adaptation: Instructions and explanations automatically adjust to the user's skill level, providing appropriate detail and terminology.
Workflow Stage Awareness: Variables automatically present the appropriate information for the current stage of a process or workflow.
Time and Date Sensitivity: Content automatically adapts based on temporal factors like time of day, day of week, or seasonal considerations.

3. Multi-Modal Variables: Content in Multiple Formats

What Are Multi-Modal Variables?

Multi-modal variables are advanced data structures that can store and output content in multiple formats or modes from a single source of truth. Unlike traditional variables that contain a single value, multi-modal variables maintain different representations of the same underlying information, allowing you to:

Present the same core information in different media formats (text, lists, tables, etc.)
Adapt content for different presentation contexts while preserving consistency
Store related variations of content without duplicating the underlying information

Benefits of Multi-Modal Variables

Format Flexibility Without Redundancy: Maintain a single source of truth while accessing it in whatever presentation format best suits your current need.
Content Reusability Across Mediums: Define content once and reuse it across multiple communication channels, reducing inconsistencies and saving time.
Technical-to-Business Translation: Bridge the gap between technical details and business value by storing both perspectives of the same information.
Content Adaptation Without Rewriting: Quickly adapt content to different formats without having to rewrite or reformulate information.
Data-Driven Decision Support: Present the same data in different analytical frames to support various decision-making processes.

4. Variable Templates: Pre-Configured Variable Structures

What Are Variable Templates?

Variable templates are pre-configured, reusable structures for creating variables that follow consistent patterns for common business tasks. They act as "blueprints" or "factories" that generate fully-formed variables with standardized formats, naming conventions, and internal structures.

Benefits of Variable Templates

Consistency Across Projects and Team Members: Ensure all variables follow the same structure and naming conventions, even when created by different team members or across multiple projects.
Rapid Creation of Complex Variables: Quickly create sophisticated variable structures without repetitive definition of common elements.
Built-In Best Practices and Structure: Embed domain expertise and best practices directly into the variable structure, ensuring quality and completeness.
Reduced Cognitive Load and Error Prevention: Eliminate mental overhead and errors by automating the creation of properly structured variables.
Standardized Documentation and Self-Description: Create variables that document themselves, making them more discoverable and understandable.

5. Variable Namespaces: Organizational Containers

What Are Variable Namespaces?

Variable namespaces are organizational containers that group related variables together under a common prefix or category. They provide a hierarchical structure for managing variables, similar to how folders organize files in a computer system or how packages organize code in programming languages.

Benefits of Variable Namespaces

Organized Knowledge Management: Create a logical, intuitive structure for managing large numbers of variables across complex projects.
Contextual Relevance and Clarity: Provide immediate context for variables, making their purpose and relationships clearer without additional documentation.
Conflict Prevention and Resolution: Prevent naming collisions and confusion when different domains use similar terminology.
Modularity and Reusability: Create modular knowledge components that can be imported, exported, and reused across projects.
Hierarchical Organization for Complex Projects: Create multiple levels of organization for large-scale projects with many variables.

6. Dynamic Variables: Self-Updating State Management

What Are Dynamic Variables?

Dynamic variables are sophisticated data structures that can automatically update their values or behaviors based on conversation context, user interactions, or changing conditions. Unlike static variables that maintain fixed values until explicitly changed, dynamic variables contain built-in logic that enables them to transform, track state changes, adapt to conversation progress, and more.

Benefits of Dynamic Variables

Conversation Stage Awareness: Variables automatically track and adapt to the different stages of a conversation, helping maintain appropriate context.
Progress-Tracking and Milestones: Automatically monitor progress toward goals, update completion status, and recognize milestone achievements.
Contextual Memory and Learning: Variables remember past states and interactions, enabling them to learn from and adapt to conversation patterns.
Intelligent Data Transformation: Automatically transform data between formats, units, or representations based on contextual needs.
State-Based Decision Logic: Implement complex decision trees that evolve based on conversation history and user choices.

7. Variable Inheritance: OOP-Inspired Relationships

What Is Variable Inheritance?

Variable inheritance is a powerful concept borrowed from object-oriented programming that allows new variables to inherit and extend the properties, structures, and behaviors of existing variables. This creates a parent-child relationship between variables, where child variables automatically receive all the characteristics of their parent while having the ability to override or extend these characteristics.

Benefits of Variable Inheritance

Template Customization Without Repetition: Create specialized versions of common templates without duplicating shared elements.
Content Specialization and Extension: Build upon existing content by adding specialized information without altering the original structure.
Centralized Updates and Maintenance: Update parent variables to automatically propagate changes to all child variables, ensuring consistency without manual updates.
Multi-Level Specialization: Create hierarchies of increasingly specialized variables to address specific use cases while maintaining inheritance chains.
Interface-Based Consistency: Define standard interfaces that inherited variables must implement, ensuring consistency across different implementations.

8. Time-Sensitive Variables: Temporal Intelligence

What Are Time-Sensitive Variables?

Time-sensitive variables are sophisticated data structures that have an awareness of time built into their definition and behavior. Unlike traditional variables that remain static until manually changed, time-sensitive variables can automatically expire after a set duration, transform based on temporal conditions, track their own history, evolve through predefined stages over time, and more.

Benefits of Time-Sensitive Variables

Automatic Content Freshness: Ensure information stays current by automatically updating or flagging content based on age.
Time-Limited Offers and Opportunities: Create promotional content with built-in expiration to avoid outdated offers remaining in materials.
Project Phase Transitions: Automatically track project timelines and advance through development phases based on calendar dates.
Seasonal and Contextual Adaptation: Automatically adjust content and recommendations based on time of year, day of week, or time of day.
Version Control and Deprecation Management: Manage software feature deprecation, API changes, and version transitions with time-aware variables.

Basic Examples: Advanced Variable Concepts in CRAFT Framework

1. Beyond Basic Value Storage: Smart Variables

Building on the existing CRAFT variable implementation found in your framework files, let's enhance variables to become more intelligent:

def create_progressive_variable(base_name, stages, initial_value):
    """Creates a variable that can evolve through defined stages."""
    return {
        "name": base_name,
        "current_stage": 0,
        "stages": stages,
        "values": [initial_value] + [None] * (len(stages) - 1),
        "stage_triggers": {}
    }

def advance_variable(var_obj, new_value=None, trigger_condition=None):
    """Advances a progressive variable to its next stage."""
    if var_obj["current_stage"] < len(var_obj["stages"]) - 1:
        var_obj["current_stage"] += 1
        if new_value:
            var_obj["values"][var_obj["current_stage"]] = new_value
        if trigger_condition:
            var_obj["stage_triggers"][var_obj["current_stage"]] = trigger_condition
    return var_obj

Example usage for entrepreneurs:

python

BUSINESS_PLAN = create_progressive_variable(
    "BUSINESS_PLAN",
    ["outline", "draft", "financial_projections", "final"],
    "# Business Plan Outline\n\n- Executive Summary\n- Market Analysis\n- Product Description"
)

2. Context-Aware Variables

Variables that adapt their content or behavior based on the conversation stage or user needs:

def context_variable(name, contexts, values):
    """Create a variable that returns different values based on context."""
    return {
        "name": name,
        "contexts": contexts,
        "values": values,
        "get": lambda ctx: values[contexts.index(ctx)] if ctx in contexts else values[0]
    }

Example for entrepreneurs:

python

PITCH_TEMPLATE = context_variable(
    "PITCH_TEMPLATE",
    ["elevator", "investor", "customer"], 
    [
        "20-second hook focusing on problem/solution",
        "3-minute presentation with market size and financial projections",
        "Benefits-focused conversation starter with social proof"
    ]
)

# Usage: PITCH_TEMPLATE.get("investor")

3. Multi-Modal Variables

Variables that can handle different content types within a unified structure:

def multimodal_variable(name, content_map):
    """Creates a variable that can output different formats from the same content."""
    return {
        "name": name,
        "content": content_map,
        "get_format": lambda fmt: content_map.get(fmt, content_map["default"])
    }

Example for entrepreneurs:

python

PRODUCT_DESCRIPTION = multimodal_variable(
    "PRODUCT_DESCRIPTION",
    {
        "default": "AI-powered task management solution",
        "detailed": "TaskMaster Pro is an AI-powered productivity solution...",
        "bullet_points": ["AI prioritization", "Smart deadlines", "Team analytics"],
        "tweet": "TaskMaster Pro: Your AI assistant that knows exactly what you should be working on next! #productivity"
    }
)

4. Variable Templates

Pre-configured variable structures for common entrepreneurial tasks:

class VariableTemplate:
    @staticmethod
    def customer_persona(name, role, goals, pain_points, budget, tech_comfort):
        """Template for consistent customer personas."""
        persona_id = f"PERSONA_{name.upper().replace(' ', '_')}"
        return f"""
        {persona_id} = \"\"\"
        Name: {name}
        Role: {role}
        Primary Goals: {", ".join(goals)}
        Key Pain Points: {", ".join(pain_points)}
        Budget Range: {budget}
        Technology Comfort: {tech_comfort}
        \"\"\"
        """
        
    @staticmethod
    def market_analysis(industry, market_size, growth_rate, key_players, trends):
        """Template for market analysis variables."""
        industry_id = f"MARKET_{industry.upper().replace(' ', '_')}"
        return f"""
        {industry_id} = \"\"\"
        Industry: {industry}
        Total Market Size: {market_size}
        Annual Growth Rate: {growth_rate}
        Key Players: {", ".join(key_players)}
        Emerging Trends: {", ".join(trends)}
        \"\"\"
        """

5. Variable Namespaces

Building on the namespace concept mentioned in CFT-FWK-SPEC, let's expand this for entrepreneurial contexts:

class Namespace:
    def __init__(self, name):
        self.name = name
        self.variables = {}
        
    def add(self, var_name, value):
        self.variables[var_name] = value
        
    def get(self, var_name):
        return self.variables.get(var_name)
        
    def list_all(self):
        return list(self.variables.keys())

Example usage:

python

Marketing = Namespace("Marketing")
Marketing.add("TARGET_AUDIENCE", "Tech-savvy professionals aged 25-45")
Marketing.add("VALUE_PROP", "Save 3 hours daily with our AI assistant")
Marketing.add("CHANNELS", ["LinkedIn", "Product Hunt", "TechCrunch"])

Product = Namespace("Product")
Product.add("FEATURES", ["AI scheduling", "Smart notifications", "Team sync"])

6. Dynamic Variables

Variables that update themselves based on conversation progress:

def dynamic_variable(name, initial_value, update_function):
    """Creates a variable that can update itself based on a function."""
    return {
        "name": name,
        "value": initial_value,
        "update": update_function,
        "history": [initial_value],
        "get": lambda: dynamic_variable["value"],
        "refresh": lambda *args: dynamic_variable.update(*args)
    }

Example for entrepreneurs:

python

def update_progress(current_value, new_milestone):
    """Updates project progress by adding a milestone."""
    milestones = current_value.split("\n")
    milestones.append(f"- [✓] {new_milestone}")
    return "\n".join(milestones)

PROJECT_PROGRESS = dynamic_variable(
    "PROJECT_PROGRESS",
    "# Project Milestones\n- [✓] Initial concept defined",
    update_progress
)

# Later in conversation:
# PROJECT_PROGRESS.refresh("Market research completed")

7. Variable Inheritance

Borrowing concepts from OOP inheritance for CRAFT variables:

def extend_variable(parent_var, extensions):
    """Creates a new variable that inherits and extends a parent variable."""
    if isinstance(parent_var, str):
        new_var = parent_var + "\n\n" + extensions
    elif isinstance(parent_var, list):
        new_var = parent_var + extensions
    elif isinstance(parent_var, dict):
        new_var = {**parent_var, **extensions}
    else:
        new_var = extensions
        
    return new_var

Example for entrepreneurs:

python

BASE_EMAIL_TEMPLATE = """
Subject: {subject}
Dear {name},

Thank you for your interest in our services.

Best regards,
{sender_name}
"""

FOLLOW_UP_EMAIL = extend_variable(BASE_EMAIL_TEMPLATE, """
I wanted to check if you had any questions about the proposal I sent last week.

I'm available this week for a quick call if you'd like to discuss further.
""")

8. Time-Sensitive Variables

Variables with built-in expiration or evolution:

def timed_variable(name, initial_value, evolution_stages=None, expiration=None):
    """Creates a variable that evolves or expires over time/usage."""
    import time
    
    current_time = time.time()
    return {
        "name": name,
        "value": initial_value,
        "created_at": current_time,
        "expires_at": current_time + expiration if expiration else None,
        "stages": evolution_stages or [],
        "current_stage": 0,
        "uses": 0,
        "get": lambda: timed_variable["value"] if not timed_variable["is_expired"]() else "[EXPIRED]",
        "is_expired": lambda: timed_variable["expires_at"] and time.time() > timed_variable["expires_at"],
        "evolve": lambda: timed_variable._advance_stage() if timed_variable["stages"] else None,
        "_advance_stage": lambda: timed_variable._set_stage(min(timed_variable["current_stage"] + 1, len(timed_variable["stages"]) - 1)),
        "_set_stage": lambda s: timed_variable.update({"current_stage": s, "value": timed_variable["stages"][s]})
    }

Example for entrepreneurs:

# A special offer that changes over time
LAUNCH_OFFER = timed_variable(
    "LAUNCH_OFFER",
    "Early bird: 50% off for first 100 customers",
    evolution_stages=[
        "Early bird: 50% off for first 100 customers",
        "Launch week special: 30% off all packages",
        "Limited time offer: 15% off premium plans",
        "Standard pricing now in effect"
    ],
    expiration=60*60*24*30  # 30 days in seconds
)

Beyond Basic Value Storage: Smart Variables in CRAFT

Understanding Smart Variables

What Are Smart Variables?

In standard programming, variables act as simple containers:

python

user_name = "Alex"  # Basic variable storing a string

Smart variables, on the other hand, are sophisticated data structures with:

Multiple related values
Internal logic
State management capabilities
Conditional behavior
Evolution over time

Think of them as miniature state machines that not only contain data but also know how to process, transform, and adapt it based on context, triggers, or previous states.

Core Concepts Behind Smart Variables

Within the CRAFT framework, smart variables build upon two philosophical principles mentioned in your specification:

Structure Enables Freedom: By providing richer data structures, smart variables create more creative possibilities while maintaining a clear framework.
Code as Conversation: These variables make AI interactions more dynamic by enabling adaptive responses based on conversation state.

Technical Implementation

Smart variables in CRAFT are typically implemented as dictionaries or objects with specialized methods and properties:

python

def create_smart_variable(initial_value, metadata=None):
    """Creates a basic smart variable with state tracking."""
    return {
        "value": initial_value,
        "metadata": metadata or {},
        "history": [initial_value],
        "version": 1,
        "updated_at": "2025-05-11",  # Using today's date
        "get": lambda: smart_variable["value"],
        "set": lambda new_value: smart_variable.update({"value": new_value, "version": smart_variable["version"] + 1})
    }

Benefits of Smart Variables

1. State Persistence Across Conversation Turns

Benefit: Smart variables maintain their state and history throughout a conversation, allowing for more coherent and context-aware AI responses.

Example:

python

CUSTOMER_JOURNEY = {
    "value": "awareness",
    "stages": ["awareness", "consideration", "decision", "loyalty"],
    "current_index": 0,
    "advance": lambda: {
        "old": CUSTOMER_JOURNEY["value"],
        "new": CUSTOMER_JOURNEY["stages"][min(CUSTOMER_JOURNEY["current_index"] + 1, len(CUSTOMER_JOURNEY["stages"]) - 1)],
        "update": CUSTOMER_JOURNEY.update({
            "current_index": min(CUSTOMER_JOURNEY["current_index"] + 1, len(CUSTOMER_JOURNEY["stages"]) - 1),
            "value": CUSTOMER_JOURNEY["stages"][min(CUSTOMER_JOURNEY["current_index"] + 1, len(CUSTOMER_JOURNEY["stages"]) - 1)]
        })
    }
}

Business Application: When an entrepreneur is working with an AI to create marketing content, the CUSTOMER_JOURNEY variable tracks where potential customers are in the sales funnel. As the conversation progresses, the entrepreneur can advance the journey:

Human: "Let's create some awareness-stage content for social media."
AI: [Generates awareness content]
Human: "Great, now let's move to consideration stage."
AI: [Accesses CUSTOMER_JOURNEY.advance() and adapts its content approach]

This eliminates the need to repeatedly explain which stage of the funnel you're targeting in each prompt.

2. Conditional Logic Without Programming

Benefit: Entrepreneurs can include decision-making capabilities within variables without writing actual code, making AI interactions more dynamic.

Example:

python

PRICING_STRATEGY = {
    "base_price": 99.99,
    "discount_tiers": {
        "new_customer": 0.15,
        "returning_customer": 0.10,
        "bulk_purchase": 0.20,
        "seasonal_promotion": 0.25
    },
    "calculate": lambda customer_type=None, is_bulk=False, is_promotion=False: 
        PRICING_STRATEGY["base_price"] * (1 - sum([
            PRICING_STRATEGY["discount_tiers"]["new_customer"] if customer_type == "new" else 0,
            PRICING_STRATEGY["discount_tiers"]["returning_customer"] if customer_type == "returning" else 0,
            PRICING_STRATEGY["discount_tiers"]["bulk_purchase"] if is_bulk else 0,
            PRICING_STRATEGY["discount_tiers"]["seasonal_promotion"] if is_promotion else 0
        ]))
}

Business Application: An entrepreneur developing pricing strategies can use this variable to quickly calculate different price points without explaining the formula each time:

Human: "What's our price for a new customer during our summer promotion?"
AI: [Calculates PRICING_STRATEGY.calculate("new", False, True) = $59.99]
Human: "And for a returning customer making a bulk purchase?"
AI: [Calculates PRICING_STRATEGY.calculate("returning", True, False) = $69.99]

This approach saves significant tokens and mental overhead compared to explaining discount rules repeatedly.

3. Progressive Disclosure and Adaptive Detail

Benefit: Smart variables can reveal different levels of detail based on the user's needs or the conversation context.

Example:

python

BUSINESS_MODEL = {
    "summary": "SaaS subscription with tiered pricing",
    "detailed": {
        "revenue_streams": ["Monthly subscriptions", "Annual plans", "Enterprise contracts"],
        "cost_structure": ["Cloud hosting", "Development team", "Marketing", "Customer support"],
        "key_metrics": ["MRR", "CAC", "LTV", "Churn rate"]
    },
    "full_analysis": "Our SaaS business model operates on a freemium approach with three paid tiers...",
    "get_detail_level": lambda level="summary": 
        BUSINESS_MODEL["summary"] if level == "summary" else 
        BUSINESS_MODEL["detailed"] if level == "detailed" else 
        BUSINESS_MODEL["full_analysis"]
}

Business Application: When creating a business plan, the entrepreneur can access different detail levels without repeating information:

Human: "Give me a brief overview of our business model for the executive summary."
AI: [Uses BUSINESS_MODEL.get_detail_level("summary")]
Human: "Now let's expand on that for the financial projections section."
AI: [Uses BUSINESS_MODEL.get_detail_level("detailed")]
Human: "For the appendix, include the complete model analysis."
AI: [Uses BUSINESS_MODEL.get_detail_level("full_analysis")]

This approach maintains consistency while adapting to different document sections, reducing the risk of contradictory information.

4. Event-Triggered Behavior

Benefit: Smart variables can respond to specific triggers or events within the conversation, automatically updating their state.

Example:

python

PRODUCT_LAUNCH = {
    "status": "planning",
    "phases": ["planning", "development", "testing", "marketing", "launch", "post-launch"],
    "current_phase_index": 0,
    "completion": {
        "planning": 0.8,
        "development": 0.4,
        "testing": 0.0,
        "marketing": 0.0,
        "launch": 0.0,
        "post-launch": 0.0
    },
    "triggers": {
        "milestone_reached": lambda phase, percent: 
            PRODUCT_LAUNCH.update({
                "completion": {**PRODUCT_LAUNCH["completion"], phase: percent}
            }),
        "advance_phase": lambda: 
            PRODUCT_LAUNCH.update({
                "current_phase_index": min(PRODUCT_LAUNCH["current_phase_index"] + 1, len(PRODUCT_LAUNCH["phases"]) - 1),
                "status": PRODUCT_LAUNCH["phases"][min(PRODUCT_LAUNCH["current_phase_index"] + 1, len(PRODUCT_LAUNCH["phases"]) - 1)]
            })
    },
    "get_status_report": lambda: 
        f"Product launch is in {PRODUCT_LAUNCH['status']} phase ({PRODUCT_LAUNCH['completion'][PRODUCT_LAUNCH['status']] * 100}% complete)"
}

Business Application: For project management, the entrepreneur can trigger updates based on progress:

Human: "We've completed 80% of the planning phase for our product launch."
AI: [Executes PRODUCT_LAUNCH["triggers"]["milestone_reached"]("planning", 0.8)]
Human: "Now that planning is almost done, let's discuss development phase tasks."
AI: [Executes PRODUCT_LAUNCH["triggers"]["advance_phase"]() and adjusts conversation]
Human: "Give me our current status report."
AI: "Product launch is in development phase (40% complete)."

This creates a conversation that feels more like working with a proactive assistant rather than a passive responder.

5. Memory Optimization

Benefit: Smart variables reduce token usage by storing complex information in compact formats that expand only when needed.

Example:

python

MARKET_RESEARCH = {
    "summary": "Survey of 500 potential customers showing strong interest in our solution",
    "key_findings": [
        "87% experience the problem we solve at least weekly",
        "Price sensitivity is moderate, with $50-100 monthly being acceptable",
        "Feature X ranked highest in importance (4.8/5)",
        "Competitor awareness is low (35%)"
    ],
    "demographic_breakdown": {
        "age_groups": {"18-24": 0.15, "25-34": 0.42, "35-44": 0.28, "45+": 0.15},
        "industries": {"Technology": 0.35, "Healthcare": 0.22, "Education": 0.18, "Other": 0.25},
        "company_sizes": {"1-50": 0.40, "51-200": 0.30, "201-1000": 0.20, "1000+": 0.10}
    },
    "full_report_url": "https://company.sharepoint.com/sites/MarketResearch/2025Report.pdf",
    "get_section": lambda section="summary": MARKET_RESEARCH.get(section, "Section not found")
}

Business Application: When working on marketing materials, the entrepreneur can reference specific research without including all details in every prompt:

Human: "Let's create an email headline based on our key research findings."
AI: [References MARKET_RESEARCH["key_findings"] without needing the full research]
Human: "For the whitepaper, include our demographic breakdown."
AI: [Accesses MARKET_RESEARCH["demographic_breakdown"] specifically]

This approach might save hundreds or thousands of tokens compared to repeatedly pasting research data into prompts.

Advanced Examples and Applications

Example 1: Multi-Stage Sales Script Variable

python

SALES_SCRIPT = {
    "stages": ["introduction", "problem", "solution", "objection_handling", "close"],
    "current_stage": "introduction",
    "content": {
        "introduction": {
            "short": "Hi, I'm [name] from [company]. We help businesses like yours increase conversion rates.",
            "detailed": "Good [morning/afternoon], I'm [name] from [company]. We've been working with [similar businesses] to increase their conversion rates by an average of 27% in the last quarter.",
            "tips": ["Speak confidently", "Personalize with research", "Keep under 30 seconds"]
        },
        "problem": {
            "short": "Many businesses struggle with low conversion rates on their websites.",
            "detailed": "From our research, we've found that companies in your industry typically see conversion rates around 2.3%. This means 97.7% of your traffic is leaving without taking action.",
            "tips": ["Use specific numbers", "Relate to their business", "Ask if this resonates"]
        },
        # Additional stages...
    },
    "advance": lambda: SALES_SCRIPT.update({"current_stage": SALES_SCRIPT["stages"][min(SALES_SCRIPT["stages"].index(SALES_SCRIPT["current_stage"]) + 1, len(SALES_SCRIPT["stages"]) - 1)]}),
    "get_current": lambda detail_level="short": SALES_SCRIPT["content"][SALES_SCRIPT["current_stage"]][detail_level],
    "get_tips": lambda: SALES_SCRIPT["content"][SALES_SCRIPT["current_stage"]]["tips"]
}

Business Application: An entrepreneur developing a sales team training program can use this variable to create a cohesive, flexible script:

Human: "I need a sales script introduction for our new sales team."
AI: [Provides SALES_SCRIPT.get_current("detailed") for the introduction]
Human: "What are some delivery tips for this part?"
AI: [Lists SALES_SCRIPT.get_tips()]
Human: "Now let's move to discussing the problem."
AI: [Executes SALES_SCRIPT.advance() and provides the problem section]

This approach ensures consistency across all sales materials while allowing flexibility in how the script is assembled and presented.

Example 2: Adaptive Customer Persona

python

CUSTOMER_PERSONA = {
    "name": "Marketing Manager Melissa",
    "basics": {
        "role": "Marketing Manager",
        "company_size": "Mid-market (100-500 employees)",
        "industry": "B2B SaaS",
        "age_range": "30-45"
    },
    "pain_points": [
        "Limited resources for content creation",
        "Difficulty demonstrating ROI",
        "Managing multiple marketing channels efficiently",
        "Staying current with digital marketing trends"
    ],
    "goals": [
        "Increase qualified leads by 30%",
        "Reduce cost per acquisition",
        "Improve marketing team productivity",
        "Build stronger brand recognition"
    ],
    "objections": {
        "price": "I don't have budget for another tool right now.",
        "time": "My team doesn't have time to learn another system.",
        "necessity": "We're already using [competitor] for this.",
        "risk": "I'm not sure this will work for our specific needs."
    },
    "content_preferences": {
        "formats": ["Case studies", "How-to guides", "Webinars", "Templates"],
        "topics": ["Marketing automation", "ROI measurement", "Team productivity", "Content strategy"]
    },
    "buying_stage": "awareness",  # awareness, consideration, decision
    "advance_stage": lambda: CUSTOMER_PERSONA.update({
        "buying_stage": {
            "awareness": "consideration",
            "consideration": "decision",
            "decision": "decision"
        }[CUSTOMER_PERSONA["buying_stage"]]
    }),
    "get_relevant_content": lambda: {
        "awareness": CUSTOMER_PERSONA["content_preferences"]["formats"][0:2],
        "consideration": ["Comparison guides", "Demo videos"],
        "decision": ["Free trial offer", "Implementation roadmap", "ROI calculator"]
    }[CUSTOMER_PERSONA["buying_stage"]],
    "get_objection": lambda objection_type: CUSTOMER_PERSONA["objections"].get(objection_type, "No specific objection found")
}

Business Application: When working on marketing and sales strategy, the entrepreneur can reference persona details without repeating them:

Human: "Let's create content for Marketing Manager Melissa at the awareness stage."
AI: [Crafts content using CUSTOMER_PERSONA details and appropriate formats]
Human: "She's shown interest. How should our approach change if she moves to consideration stage?"
AI: [Uses CUSTOMER_PERSONA.advance_stage() and adapts content strategy]
Human: "What objection might she have about implementation time?"
AI: [Provides CUSTOMER_PERSONA.get_objection("time")]

The smart variable allows for dynamic adaptation to different marketing scenarios while maintaining consistency in how the persona is understood and addressed.

Example 3: Progressive Business Plan Builder

python

BUSINESS_PLAN = {
    "sections": {
        "executive_summary": {"status": "draft", "content": "Initial outline of key business points..."},
        "company_description": {"status": "incomplete", "content": ""},
        "market_analysis": {"status": "incomplete", "content": ""},
        "product_line": {"status": "incomplete", "content": ""},
        "marketing_strategy": {"status": "incomplete", "content": ""},
        "financial_projections": {"status": "incomplete", "content": ""}
    },
    "current_section": "executive_summary",
    "update_section": lambda section, content, status="draft": BUSINESS_PLAN["sections"].update({
        section: {"status": status, "content": content}
    }),
    "set_current_section": lambda section: BUSINESS_PLAN.update({"current_section": section}),
    "get_section": lambda section=None: BUSINESS_PLAN["sections"][section or BUSINESS_PLAN["current_section"]],
    "get_status": lambda: {
        "total_sections": len(BUSINESS_PLAN["sections"]),
        "completed": sum(1 for section in BUSINESS_PLAN["sections"].values() if section["status"] == "complete"),
        "in_progress": sum(1 for section in BUSINESS_PLAN["sections"].values() if section["status"] == "draft"),
        "not_started": sum(1 for section in BUSINESS_PLAN["sections"].values() if section["status"] == "incomplete")
    }
}

Business Application: When working with an AI on business plan development, this variable creates a persistent structure that evolves over multiple sessions:

Human: "Let's start working on the company description section of our business plan."
AI: [Uses BUSINESS_PLAN.set_current_section("company_description") and begins drafting]
Human: "Here's our company mission and vision to include."
AI: [Updates BUSINESS_PLAN.update_section("company_description", new_content, "draft")]
Human: "What's our overall progress on the business plan?"
AI: [Reports BUSINESS_PLAN.get_status(): "1 of 6 sections complete, 2 in progress, 3 not started"]

This approach allows for an evolving document that maintains state across multiple conversation sessions, providing the entrepreneur with a clear sense of progress and organization.

Implementation in CRAFT Framework

To integrate smart variables into your CRAFT framework, I recommend creating:

A standardized smart variable constructor function in the framework spec
Template libraries for common business scenarios in project-specific files
Documentation on extending and customizing smart variables

This approach would build on your existing CRAFT philosophy while adding powerful new capabilities that entrepreneurs would find immediately useful for complex projects.

Context-Aware Variables in CRAFT

Understanding Context-Aware Variables

What Are Context-Aware Variables?

The stage of conversation
The audience being addressed
The medium of communication
The user's expertise level
The specific task being performed
Time-based conditions

These variables act as chameleons in your AI prompts, automatically adjusting to provide the most relevant information for the current situation without requiring explicit instructions each time the context changes.

Core Concepts Behind Context-Aware Variables

In the CRAFT framework, context-aware variables align with two of your core philosophical principles:

Structure Enables Freedom: By encoding context-sensitivity directly into variables, you free yourself from constantly specifying how information should adapt to different situations.
Conservation of Tokens: Context-aware variables significantly reduce token usage by eliminating repetitive instructions about how to modify content for different contexts.

Technical Implementation

At their simplest, context-aware variables can be implemented as dictionaries with context keys and corresponding values:

python

def create_context_variable(name, context_map, default_context=None):
    """Creates a variable that returns different values based on context."""
    return {
        "name": name,
        "contexts": list(context_map.keys()),
        "values": context_map,
        "default_context": default_context or list(context_map.keys())[0],
        "get": lambda ctx=None: context_map.get(ctx or default_context or list(context_map.keys())[0])
    }

More sophisticated implementations might include:

Context detection logic
Fuzzy matching for similar contexts
Hierarchical context inheritance
Context transition rules

Benefits of Context-Aware Variables

1. Audience-Adaptive Communication

Benefit: Content automatically adjusts to the needs, knowledge level, and preferences of different audiences without requiring explicit instructions.

Example:

python

PRODUCT_EXPLANATION = create_context_variable(
    "PRODUCT_EXPLANATION",
    {
        "technical": "Our SaaS platform utilizes a microservices architecture with React frontend and Django REST API backend, featuring OAuth2 authentication and WebSocket connections for real-time data synchronization.",
        
        "executive": "Our software solution streamlines workflow management, reducing operational costs by an average of 23% while improving team productivity metrics by 37% in the first quarter of implementation.",
        
        "sales": "Our platform helps businesses like yours save time and money by automating repetitive tasks. Our average customer reports saving 15 hours per week per employee, with a typical ROI within the first 3 months.",
        
        "customer": "Our easy-to-use app helps you organize your work better. Just upload your tasks, and our smart system will help you prioritize and track everything in one place - no more forgotten deadlines or missed assignments!"
    }
)

Business Application: An entrepreneur preparing for different stakeholder meetings can use a single variable to maintain consistent product information while automatically adapting the presentation style:

Human: "I need to explain our product in our sales meeting tomorrow."
AI: [Uses PRODUCT_EXPLANATION.get("sales")]
Human: "Now I need to prepare how to describe it for our technical documentation."
AI: [Uses PRODUCT_EXPLANATION.get("technical")]
Human: "And finally, how should I describe it in our investor pitch deck?"
AI: [Uses PRODUCT_EXPLANATION.get("executive")]

This ensures the core product details remain consistent while the presentation adapts to each audience's specific needs and language preferences.

2. Communication Channel Optimization

Benefit: Content automatically formats itself appropriately for different media channels without repetitive formatting instructions.

Example:

python

PRODUCT_ANNOUNCEMENT = create_context_variable(
    "PRODUCT_ANNOUNCEMENT",
    {
        "email": {
            "subject": "Introducing TaskFlow Pro: Revolutionize Your Workflow",
            "greeting": "Hi [First Name],",
            "body": "We're excited to announce our new TaskFlow Pro platform...",
            "cta": "Start Your Free Trial",
            "signature": "The TaskFlow Team"
        },
        
        "twitter": "🚀 Just launched: TaskFlow Pro - manage projects 3x faster with AI-powered workflows. Try it free for 14 days! #ProductivityHack",
        
        "linkedin": "**Exciting Announcement**: Today we're launching TaskFlow Pro, our new AI-powered project management solution designed for growing teams. In beta testing, users reported saving an average of 12 hours per week on administrative tasks. Learn more and start your free trial at the link below.",
        
        "press_release": {
            "headline": "TaskFlow Inc. Launches Revolutionary AI-Powered Project Management Solution",
            "dateline": "SAN FRANCISCO, CA - May 11, 2025",
            "lead": "TaskFlow Inc. today announced the release of TaskFlow Pro, an innovative project management platform leveraging artificial intelligence to streamline team workflows...",
            "body": "...",
            "boilerplate": "About TaskFlow Inc.: Founded in 2023, TaskFlow is a leading provider of..."
        }
    }
)

Business Application: When launching a new product, an entrepreneur can maintain messaging consistency across multiple channels without reformatting for each one:

Human: "I need to announce our product launch on Twitter."
AI: [Provides PRODUCT_ANNOUNCEMENT.get("twitter")]
Human: "Now I need to draft the announcement email to our customer list."
AI: [Formats complete email using PRODUCT_ANNOUNCEMENT.get("email")]
Human: "Let's also prepare the LinkedIn post for our company page."
AI: [Provides professional LinkedIn format using PRODUCT_ANNOUNCEMENT.get("linkedin")]

This approach ensures brand consistency while optimizing for each platform's unique format and audience expectations, saving significant time in reformatting and reducing the risk of messaging inconsistencies.

3. Skill Level Adaptation

Benefit: Instructions and explanations automatically adjust to the user's skill level, providing appropriate detail and terminology.

Example:

python

API_DOCUMENTATION = create_context_variable(
    "API_DOCUMENTATION",
    {
        "beginner": {
            "overview": "Our API lets you connect your app to our service. Think of it as a bridge between different software systems.",
            "authentication": "First, you'll need an API key. This is like a special password that lets our system know who you are.",
            "example": "Here's a simple example using our starter code...",
            "troubleshooting": "If you see an error message, check that you've entered your API key correctly."
        },
        
        "intermediate": {
            "overview": "Our REST API provides programmatic access to our platform's core functionality through HTTP endpoints.",
            "authentication": "Authentication uses API keys passed in the request header. Keys are environment-specific (test/production).",
            "example": "This example demonstrates basic CRUD operations...",
            "troubleshooting": "Common status codes include 401 (authentication error), 403 (permissions error), and 429 (rate limit exceeded)."
        },
        
        "advanced": {
            "overview": "Our API follows RESTful principles with JSON:API specification compliance. We offer GraphQL endpoints for complex data queries.",
            "authentication": "We support OAuth2 with JWT tokens and API key authentication. For production environments, we recommend implementing certificate pinning.",
            "example": "This implementation showcases pagination handling, error retry logic, and optimistic updates...",
            "troubleshooting": "Implement idempotency keys for retry safety. Check our logging endpoints for detailed debugging information."
        }
    }
)

Business Application: When creating developer documentation, this approach ensures appropriate content for different user skill levels:

Human: "Let's create API documentation for beginners who are just getting started."
AI: [Uses API_DOCUMENTATION.get("beginner")]
Human: "Now let's add a section for our advanced users who need implementation details."
AI: [Uses API_DOCUMENTATION.get("advanced")]

This variable adapts the depth, terminology, and examples to match user expertise, improving learning outcomes without maintaining multiple separate documentation sets.

4. Workflow Stage Awareness

Benefit: Variables automatically present the appropriate information for the current stage of a process or workflow.

Example:

python

STARTUP_GUIDANCE = create_context_variable(
    "STARTUP_GUIDANCE",
    {
        "ideation": {
            "focus_areas": ["Problem validation", "Market research", "Unique value proposition"],
            "key_questions": ["What specific problem are you solving?", "Who experiences this problem most acutely?", "How are people currently solving this?"],
            "recommended_tools": ["Customer interviews", "Problem validation canvas", "Competitor analysis"],
            "common_pitfalls": ["Building a solution without a clear problem", "Insufficient market research", "Emotional attachment to ideas"]
        },
        
        "validation": {
            "focus_areas": ["MVP development", "Customer feedback", "Solution-market fit"],
            "key_questions": ["What is the simplest version of your product that provides value?", "How will you measure success?", "What price point allows for quick testing?"],
            "recommended_tools": ["Landing page tests", "Prototype testing", "Wizard of Oz MVP"],
            "common_pitfalls": ["Over-engineering the MVP", "Ignoring negative feedback", "Premature scaling"]
        },
        
        "launch": {
            "focus_areas": ["Go-to-market strategy", "Customer acquisition", "Onboarding optimization"],
            "key_questions": ["What channels reach your target customers cost-effectively?", "How will you convert initial interest to paid customers?", "What metrics indicate successful launch?"],
            "recommended_tools": ["Marketing automation", "Analytics setup", "Customer success playbooks"],
            "common_pitfalls": ["Unclear positioning", "Too many marketing channels", "Poor onboarding experience"]
        },
        
        "growth": {
            "focus_areas": ["Scaling operations", "Retention optimization", "Unit economics"],
            "key_questions": ["What is your current CAC and LTV?", "Which features drive retention?", "What processes need to be automated?"],
            "recommended_tools": ["CRM integration", "Cohort analysis", "Customer journey mapping"],
            "common_pitfalls": ["Premature hiring", "Neglecting existing customers", "Failing to optimize unit economics"]
        }
    }
)

Business Application: For entrepreneurs at different stages of their startup journey, this variable provides stage-appropriate guidance:

Human: "I'm in the ideation phase of my startup. What should I focus on?"
AI: [Provides STARTUP_GUIDANCE.get("ideation")["focus_areas"] and related information]
Human: "I've validated my idea and built an MVP. What are common pitfalls when launching?"
AI: [Provides STARTUP_GUIDANCE.get("launch")["common_pitfalls"]]

This approach delivers precisely the right guidance at each stage without the entrepreneur needing to specify which aspects are relevant to their current phase.

5. Time and Date Sensitivity

Benefit: Content automatically adapts based on temporal factors like time of day, day of week, or seasonal considerations.

Example:

python

MARKETING_CAMPAIGN = create_context_variable(
    "MARKETING_CAMPAIGN",
    {
        "black_friday": {
            "headline": "Our BIGGEST Sale of the Year: 50% OFF Everything",
            "urgency": "Limited Time: Black Friday Weekend Only!",
            "offer": "Half off all plans plus free onboarding ($599 value)",
            "cta": "Shop Black Friday Deals",
            "timing": "November 25-28, 2025"
        },
        
        "new_year": {
            "headline": "New Year, New Productivity: 30% Off Annual Plans",
            "urgency": "Resolution Special: Offer Ends January 15th",
            "offer": "30% discount when you purchase any annual plan",
            "cta": "Start Your Year Right",
            "timing": "January 1-15, 2025"
        },
        
        "summer": {
            "headline": "Summer Productivity Boost: 3 Months Free",
            "urgency": "Summer Special: Limited Availability",
            "offer": "Buy 9 months, get 3 free on any plan",
            "cta": "Claim Your Free Months",
            "timing": "June 1-30, 2025"
        },
        
        "standard": {
            "headline": "Streamline Your Workflow with Our Platform",
            "urgency": "Join thousands of satisfied customers",
            "offer": "7-day free trial, no credit card required",
            "cta": "Start Free Trial",
            "timing": "Ongoing"
        }
    }
)

Business Application: For seasonal marketing campaigns, this variable automatically provides the appropriate messaging for the current time period:

Human: "Let's prepare our November marketing emails."
AI: [Uses MARKETING_CAMPAIGN.get("black_friday") for seasonal relevance]
Human: "What should our website banner look like after the holiday season?"
AI: [Transitions to MARKETING_CAMPAIGN.get("new_year")]

This temporal awareness ensures marketing stays seasonally appropriate without explicit instructions, improving relevance and conversion rates.

Advanced Examples and Applications

Example 1: Multi-Dimensional Context Awareness

More sophisticated context-aware variables can respond to multiple dimensions of context simultaneously:

python

def create_multi_context_variable(name, dimensions, content_matrix):
    """Creates a variable that responds to multiple context dimensions."""
    return {
        "name": name,
        "dimensions": dimensions,
        "content": content_matrix,
        "get": lambda **kwargs: _navigate_matrix(content_matrix, kwargs),
    }

def _navigate_matrix(matrix, context_values):
    """Helper function to navigate a multi-dimensional context matrix."""
    if not isinstance(matrix, dict):
        return matrix
        
    for dimension, value in context_values.items():
        if value in matrix:
            return _navigate_matrix(matrix[value], {k: v for k, v in context_values.items() if k != dimension})
    
    # Return default if specific context not found
    return _navigate_matrix(matrix.get("default", "Context not available"), 
                          {k: v for k, v in context_values.items() if k != "default"})

Implementation Example:

python

PRODUCT_MESSAGING = create_multi_context_variable(
    "PRODUCT_MESSAGING",
    ["audience", "buying_stage", "pain_point"],
    {
        "ceo": {
            "awareness": {
                "time_management": "CEOs lose 38% of their strategic planning time to administrative overhead.",
                "team_performance": "The gap between your top and bottom-performing teams represents a 45% productivity difference.",
                "default": "Leadership effectiveness is directly correlated with organizational clarity."
            },
            "consideration": {
                "time_management": "Our platform reclaims 5+ hours of executive time weekly through AI-powered prioritization.",
                "default": "Our solution bridges the execution gap between strategy and implementation."
            },
            "default": "Transform organizational effectiveness with data-driven insights."
        },
        "manager": {
            "awareness": {
                "team_coordination": "Managers spend 65% of their time coordinating work rather than improving outcomes.",
                "default": "The typical manager loses 12 hours weekly to administrative tasks rather than team development."
            },
            "consideration": {
                "team_coordination": "Streamline team coordination with automated task allocation and progress tracking.",
                "reporting": "Generate comprehensive team performance reports in minutes instead of hours.",
                "default": "Cut administrative overhead by 62% while improving team visibility."
            },
            "default": "Transform team performance through streamlined workflows and actionable insights."
        },
        "default": "Improve organizational effectiveness with our productivity platform."
    }
)

Business Application: This multi-dimensional approach allows precisely targeted messaging based on multiple factors:

Human: "What message should we use for CEOs in the awareness stage who are concerned about time management?"
AI: [Returns PRODUCT_MESSAGING.get(audience="ceo", buying_stage="awareness", pain_point="time_management")]

Multi-Modal Variables in CRAFT

Understanding Multi-Modal Variables

What Are Multi-Modal Variables?

Multi-modal variables are advanced data structures in the CRAFT framework that can store and output content in multiple formats or modes from a single source of truth. Unlike traditional variables that contain a single value, multi-modal variables maintain different representations of the same underlying information, allowing you to:

Present the same core information in different media formats (text, lists, tables, etc.)
Adapt content for different presentation contexts while preserving consistency
Store related variations of content without duplicating the underlying information

Think of multi-modal variables as versatile containers that hold multiple expressions of the same core concept, ready to be accessed in whatever form best suits the current need.

Core Concepts Behind Multi-Modal Variables

Multi-modal variables align perfectly with two key philosophical principles of your CRAFT framework:

Structure Enables Freedom: By structuring content in different modes upfront, you gain the freedom to switch between presentations without reformulating.
Conservation of Tokens: Instead of generating new formats on demand (which consumes tokens), you can pre-define common formats and simply select the appropriate one.

Technical Implementation

At a basic level, multi-modal variables can be implemented as dictionary structures with format keys:

python

def create_multimodal_variable(name, content_modes):
    """Creates a variable that can output different formats of the same content."""
    return {
        "name": name,
        "modes": list(content_modes.keys()),
        "content": content_modes,
        "get": lambda mode=None: content_modes.get(mode or "default", content_modes.get("default")),
        "available_modes": lambda: list(content_modes.keys())
    }

More sophisticated implementations might include:

Transformation functions to convert between modes dynamically
Composition rules for combining modes
Mode validation for structural consistency
Default fallbacks when a requested mode isn't available

Benefits of Multi-Modal Variables

1. Format Flexibility Without Redundancy

Benefit: Maintain a single source of truth while accessing it in whatever presentation format best suits your current need.

Example:

python

BUSINESS_MODEL = create_multimodal_variable(
    "BUSINESS_MODEL",
    {
        "default": "SaaS subscription with tiered pricing and optional professional services",
        
        "paragraph": "Our business operates on a Software-as-a-Service (SaaS) model with three subscription tiers: Basic ($49/mo), Professional ($99/mo), and Enterprise ($249/mo). We supplement this core revenue with optional professional services including implementation support, custom integrations, and strategic consulting.",
        
        "bullet_points": [
            "Core offering: SaaS platform with tiered subscription model",
            "Pricing tiers: Basic ($49/mo), Professional ($99/mo), Enterprise ($249/mo)",
            "Secondary revenue: Professional services and implementation support",
            "Sales approach: Self-service for Basic, inside sales for Professional, account executives for Enterprise"
        ],
        
        "table": {
            "headers": ["Tier", "Price", "Target Customer", "Key Features"],
            "rows": [
                ["Basic", "$49/mo", "Solopreneurs, startups", "Core functionality, 5 users, email support"],
                ["Professional", "$99/mo", "SMBs, growing teams", "Advanced features, 20 users, priority support"],
                ["Enterprise", "$249/mo", "Large organizations", "Custom features, unlimited users, dedicated support"]
            ]
        },
        
        "diagram": "flowchart LR\n    A[Customer Need] --> B[Subscription Sign-up]\n    B --> C{Choose Tier}\n    C -->|Basic| D[Self-service Onboarding]\n    C -->|Professional| E[Guided Onboarding]\n    C -->|Enterprise| F[White-glove Implementation]\n    D --> G[Recurring Revenue]\n    E --> G\n    F --> G\n    F --> H[Professional Services]",
        
        "json": {
            "model_type": "SaaS",
            "primary_revenue": "subscriptions",
            "tiers": [
                {"name": "Basic", "price": 49, "user_limit": 5},
                {"name": "Professional", "price": 99, "user_limit": 20},
                {"name": "Enterprise", "price": 249, "user_limit": "unlimited"}
            ],
            "secondary_revenue": ["implementation", "training", "custom_development"]
        }
    }
)

Business Application: An entrepreneur creating various business documents can maintain consistent business model information across different formats:

Human: "I need to explain our business model in the executive summary of our business plan."
AI: [Uses BUSINESS_MODEL.get("paragraph") for narrative format]
Human: "For the investor pitch deck, I need a more visual representation."
AI: [Uses BUSINESS_MODEL.get("diagram") for a flowchart]
Human: "Let's create a pricing comparison table for our website."
AI: [Uses BUSINESS_MODEL.get("table") for structured pricing information]

This ensures the underlying business model remains consistent across all materials while adapting to the optimal presentation format for each context.

2. Content Reusability Across Mediums

Benefit: Define content once and reuse it across multiple communication channels, reducing inconsistencies and saving time.

Example:

python

PRODUCT_FEATURES = create_multimodal_variable(
    "PRODUCT_FEATURES",
    {
        "default": ["AI task prioritization", "Real-time collaboration", "Advanced analytics", "Integration ecosystem"],
        
        "website_copy": {
            "headline": "Powerful Features That Transform Your Workflow",
            "intro": "Our platform combines intelligent automation with human-centered design to deliver an unmatched productivity experience.",
            "features": [
                {
                    "title": "AI-Powered Task Prioritization",
                    "description": "Our intelligent algorithm learns your work patterns and automatically prioritizes tasks for maximum impact.",
                    "icon": "brain"
                },
                {
                    "title": "Real-Time Collaboration",
                    "description": "Work seamlessly with your team with instant updates, shared workspaces, and integrated communication.",
                    "icon": "users"
                },
                {
                    "title": "Advanced Analytics Dashboard",
                    "description": "Gain insights into productivity patterns, team performance, and project progress with customizable reports.",
                    "icon": "chart-line"
                },
                {
                    "title": "Extensive Integration Ecosystem",
                    "description": "Connect with 100+ popular tools including Slack, Gmail, Asana, and Notion for a unified workflow.",
                    "icon": "plug"
                }
            ]
        },
        
        "email_highlights": "• **AI Prioritization**: Never waste time figuring out what to work on next\n• **Real-Time Collaboration**: Stay in sync with your entire team\n• **Advanced Analytics**: Measure what matters with customizable dashboards\n• **Seamless Integrations**: Connect with all your favorite tools",
        
        "sales_script": {
            "intro": "Let me highlight the four key features that make our solution unique in the market.",
            "talking_points": [
                "Our AI task prioritization saves users an average of 5 hours per week by automatically organizing their workflow based on deadlines, importance, and work patterns.",
                "The real-time collaboration suite eliminates the need for status meetings by providing instant visibility into project progress and team activities.",
                "Our advanced analytics dashboard has been cited by 87% of customers as instrumental in identifying productivity bottlenecks and improving team performance.",
                "With over 100 integrations, our platform connects with your existing tech stack, creating a seamless workflow without disrupting your established processes."
            ],
            "addressing_objections": {
                "complexity": "While the platform is sophisticated, our intuitive design and personalized onboarding ensure a smooth learning curve.",
                "price": "When you factor in the time savings alone, most customers see ROI within the first 6 weeks."
            }
        },
        
        "api_endpoints": [
            {"path": "/api/v1/tasks/priorities", "method": "GET", "description": "Retrieve AI-prioritized task list"},
            {"path": "/api/v1/collaboration/workspaces", "method": "GET", "description": "List available collaboration workspaces"},
            {"path": "/api/v1/analytics/reports", "method": "GET", "description": "Generate custom analytics reports"},
            {"path": "/api/v1/integrations", "method": "GET", "description": "List available third-party integrations"}
        ]
    }
)

Business Application: When developing marketing materials across channels, the entrepreneur can maintain feature consistency:

Human: "We need to update our website product features section."
AI: [Uses PRODUCT_FEATURES.get("website_copy") for web-optimized presentation]
Human: "Let's draft an email announcing these features to our mailing list."
AI: [Incorporates PRODUCT_FEATURES.get("email_highlights") for email-friendly format]
Human: "Our sales team needs talking points about these features."
AI: [Provides PRODUCT_FEATURES.get("sales_script") with detailed value propositions]

This approach ensures brand and product messaging remains consistent while optimizing for each medium's unique requirements and audience expectations.

3. Technical-to-Business Translation

Benefit: Bridge the gap between technical details and business value by storing both perspectives of the same information.

Example:

python

SECURITY_FEATURES = create_multimodal_variable(
    "SECURITY_FEATURES",
    {
        "default": ["End-to-end encryption", "Two-factor authentication", "SOC 2 compliance", "Regular penetration testing"],
        
        "technical": {
            "encryption": {
                "method": "AES-256 encryption for data at rest; TLS 1.3 for data in transit",
                "implementation": "Zero-knowledge architecture with client-side encryption",
                "key_management": "HSM-backed key storage with regular rotation"
            },
            "authentication": {
                "methods": ["TOTP-based authenticator apps", "Security key (FIDO2/WebAuthn)", "SMS fallback"],
                "session_management": "Configurable timeout with automatic IP-change detection",
                "access_control": "Role-based access with principle of least privilege enforcement"
            },
            "compliance": {
                "certifications": ["SOC 2 Type II", "GDPR", "HIPAA BAA available"],
                "audit": "Continuous compliance monitoring with automated controls testing",
                "documentation": "Comprehensive security policies with regular review cycles"
            },
            "testing": {
                "frequency": "Quarterly third-party penetration testing",
                "methodology": "OWASP Top 10 + SANS Top 25 coverage",
                "remediation": "Critical findings addressed within 24 hours"
            }
        },
        
        "business_value": {
            "encryption": "Protect your sensitive company data with the same encryption technology used by leading financial institutions.",
            "authentication": "Prevent unauthorized access with multi-layered security that balances protection with ease of use.",
            "compliance": "Meet your industry's regulatory requirements while simplifying your audit processes.",
            "testing": "Rest easy knowing your data is protected by security measures that are continuously verified against emerging threats."
        },
        
        "one_pager": {
            "headline": "Enterprise-Grade Security Without Enterprise Complexity",
            "intro": "Your data's security is our top priority. Our comprehensive approach protects your information at every level.",
            "bullet_points": [
                "Bank-level encryption secures all your sensitive information",
                "Advanced authentication prevents unauthorized access",
                "Industry certifications demonstrate our commitment to security best practices",
                "Regular security testing ensures continuous protection against new threats"
            ],
            "customer_quote": "The robust security features gave our compliance team confidence to approve platform adoption across all departments. - CIO, Fortune 500 Company"
        },
        
        "faq": [
            {
                "question": "How do you protect my company's sensitive data?",
                "answer": "We implement end-to-end encryption using AES-256 and TLS 1.3 protocols, ensuring your data remains secure both in storage and during transmission."
            },
            {
                "question": "What security certifications does your platform have?",
                "answer": "We maintain SOC 2 Type II certification and are GDPR compliant. For healthcare organizations, we offer HIPAA Business Associate Agreements."
            },
            {
                "question": "How do you prevent unauthorized access to our account?",
                "answer": "We employ multi-factor authentication options including authenticator apps and security keys, along with role-based access controls to ensure only authorized personnel can access specific information."
            },
            {
                "question": "How often do you test your security measures?",
                "answer": "We conduct quarterly penetration tests by independent security firms and maintain a continuous security monitoring program to quickly identify and address potential vulnerabilities."
            }
        ]
    }
)

Business Application: This variable bridges the technical-business divide for security features:

Human: "Our development team needs details on our security implementation for documentation."
AI: [Provides SECURITY_FEATURES.get("technical") with implementation specifics]
Human: "For our sales materials, how should we explain the business value of these security features?"
AI: [Uses SECURITY_FEATURES.get("business_value") to translate technical features into benefits]
Human: "Let's create an FAQ section for our website about security."
AI: [Implements SECURITY_FEATURES.get("faq") for customer-friendly explanations]

This approach ensures technical accuracy while making complex features accessible to non-technical audiences, supporting both implementation and marketing needs.

4. Content Adaptation Without Rewriting

Benefit: Quickly adapt content to different formats without having to rewrite or reformulate information.

Example:

python

COMPANY_MISSION = create_multimodal_variable(
    "COMPANY_MISSION",
    {
        "default": "To empower organizations to achieve more through intelligent workflow automation",
        
        "full_statement": "Our mission is to empower organizations of all sizes to achieve more through intelligent workflow automation that adapts to their unique needs, allowing teams to focus on creative and strategic work while reducing administrative burden.",
        
        "values": [
            "Customer Success: We measure our success by our customers' success",
            "Continuous Innovation: We constantly push the boundaries of what's possible",
            "Operational Excellence: We strive for efficiency and quality in everything we do",
            "Inclusive Collaboration: We believe diverse perspectives lead to better solutions"
        ],
        
        "story": "TaskFlow began when our founders experienced firsthand the productivity drain of manual workflows at their previous companies. They envisioned a future where AI could eliminate repetitive tasks, freeing humans to focus on creative and strategic work. This vision of human-centered automation continues to drive everything we do.",
        
        "tagline": "Intelligent workflows for human-centered productivity",
        
        "founder_quote": "We don't just want to make work easier—we want to make it more meaningful by removing the robotic tasks that drain creative energy and strategic thinking. - Maria Chen, Co-founder & CEO",
        
        "video_script": {
            "opening": "Every day, knowledge workers waste hours on tasks that could be automated.",
            "problem": "This administrative overhead costs organizations billions in lost productivity and leads to employee burnout.",
            "solution": "At TaskFlow, we're on a mission to change that through intelligent workflow automation.",
            "impact": "By empowering teams to focus on high-value work, we're not just improving efficiency—we're transforming what's possible.",
            "closing": "Join us in building a future where technology handles the routine, and humans focus on what matters most."
        }
    }
)

Business Application: An entrepreneur creating various company materials can maintain mission consistency across formats:

Human: "I need our mission statement for the about page of our website."
AI: [Provides COMPANY_MISSION.get("full_statement")]
Human: "What should I say in my 30-second pitch at the networking event?"
AI: [Uses elements from COMPANY_MISSION.get("tagline") and COMPANY_MISSION.get("story")]
Human: "We're creating a company culture video. What should the script emphasize?"
AI: [Provides COMPANY_MISSION.get("video_script")]

This approach ensures consistent messaging about company purpose while adapting to the constraints and opportunities of each format.

5. Data-Driven Decision Support

Benefit: Present the same data in different analytical frames to support various decision-making processes.

Example:

python

MARKET_ANALYSIS = create_multimodal_variable(
    "MARKET_ANALYSIS",
    {
        "default": "Analysis of the B2B productivity software market for Q2 2025",
        
        "executive_summary": "The B2B productivity software market continues to show strong growth at 18% YoY, with AI-enhanced solutions capturing increasing market share (now 37%). Our competitive position remains strong in the mid-market segment, though increasing competition in the enterprise space requires attention to our feature differentiation and sales approach.",
        
        "data_table": {
            "headers": ["Metric", "Q2 2024", "Q2 2025", "YoY Change", "Industry Average"],
            "rows": [
                ["Total Market Size", "$24.8B", "$29.3B", "+18%", "N/A"],
                ["AI-Enhanced Share", "29%", "37%", "+8pts", "N/A"],
                ["Our Market Share", "3.2%", "3.8%", "+0.6pts", "N/A"],
                ["Customer Acquisition Cost", "$1,250", "$1,320", "+5.6%", "+12%"],
                ["Average Contract Value", "$15,200", "$16,700", "+9.9%", "+7%"],
                ["Churn Rate", "5.8%", "5.2%", "-0.6pts", "6.7%"]
            ]
        },
        
        "charts": {
            "market_growth": "Line chart showing market size growth from $14.2B in Q2 2022 to $29.3B in Q2 2025",
            "segment_analysis": "Bar chart comparing our performance across SMB (strong), Mid-market (dominant), and Enterprise (emerging) segments",
            "competitive_landscape": "Scatter plot positioning major competitors by feature richness (x-axis) and price (y-axis)",
            "regional_performance": "Heat map showing our market penetration across geographic regions"
        },
        
        "swot": {
            "strengths": [
                "Strong position in mid-market segment (+24% YoY growth)",
                "Industry-leading customer satisfaction (NPS 72 vs. industry avg 45)",
                "Unique AI capabilities in workflow automation"
            ],
            "weaknesses": [
                "Limited enterprise customer base (<5% of revenue)",
                "Lower brand recognition vs. established competitors",
                "Gaps in advanced collaboration features"
            ],
            "opportunities": [
                "Growing demand for AI-enhanced solutions (+37% YoY)",
                "Expansion into adjacent verticals (healthcare showing 42% growth)",
                "Strategic partnerships with complementary platforms"
            ],
            "threats": [
                "Increasing competition from well-funded startups",
                "Enterprise vendors moving downmarket",
                "Potential economic slowdown affecting software budgets"
            ]
        },
        
        "recommendations": [
            "Accelerate AI feature development to maintain technological advantage (+$1.2M in Q3 R&D)",
            "Enhance enterprise sales team with industry-specific experts (5 new hires in Q3)",
            "Develop strategic partnership program targeting complementary solutions",
            "Invest in brand awareness campaign focused on thought leadership (+$800K in Q3-Q4)"
        ]
    }
)

Business Application: When preparing for various business planning meetings, different perspectives on the same market data can be accessed:

Human: "I need a quick overview of our market position for the board meeting."
AI: [Provides MARKET_ANALYSIS.get("executive_summary")]
Human: "For the product planning session, let's look at our SWOT analysis."
AI: [Presents MARKET_ANALYSIS.get("swot")]
Human: "The marketing team needs the data on segment performance."
AI: [Describes MARKET_ANALYSIS.get("charts")["segment_analysis"]]

This approach provides consistent market insights while presenting the information in the most relevant analytical framework for each audience and decision context.

Advanced Examples and Applications

Example 1: Progressive Content Scaling

A more sophisticated multi-modal variable could offer content that scales based on available space or time constraints:

python

PITCH_DECK = create_multimodal_variable(
    "PITCH_DECK",
    {
        "elevator_30sec": {
            "hook": "We help companies recover thousands of wasted hours with AI-powered workflow automation.",
            "problem": "Teams waste 40% of their time on manual, repetitive tasks.",
            "solution": "Our platform automates these workflows, freeing people to focus on creative work.",
            "ask": "We're raising our Series A to scale our go-to-market strategy."
        },
        
        "quick_pitch_2min": {
            "hook": "We help companies recover thousands of wasted hours with AI-powered workflow automation.",
            "problem": {
                "statement": "Teams waste 40% of their time on manual, repetitive tasks.",
                "evidence": "Our research across 500+ companies shows an average of 16 hours per employee per week spent on administrative overhead.",
                "impact": "This represents over $525B in lost productivity annually in the US alone."
            },
            "solution": {
                "approach": "Our platform uses AI to learn workflow patterns and automate repetitive processes.",
                "differentiation": "Unlike other tools that require extensive configuration, our system learns by observation.",
                "results": "Early customers report 60%+ reduction in administrative tasks within 4 weeks."
            },
            "business_model": "SaaS subscription with tiered pricing based on user count and automation complexity.",
            "traction": "Currently at $2.4M ARR with 112% YoY growth and 93% retention rate.",
            "ask": "Raising $8M Series A to expand our sales team and accelerate our product roadmap."
        },
        
        "standard_pitch_10min": {
            "intro": {
                "team": ["Maria Chen (CEO): Previously VP Product at TaskStar, scaled to $50M ARR", 
                         "James Wilson (CTO): Former AI Lead at Google, 12 patents in ML",
                         "Sophie Kim (CPO): Built Workflow.io, acquired by Microsoft in 2022"],
                "vision": "A future where AI handles routine work, allowing humans to focus on creative and strategic pursuits."
            },
            "problem": {
                "statement": "Knowledge workers spend 40% of their time on administrative tasks.",
                "market_research": "Based on our study of 500+ companies across industries.",
                "customer_quotes": ["We were drowning in repetitive tasks that added no real value." - COO, TechCorp],
                "cost_analysis": "$525B+ in annual productivity loss in US alone (BCG research)",
                "existing_solutions": "Current tools require extensive manual configuration and don't adapt to changing workflows."
            },
            "solution": {
                "core_technology": "Proprietary AI system that learns workflow patterns through observation",
                "key_features": ["Automatic task detection and prioritization", 
                                 "Predictive workflow automation",
                                 "Adaptive learning from user behavior",
                                 "No-code automation builder"],
                "demo_highlights": "Live system demonstration showing email processing automation and meeting scheduling",
                "technology_advantage": "Self-improving algorithms that increase automation coverage by 5% monthly"
            },
            "market": {
                "size": "TAM: $84B, SAM: $28B, SOM: $2.2B",
                "growth": "16% CAGR for productivity software, 37% for AI-enhanced solutions",
                "target_segments": "Initial focus on mid-market professional services and technology companies",
                "expansion_strategy": "Land-and-expand model with department entry leading to organization-wide adoption"
            },
            "business_model": {
                "pricing": "Three tiers: Professional ($39/user/mo), Business ($79/user/mo), Enterprise (custom)",
                "unit_economics": "CAC: $2,200, LTV: $18,500, Payback period: 11 months",
                "growth_levers": "Expansion revenue (72% net revenue retention) and strategic partnerships"
            },
            "traction": {
                "customers": "187 paying customers including 3 Fortune 500",
                "revenue": "$2.4M ARR, 112% YoY growth",
                "retention": "93% logo retention, 117% net dollar retention",
                "testimonials": ["TaskFlow has transformed how we operate, saving each team member about 12 hours weekly." - CIO, GlobalServices Inc.]
            },
            "roadmap": {
                "product": ["Advanced analytics dashboard (Q3 2025)", "Cross-application workflow automation (Q4 2025)", "Third-party developer API (Q1 2026)"],
                "go_to_market": ["Expand sales team from 8 to 24 (next 12 months)", "Launch partner program (Q3 2025)", "International expansion starting with EU (Q1 2026)"]
            },
            "financials": {
                "historical": "2023: $0.8M ARR, 2024: $1.8M ARR, 2025 YTD: $2.4M ARR",
                "projections": "2025: $5.2M ARR, 2026: $13.5M ARR, 2027: $32M ARR",
                "key_metrics": "Currently operating at 68% gross margin, targeting 75% at scale"
            },
            "fundraising": {
                "current_round": "Series A: $8M",
                "use_of_funds": ["Sales team expansion (45%)", "Product development (30%)", "Marketing (15%)", "Operations (10%)"],
                "previous_funding": "Seed: $2.2M in 2023 led by Acme Ventures",
                "ideal_partners": "Looking for investors with B2B SaaS expertise and relevant industry connections"
            }
        },
        
        "one_pager": {
            "company": "TaskFlow AI, Inc.",
            "founded": "2023",
            "location": "San Francisco, CA",
            "employees": "42",
            "funding_to_date": "$2.2M Seed",
            "current_raise": "$8M Series A",
            "one_liner": "AI-powered workflow automation that learns and adapts to your team's unique processes",
            "problem": "Knowledge workers waste 40% of their time on administrative tasks that could be automated.",
            "solution": "TaskFlow's AI observes workflows and automatically creates automation rules without complex configuration.",
            "product": "SaaS platform with AI task detection, predictive automation, and no-code builder",
            "traction": "$2.4M ARR, 112% YoY growth, 187 customers, 93% retention",
            "team": "Ex-Google AI leadership and B2B SaaS veterans with previous successful exits",
            "contact": "maria@taskflow.ai"
        }
    }
)

Business Application: When preparing for investor meetings of varying lengths, this variable provides appropriately scaled content:

Human: "I have a chance elevator pitch with an investor. What should I say in 30 seconds?"
AI: [Provides PITCH_DECK.get("elevator_30sec")]
Human: "I secured a 10-minute slot at the pitch competition. What should I cover?"
AI: [Provides PITCH_DECK.get("standard_pitch_10min")]
Human: "An investor requested a one-pager instead. What should that include?"
AI: [Provides PITCH_DECK.get("one_pager")]

This approach ensures the entrepreneur communicates the same core message with appropriate depth for each time constraint, without having to reformulate their pitch for each scenario.

Example 2: Multi-Modal Content with Nested Variables

For more complex scenarios, multi-modal variables can be nested to create sophisticated content structures:

python

# Define nested component variables first
PRODUCT_BENEFITS = create_multimodal_variable(
    "PRODUCT_BENEFITS",
    {
        "time_savings": {
            "headline": "Save 12+ Hours Per Week",
            "description": "Eliminate repetitive tasks and administrative overhead through intelligent automation.",
            "statistic": "Our customers report an average time savings of 12.4 hours per team member weekly.",
            "testimonial": "TaskFlow freed up almost a quarter of my workweek that I was spending on repetitive tasks. - Marketing Director"
        },
        "team_alignment": {
            "headline": "Keep Everyone In Sync",
            "description": "Ensure your entire team works from the same information with real-time updates and transparent workflows.",
            "statistic": "Teams using TaskFlow report 64% fewer misalignment issues and 28% fewer status meetings.",
            "testimonial": "We've practically eliminated 'I thought someone else was doing that' from our vocabulary. - Project Manager"
        },
        "data_driven": {
            "headline": "Make Decisions With Confidence",
            "description": "Gain insights into productivity patterns, bottlenecks, and team performance with customizable analytics.",
            "statistic": "87% of customers report that TaskFlow analytics have helped identify and resolve productivity bottlenecks.",
            "testimonial": "For the first time, we can see exactly where our time is going and make data-backed decisions about processes. - Operations Lead"
        }
    }
)

# Main multi-modal variable that references the nested one
LANDING_PAGE = create_multimodal_variable(
    "LANDING_PAGE",
    {
        "hero": {
            "headline": "Work Smarter, Not Harder",
            "subheadline": "TaskFlow's AI-powered platform eliminates busywork so your team can focus on what matters.",
            "cta": "Start Free Trial",
            "secondary_cta": "Watch Demo",
            "image": "hero-productivity-visualization.jpg"
        },
        
        "benefits_section": {
            "headline": "How TaskFlow Transforms Your Workflow",
            "benefits": PRODUCT_BENEFITS,
            "format": "3-column layout with icons"
        },
        
        "how_it_works": {
            "headline": "Intelligent Automation That Learns Your Workflow",
            "steps": [
                {
                    "number": "01",
                    "title": "Connect Your Tools",
                    "description": "Integrate with your existing software stack in minutes with our one-click connectors."
                },
                {
                    "number": "02",
                    "title": "AI Analyzes Your Workflow",
                    "description": "Our system observes how your team works and identifies patterns and repetitive tasks."
                },
                {
                    "number": "03", 
                    "title": "Automate and Optimize",
                    "description": "TaskFlow suggests and implements automations, continuously improving as you work."
                }
            ],
            "video": "workflow-analysis-demo.mp4"
        },
        
        "testimonials": {
            "headline": "Trusted by Innovative Teams",
            "featured": {
                "quote": "TaskFlow has transformed how we operate. Each team member saves about 12 hours weekly on administrative tasks.",
                "author": "Sarah Johnson",
                "title": "CIO, GlobalServices Inc.",
                "image": "testimonial-sarah.jpg"
            },
            "logos": ["adobe.svg", "shopify.svg", "atlassian.svg", "salesforce.svg", "slack.svg"]
        },
        
        "pricing": {
            "headline": "Simple, Transparent Pricing",
            "plans": [
                {
                    "name": "Professional",
                    "price": "$39",
                    "unit": "per user/month",
                    "best_for": "Small teams and startups",
                    "features": ["Core automation features", "5 integration connections", "Basic analytics", "Email support"]
                },
                {
                    "name": "Business",
                    "price": "$79",
                    "unit": "per user/month",
                    "best_for": "Growing companies",
                    "features": ["Advanced automation workflows", "Unlimited integrations", "Team analytics dashboard", "Priority support"]
                },
                {
                    "name": "Enterprise",
                    "price": "Custom",
                    "unit": "pricing",
                    "best_for": "Large organizations",
                    "features": ["Custom workflow development", "API access", "Dedicated success manager", "SSO and advanced security"]
                }
            ],
            "cta": "Start Free 14-Day Trial"
        },
        
        "mobile_view": {
            "simplify": True,
            "stack_sections": True,
            "condense_features": True,
            "show_menu_button": True
        }
    }
)

Business Application: This nested structure enables precise content management for website development:

Human: "We need content for the benefits section of our landing page."
AI: [Accesses LANDING_PAGE.get("benefits_section") which references PRODUCT_BENEFITS]
Human: "For mobile optimization, what changes should we make to the page structure?"
AI: [Provides LANDING_PAGE.get("mobile_view") configuration]

This approach allows for modular content management where components can be reused across different sections or pages while maintaining a coherent overall structure.

Implementation in CRAFT Framework

To integrate multi-modal variables into your CRAFT framework effectively, consider:

Creating a multi-modal variable base class that includes standard methods for accessing different modes
Developing transformation functions that can convert content between modes when a specific mode isn't available
Adding validation logic to ensure modes maintain structural consistency
Implementing mode inheritance where modes can extend or override other modes

This approach would enhance the CRAFT framework's ability to manage complex content across multiple presentation contexts, aligning perfectly with your goal of deeper, more intuitive interactions with AI chat tools.

Variable Templates in CRAFT

Understanding Variable Templates

What Are Variable Templates?

Unlike basic variables that require manual definition each time, variable templates provide:

Standardized formats for frequently used variable types
Parameter-driven generation of complex variable structures
Consistent naming conventions and organizational patterns
Built-in validation and default values
Domain-specific structures aligned with common business needs

Think of variable templates as specialized "forms" that ensure variables for similar purposes have consistent structure, making them more predictable and interoperable within your CRAFT framework.

Core Concepts Behind Variable Templates

Variable templates align perfectly with three key philosophical principles of your CRAFT framework:

Structure Enables Freedom: By providing standardized templates, you eliminate the cognitive overhead of designing variable structures repeatedly.
Accessibility Through Familiarity: Templates create predictable patterns that become familiar, making the system more approachable.
Conservation of Tokens: Templates reduce tokens used in defining variable structures, focusing tokens on the unique content instead.

Technical Implementation

At a basic level, variable templates can be implemented as functions that generate structured variables:

python

def create_variable_template(template_name, structure_generator):
    """Creates a reusable template for generating variables with consistent structure."""
    def template_function(*args, **kwargs):
        return structure_generator(*args, **kwargs)
    
    template_function.__name__ = template_name
    return template_function

More sophisticated implementations might include:

Parameter validation and type checking
Default values for optional parameters
Nested template composition
Inheritance between templates
Domain-specific validation rules

Benefits of Variable Templates

1. Consistency Across Projects and Team Members

Benefit: Ensure all variables follow the same structure and naming conventions, even when created by different team members or across multiple projects.

Example:

python

# Define a template for consistent customer persona creation
customer_persona_template = create_variable_template(
    "customer_persona_template",
    lambda name, role, goals, pain_points, budget, tech_comfort, buying_stage="awareness":
    {
        "variable_name": f"PERSONA_{name.upper().replace(' ', '_')}",
        "content": {
            "name": name,
            "role": role,
            "goals": goals if isinstance(goals, list) else [goals],
            "pain_points": pain_points if isinstance(pain_points, list) else [pain_points],
            "budget_range": budget,
            "tech_comfort": tech_comfort,
            "buying_stage": buying_stage
        }
    }
)

# Using the template to create consistent personas
PERSONA_MARKETING_MARIA = customer_persona_template(
    "Marketing Maria",
    "Marketing Director",
    ["Increase brand visibility", "Improve campaign ROI", "Streamline content creation"],
    ["Limited resources", "Difficulty proving ROI", "Content bottlenecks"],
    "$10K-25K monthly",
    "High - early technology adopter"
)

PERSONA_FINANCE_FRANK = customer_persona_template(
    "Finance Frank",
    "CFO",
    ["Reduce operational costs", "Improve financial forecasting accuracy"],
    ["Disconnected financial data", "Manual reporting processes"],
    "$50K-100K annual",
    "Medium - comfortable with established technologies",
    "consideration"
)

Business Application: When entrepreneurs are developing marketing strategies, consistent persona structures enable reliable segmentation and targeting:

Human: "Let's create a tailored email campaign for Marketing Maria persona."
AI: [Accesses PERSONA_MARKETING_MARIA with confidence in its structure]
Human: "Now let's adjust that campaign for Finance Frank."
AI: [Uses PERSONA_FINANCE_FRANK knowing its structure matches Maria's]

This consistency ensures that when the team expands to include additional members, all personas will follow the same structure, making them interoperable across marketing materials.

2. Rapid Creation of Complex Variables

Benefit: Quickly create sophisticated variable structures without repetitive definition of common elements.

Example:

# Define a template for product feature variables with multiple presentation formats
product_feature_template = create_variable_template(
    "product_feature_template",
    lambda name, key_benefit, technical_description, user_story, icon=None, priority="medium":
    {
        "variable_name": f"FEATURE_{name.upper().replace(' ', '_')}",
        "content": {
            "name": name,
            "key_benefit": key_benefit,
            "technical_description": technical_description,
            "user_story": user_story,
            "icon": icon,
            "priority": priority,
            "formats": {
                "website": f"<strong>{name}</strong>: {key_benefit}",
                "sales_deck": f"{name}\n• {key_benefit}\n• {user_story.split('.')[0]}.",
                "technical_docs": f"## {name}\n\n{technical_description}\n\n**Primary Benefit**: {key_benefit}",
                "app_interface": {"title": name, "description": key_benefit, "icon": icon}
            }
        }
    }
)

# Create a series of feature variables quickly
FEATURE_AI_PRIORITIZATION = product_feature_template(
    "AI Task Prioritization",
    "Automatically organize tasks based on deadlines, importance, and work patterns",
    "Neural network algorithm learns from user behavior and applies reinforcement learning to optimize task sequencing",
    "Jane saved 5 hours weekly when the system started automatically highlighting her most important tasks each morning.",
    "brain-circuit",
    "high"
)

FEATURE_REAL_TIME_COLLAB = product_feature_template(
    "Real-Time Collaboration",
    "Work seamlessly with your team with instant updates and shared workspaces",
    "WebSocket protocol enables synchronous document editing with conflict resolution",
    "The marketing team eliminated version control issues when everyone could simultaneously edit the campaign brief.",
    "users",
    "high"
)

FEATURE_DATA_VISUALIZATION = product_feature_template(
    "Advanced Analytics Dashboard",
    "Gain insights into productivity patterns and team performance",
    "D3.js visualization library with custom charting components and real-time data processing",
    "The project manager identified workflow bottlenecks by viewing the team productivity dashboard.",
    "chart-line",
    "medium"
)

Business Application: When developing product marketing materials, this template approach drastically accelerates content creation:

Human: "I need to add our top three features to the website."
AI: [Extracts website format from each feature: FEATURE_AI_PRIORITIZATION.content.formats.website, etc.]
Human: "Now let's prepare these features for the sales presentation."
AI: [Switches to sales_deck format from the same feature variables]
Human: "And we need technical documentation for the developer portal."
AI: [Uses technical_docs format without needing to recreate the content]

This approach allows entrepreneurs to rapidly develop consistent marketing and documentation across channels without duplicating effort.

3. Built-In Best Practices and Structure

Benefit: Embed domain expertise and best practices directly into the variable structure, ensuring quality and completeness.

Example:

python

# Define a template for marketing campaigns that enforces best practices
marketing_campaign_template = create_variable_template(
    "marketing_campaign_template",
    lambda name, primary_goal, target_audience, messaging, channels, budget, start_date, end_date, success_metrics=None:
    {
        "variable_name": f"CAMPAIGN_{name.upper().replace(' ', '_')}",
        "content": {
            "name": name,
            "primary_goal": primary_goal,
            "secondary_goals": [],
            "target_audience": target_audience if isinstance(target_audience, list) else [target_audience],
            "key_messaging": {
                "headline": messaging.get("headline", ""),
                "primary_message": messaging.get("primary_message", ""),
                "supporting_points": messaging.get("supporting_points", []),
                "call_to_action": messaging.get("call_to_action", "")
            },
            "channels": channels if isinstance(channels, list) else [channels],
            "budget": {
                "total": budget,
                "allocation": {},
                "tracking_code": f"UTM_{name.lower().replace(' ', '_')}"
            },
            "timeline": {
                "planning_phase": {
                    "start": (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=30)).strftime("%Y-%m-%d"),
                    "end": (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).strftime("%Y-%m-%d"),
                    "key_milestones": ["Creative development", "Channel setup", "Audience targeting"]
                },
                "active_phase": {
                    "start": start_date,
                    "end": end_date,
                    "key_milestones": ["Launch", "Mid-campaign optimization", "Final push"]
                },
                "evaluation_phase": {
                    "start": (datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1)).strftime("%Y-%m-%d"),
                    "end": (datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=14)).strftime("%Y-%m-%d"),
                    "key_milestones": ["Initial results analysis", "Full campaign report", "Lessons learned documentation"]
                }
            },
            "success_metrics": success_metrics or {
                "primary": {"metric": "Conversion rate", "target": "To be defined"},
                "secondary": []
            },
            "status": "Planning",
            "results": {},
            "learnings": []
        }
    }
)

# Create a campaign using the template
CAMPAIGN_SUMMER_PROMO = marketing_campaign_template(
    "Summer Productivity Boost",
    "Generate 200 qualified leads",
    ["Marketing Directors", "Operations Managers"],
    {
        "headline": "Boost Your Summer Productivity by 30%",
        "primary_message": "Don't let summer slowdowns affect your team's productivity",
        "supporting_points": [
            "Automated workflows keep projects moving even during vacation season",
            "Real-time visibility helps manage distributed summer teams",
            "Set up now to see results before fall planning season"
        ],
        "call_to_action": "Start Free Trial"
    },
    ["LinkedIn", "Email", "Google Search"],
    "$25,000",
    "2025-06-01",
    "2025-06-30",
    {
        "primary": {"metric": "Qualified leads", "target": "200"},
        "secondary": [
            {"metric": "Email open rate", "target": "25%"},
            {"metric": "LinkedIn CTR", "target": "2.5%"},
            {"metric": "Trial sign-ups", "target": "50"}
        ]
    }
)

Business Application: This template enforces marketing best practices by ensuring all campaigns include necessary components:

Human: "Let's plan our summer promotion campaign."
AI: [Helps create CAMPAIGN_SUMMER_PROMO using the template]
Human: "I forgot to add success metrics. What should we track?"
AI: [Notes that the template already includes success_metrics structure and suggests appropriate metrics]
Human: "When should we start planning activities for this campaign?"
AI: [References the auto-calculated planning_phase dates in CAMPAIGN_SUMMER_PROMO.content.timeline]

The template automatically includes often-forgotten elements like success metrics, tracking codes, and a complete campaign timeline with planning and evaluation phases, ensuring comprehensive campaign planning.

4. Reduced Cognitive Load and Error Prevention

Benefit: Eliminate mental overhead and errors by automating the creation of properly structured variables.

Example:

python

# Define a template for financial models with built-in validation
financial_projection_template = create_variable_template(
    "financial_projection_template",
    lambda name, starting_revenue, growth_rate, cogs_percentage, opex, time_periods=12, period_type="month":
    {
        "variable_name": f"FINANCIAL_MODEL_{name.upper().replace(' ', '_')}",
        "content": {
            "name": name,
            "assumptions": {
                "starting_monthly_revenue": starting_revenue,
                "monthly_growth_rate": min(max(growth_rate, 0.0), 1.0),  # Validate between 0-100%
                "cogs_percentage": min(max(cogs_percentage, 0.0), 1.0),  # Validate between 0-100%
                "operating_expenses": opex
            },
            "periods": time_periods,
            "period_type": period_type,
            "calculations": {
                "revenue": [starting_revenue * (1 + growth_rate) ** i for i in range(time_periods)],
                "cogs": [starting_revenue * (1 + growth_rate) ** i * cogs_percentage for i in range(time_periods)],
                "gross_profit": [
                    (starting_revenue * (1 + growth_rate) ** i) - 
                    (starting_revenue * (1 + growth_rate) ** i * cogs_percentage) 
                    for i in range(time_periods)
                ],
                "opex": [opex] * time_periods,
                "net_income": [
                    (starting_revenue * (1 + growth_rate) ** i) - 
                    (starting_revenue * (1 + growth_rate) ** i * cogs_percentage) - 
                    opex 
                    for i in range(time_periods)
                ]
            },
            "key_metrics": {
                "total_revenue": sum([starting_revenue * (1 + growth_rate) ** i for i in range(time_periods)]),
                "average_gross_margin": 1 - cogs_percentage,
                "break_even_period": next(
                    (i for i, income in enumerate([
                        (starting_revenue * (1 + growth_rate) ** i) - 
                        (starting_revenue * (1 + growth_rate) ** i * cogs_percentage) - 
                        opex 
                        for i in range(time_periods)
                    ]) if income > 0), 
                    None
                )
            },
            "formats": {
                "summary": f"The {name} projects total revenue of ${sum([starting_revenue * (1 + growth_rate) ** i for i in range(time_periods)]):,.2f} over {time_periods} {period_type}s with {1 - cogs_percentage:.1%} gross margin.",
                "chart_data": {
                    "labels": [f"{period_type.capitalize()} {i+1}" for i in range(time_periods)],
                    "datasets": [
                        {
                            "label": "Revenue",
                            "data": [starting_revenue * (1 + growth_rate) ** i for i in range(time_periods)]
                        },
                        {
                            "label": "Expenses",
                            "data": [
                                (starting_revenue * (1 + growth_rate) ** i * cogs_percentage) + opex 
                                for i in range(time_periods)
                            ]
                        },
                        {
                            "label": "Net Income",
                            "data": [
                                (starting_revenue * (1 + growth_rate) ** i) - 
                                (starting_revenue * (1 + growth_rate) ** i * cogs_percentage) - 
                                opex 
                                for i in range(time_periods)
                            ]
                        }
                    ]
                }
            }
        }
    }
)

# Create a financial model using the template
FINANCIAL_MODEL_SAAS_STARTUP = financial_projection_template(
    "SaaS Startup Projection",
    50000,  # $50k starting monthly revenue
    0.08,   # 8% monthly growth
    0.25,   # 25% COGS
    80000,  # $80k monthly operating expenses
    24,     # 24 time periods
    "month"
)

Business Application: When creating financial projections, this template handles complex calculations automatically:

Human: "Let's create a financial model for our SaaS startup."
AI: [Helps build FINANCIAL_MODEL_SAAS_STARTUP using the template]
Human: "When will we reach break-even according to this model?"
AI: [Accesses FINANCIAL_MODEL_SAAS_STARTUP.content.key_metrics.break_even_period]
Human: "I'd like to see how changing our growth rate to 10% would affect revenue."
AI: [Creates a modified version of the model with updated parameters]

This approach prevents calculation errors, ensures consistent methodology across financial projections, and allows entrepreneurs to focus on strategic decision-making rather than spreadsheet formulas.

5. Standardized Documentation and Self-Description

Benefit: Create variables that document themselves, making them more discoverable and understandable.

Example:

python

# Define a template for API endpoints with built-in documentation
api_endpoint_template = create_variable_template(
    "api_endpoint_template",
    lambda name, method, path, description, params=None, response_format=None, authentication="required", rate_limit="100/hour":
    {
        "variable_name": f"API_{name.upper().replace(' ', '_')}",
        "content": {
            "name": name,
            "method": method.upper(),
            "path": path if path.startswith('/') else f'/{path}',
            "description": description,
            "parameters": params or [],
            "response": response_format or {"type": "JSON"},
            "authentication": authentication,
            "rate_limit": rate_limit,
            "examples": {
                "curl": f"curl -X {method.upper()} https://api.example.com{path if path.startswith('/') else f'/{path}'} -H \"Authorization: Bearer YOUR_API_KEY\"",
                "python": f"import requests\n\nurl = \"https://api.example.com{path if path.startswith('/') else f'/{path}'}\"\nheaders = {\"Authorization\": \"Bearer YOUR_API_KEY\"}\n\nresponse = requests.{method.lower()}(url, headers=headers)\ndata = response.json()",
                "javascript": f"fetch('https://api.example.com{path if path.startswith('/') else f'/{path}', {{\n  method: '{method.upper()}',\n  headers: {{\n    'Authorization': 'Bearer YOUR_API_KEY'\n  }}\n}})\n.then(response => response.json())\n.then(data => console.log(data))"
            },
            "documentation": lambda format="markdown": {
                "markdown": f"## {name}\n\n{description}\n\n**URL**: `{path}`\n**Method**: `{method.upper()}`\n**Auth required**: {authentication}\n**Rate limit**: {rate_limit}",
                "html": f"<h2>{name}</h2><p>{description}</p><p><strong>URL</strong>: <code>{path}</code></p><p><strong>Method</strong>: <code>{method.upper()}</code></p><p><strong>Auth required</strong>: {authentication}</p><p><strong>Rate limit</strong>: {rate_limit}</p>",
                "json": {"name": name, "description": description, "path": path, "method": method.upper(), "auth": authentication, "rate_limit": rate_limit}
            }[format]
        }
    }
)

# Create API endpoint documentation using the template
API_GET_USER_PROFILE = api_endpoint_template(
    "Get User Profile",
    "GET",
    "/api/v1/users/{user_id}",
    "Retrieves detailed information about a specific user account.",
    [
        {"name": "user_id", "type": "string", "required": True, "description": "Unique identifier for the user"},
        {"name": "include_preferences", "type": "boolean", "required": False, "description": "Whether to include user preferences in the response"}
    ],
    {
        "type": "JSON",
        "schema": {
            "id": "string",
            "name": "string",
            "email": "string",
            "created_at": "datetime",
            "preferences": "object (optional)"
        }
    }
)

API_UPDATE_SETTINGS = api_endpoint_template(
    "Update User Settings",
    "PATCH",
    "/api/v1/users/{user_id}/settings",
    "Updates user settings and preferences.",
    [
        {"name": "user_id", "type": "string", "required": True, "description": "Unique identifier for the user"},
        {"name": "settings", "type": "object", "required": True, "description": "Object containing settings to update"}
    ],
    {
        "type": "JSON",
        "schema": {
            "success": "boolean",
            "updated_settings": "array of strings"
        }
    }
)

Business Application: When creating developer documentation or SDK examples, this template generates consistent, self-documenting endpoints:

Human: "We need to document our user profile API endpoint for developers."
AI: [Provides API_GET_USER_PROFILE.content.documentation("markdown")]
Human: "Let's include code examples for the settings update endpoint."
AI: [Accesses API_UPDATE_SETTINGS.content.examples]
Human: "Can we generate the API documentation in HTML format for our developer portal?"
AI: [Provides API_GET_USER_PROFILE.content.documentation("html")]

This approach ensures comprehensive, consistent API documentation that automatically includes all necessary information developers need, while supporting multiple output formats without duplicating content.

Advanced Examples and Applications

Example 1: Multi-Level Template Composition

Templates can be composed to create more complex structures with reusable components:

python

# Base template for consistent formatting across content types
def content_base_template(title, content_type, body, tags=None, author=None, created_at=None):
    """Base template for all content types."""
    from datetime import datetime
    
    return {
        "title": title,
        "content_type": content_type,
        "body": body,
        "metadata": {
            "tags": tags or [],
            "author": author or "Admin",
            "created_at": created_at or datetime.now().strftime("%Y-%m-%d"),
            "updated_at": datetime.now().strftime("%Y-%m-%d"),
            "word_count": len(body.split()),
            "reading_time": f"{max(1, round(len(body.split()) / 200))} min"
        }
    }

# Specialized template for blog posts using the base template
def blog_post_template(title, intro, sections, conclusion, call_to_action=None, featured_image=None, tags=None, author=None):
    """Template for structured blog posts."""
    # Compose sections into full body
    body = f"{intro}\n\n"
    
    for section in sections:
        body += f"## {section['heading']}\n\n{section['content']}\n\n"
    
    body += f"{conclusion}\n\n"
    
    if call_to_action:
        body += f"**{call_to_action}**"
    
    # Use the base template for consistent structure
    blog_content = content_base_template(
        title=title,
        content_type="blog_post",
        body=body,
        tags=tags,
        author=author
    )
    
    # Add blog-specific fields
    blog_content["featured_image"] = featured_image
    blog_content["sections"] = [s["heading"] for s in sections]
    blog_content["formats"] = {
        "full": blog_content["body"],
        "summary": f"{intro}\n\n{conclusion}",
        "newsletter": f"# {title}\n\n{intro}\n\n{call_to_action or 'Read the full article on our blog.'}"
    }
    
    return blog_content

# Specialized template for product pages using the base template
def product_page_template(product_name, short_description, features, specifications, pricing, testimonials=None, faq=None, tags=None):
    """Template for product pages."""
    # Compose structured product content
    body = f"# {product_name}\n\n{short_description}\n\n## Key Features\n\n"
    
    for feature in features:
        body += f"### {feature['name']}\n{feature['description']}\n\n"
    
    body += "## Technical Specifications\n\n"
    for key, value in specifications.items():
        body += f"**{key}**: {value}\n\n"
    
    body += "## Pricing\n\n"
    for plan in pricing:
        body += f"### {plan['name']}: ${plan['price']}\n{plan['description']}\n\n"
    
    if testimonials:
        body += "## What Our Customers Say\n\n"
        for testimonial in testimonials:
            body += f"> \"{testimonial['quote']}\"\n> — {testimonial['author']}, {testimonial['company']}\n\n"
    
    if faq:
        body += "## Frequently Asked Questions\n\n"
        for question in faq:
            body += f"**Q: {question['question']}**\n\nA: {question['answer']}\n\n"
    
    # Use the base template for consistent structure
    product_content = content_base_template(
        title=product_name,
        content_type="product_page",
        body=body,
        tags=tags
    )
    
    # Add product-specific fields
    product_content["product_data"] = {
        "name": product_name,
        "features": features,
        "specifications": specifications,
        "pricing": pricing,
        "testimonials": testimonials,
        "faq": faq
    }
    
    product_content["structured_data"] = {
        "@context": "https://schema.org/",
        "@type": "Product",
        "name": product_name,
        "description": short_description,
        "offers": {
            "@type": "AggregateOffer",
            "lowPrice": min(plan["price"] for plan in pricing),
            "highPrice": max(plan["price"] for plan in pricing),
            "priceCurrency": "USD"
        }
    }
    
    return product_content

Business Application: Using these composed templates, entrepreneurs can create consistent content across their website:

Human: "Let's create a blog post about productivity tips."
AI: [Uses blog_post_template to create structured content with proper sections]
Human: "Now we need a product page for our premium plan."
AI: [Uses product_page_template which inherits from content_base_template]
Human: "Can we extract just the summary of the blog post for our newsletter?"
AI: [Accesses the formats.summary property from the blog post template]

This approach ensures consistency across different content types while allowing for specialized fields and formatting appropriate to each content category.

Example 2: Domain-Specific Business Templates

Templates tailored to specific business domains can encode industry best practices:

python

# E-commerce product launch template with phased rollout
def ecommerce_product_launch_template(
    product_name, 
    target_audience, 
    price_point, 
    usp, 
    production_time, 
    inventory_units, 
    marketing_budget
):
    """Template for e-commerce product launches with built-in timeline and metrics."""
    from datetime import datetime, timedelta
    
    start_date = datetime.now()
    
    # Calculate key dates based on production time
    production_end = start_date + timedelta(days=production_time)
    soft_launch = production_end + timedelta(days=14)
    full_launch = soft_launch + timedelta(days=21)
    promo_end = full_launch + timedelta(days=30)
    
    # Calculate recommended inventory allocation based on industry best practices
    initial_batch = min(max(int(inventory_units * 0.2), 50), 500)
    main_inventory = inventory_units - initial_batch
    
    # Calculate budget allocation based on e-commerce benchmarks
    budget_allocation = {
        "product_photography": round(marketing_budget * 0.15, 2),
        "social_media_ads": round(marketing_budget * 0.35, 2),
        "influencer_marketing": round(marketing_budget * 0.25, 2),
        "email_campaigns": round(marketing_budget * 0.15, 2),
        "pr_outreach": round(marketing_budget * 0.10, 2)
    }
    
    # Build the completed template
    launch_plan = {
        "product": {
            "name": product_name,
            "price": price_point,
            "unique_selling_proposition": usp,
            "target_audience": target_audience
        },
        "timeline": {
            "planning_phase": {
                "start_date": start_date.strftime("%Y-%m-%d"),
                "end_date": (start_date + timedelta(days=14)).strftime("%Y-%m-%d"),
                "key_activities": [
                    "Finalize product specifications",
                    "Create marketing assets",
                    "Set up analytics tracking",
                    "Prepare sales channels"
                ]
            },
            "production_phase": {
                "start_date": (start_date + timedelta(days=15)).strftime("%Y-%m-%d"),
                "end_date": production_end.strftime("%Y-%m-%d"),
                "key_activities": [
                    "Manufacturing",
                    "Quality control",
                    "Packaging design and production",
                    "Inventory management setup"
                ]
            },
            "soft_launch": {
                "date": soft_launch.strftime("%Y-%m-%d"),
                "inventory_allocation": initial_batch,
                "key_activities": [
                    "Limited release to early adopters",
                    "Gather initial customer feedback",
                    "Test sales process and fulfillment",
                    "Early influencer seeding"
                ]
            },
            "full_launch": {
                "date": full_launch.strftime("%Y-%m-%d"),
                "inventory_allocation": main_inventory,
                "key_activities": [
                    "Full marketing campaign activation",
                    "Press release distribution",
                    "Influencer campaign launch",
                    "Social media push"
                ]
            },
            "promotional_period": {
                "start_date": full_launch.strftime("%Y-%m-%d"),
                "end_date": promo_end.strftime("%Y-%m-%d"),
                "key_activities": [
                    "Monitor and optimize ad performance",
                    "Collect and showcase customer testimonials",
                    "Implement any necessary product adjustments",
                    "Plan for inventory replenishment"
                ]
            }
        },
        "marketing": {
            "budget": marketing_budget,
            "allocation": budget_allocation,
            "recommended_channels": get_recommended_channels(target_audience),
            "key_messaging": [
                f"Problem statement: {generate_problem_statement(target_audience, usp)}",
                f"Solution statement: {product_name} {generate_solution_statement(usp)}",
                f"Value proposition: {generate_value_proposition(price_point, usp)}"
            ]
        },
        "metrics": {
            "pre_launch": [
                "Email sign-ups",
                "Landing page conversion rate",
                "Social media engagement"
            ],
            "launch": [
                "First-day sales",
                "Website traffic",
                "Conversion rate",
                "Average order value"
            ],
            "post_launch": [
                "30-day sales volume",
                "Customer acquisition cost",
                "Return rate",
                "Net promoter score",
                "Repeat purchase rate"
            ],
            "target_kpis": {
                "inventory_turnover": "70% within 60 days",
                "customer_acquisition_cost": f"Less than {round(price_point * 0.3, 2)}",
                "conversion_rate": "3-5%",
                "return_rate": "Under 10%"
            }
        },
        "contingency_plans": {
            "over_demand": [
                "Priority allocation system",
                "Transparent waitlist communication",
                "Accelerated production schedule option",
                "Bundle with digital products while awaiting restock"
            ],
            "under_demand": [
                "Early-bird discount strategy",
                "Bundle offers with existing products",
                "Retarget visitors who didn't purchase",
                "Influencer push with special offers"
            ],
            "quality_issues": [
                "Predetermined quality control checkpoints",
                "Customer service response templates",
                "Remediation process",
                "Replacement/refund policy"
            ]
        }
    }
    
    return launch_plan

# Helper functions for the template
def get_recommended_channels(target_audience):
    """Determine best marketing channels based on target audience."""
    # Simplified implementation for example purposes
    if "professionals" in target_audience.lower():
        return ["LinkedIn", "Industry publications", "Email marketing", "Google Ads"]
    elif "gen z" in target_audience.lower() or "younger" in target_audience.lower():
        return ["TikTok", "Instagram", "Influencer marketing", "Snapchat"]
    else:
        return ["Facebook", "Instagram", "Google Ads", "Email marketing"]

def generate_problem_statement(target_audience, usp):
    """Generate a problem statement based on audience and USP."""
    # Simplified implementation for example purposes
    if "time" in usp.lower():
        return f"{target_audience} struggle with time-consuming tasks that distract from priorities"
    elif "cost" in usp.lower() or "affordable" in usp.lower():
        return f"{target_audience} face unnecessarily high costs for essential products"
    else:
        return f"{target_audience} lack effective solutions for their specific needs"

def generate_solution_statement(usp):
    """Generate a solution statement based on USP."""
    # Simplified implementation for example purposes
    return f"provides a unique solution that {usp.lower()}"

def generate_value_proposition(price, usp):
    """Generate a value proposition based on price and USP."""
    # Simplified implementation for example purposes
    if price < 50:
        return f"An affordable way to {usp.lower()}"
    elif price < 200:
        return f"A professional-grade solution to {usp.lower()}"
    else:
        return f"The ultimate premium solution to {usp.lower()}"

Business Application: For e-commerce entrepreneurs, this specialized template encodes industry best practices:

Human: "I'm planning to launch a new workout supplement product."
AI: [Uses ecommerce_product_launch_template to create a comprehensive launch plan]
Human: "What should our marketing budget allocation look like?"
AI: [References the launch_plan.marketing.allocation with industry-standard percentages]
Human: "How much inventory should we allocate for the soft launch?"
AI: [Provides launch_plan.timeline.soft_launch.inventory_allocation calculated based on best practices]

This approach ensures entrepreneurs follow proven industry patterns for product launches while tailoring the specifics to their unique product, automatically calculating appropriate budgets, timelines, and inventory allocations.

Implementation in CRAFT Framework

To integrate variable templates into your CRAFT framework effectively, consider:

Creating a template registry to organize and discover available templates
Developing a template extension mechanism for users to create custom templates
Building template validation to ensure correct parameter usage
Implementing template composition to allow templates to build upon each other

This approach would significantly enhance the CRAFT framework's ability to support entrepreneurs in common business tasks, aligning perfectly with your goal of creating structure that enables creative freedom.

The examples above demonstrate how variable templates can embed domain expertise directly into the CRAFT framework, making sophisticated business processes more accessible without requiring specialized knowledge of variable structure design.

Variable Namespaces in CRAFT

Understanding Variable Namespaces

What Are Variable Namespaces?

Rather than having all variables exist in a single "global" space, namespaces allow you to:

Group variables by purpose, domain, or function
Prevent naming conflicts between similar variables
Create logical hierarchies for complex projects
Establish clear boundaries between different aspects of a project

Think of namespaces as labeled containers or "neighborhoods" where related variables live together, creating an intuitive organizational structure that makes variables easier to find, understand, and use correctly.

Core Concepts Behind Variable Namespaces

Namespaces align with three key philosophical principles from your CRAFT framework:

Structure Enables Freedom: By organizing variables into logical groups, namespaces reduce cognitive load and make working with large numbers of variables more manageable.
Cumulative Intelligence: Namespaces facilitate building knowledge across multiple AI sessions by creating predictable locations for specific types of information.
Conservation of Tokens: Namespace organization reduces the need for lengthy explanations about variable relationships and purpose, saving valuable tokens.

Technical Implementation

At a basic level, namespaces can be implemented as objects or dictionaries that contain related variables:

python

class Namespace:
    def __init__(self, name, description=None):
        self.name = name
        self.description = description or f"Namespace for {name}-related variables"
        self.variables = {}
        
    def add(self, name, value, description=None):
        """Add a variable to this namespace"""
        self.variables[name] = {
            "value": value,
            "description": description or f"Variable '{name}' in {self.name} namespace"
        }
        return self  # Allow method chaining
        
    def get(self, name):
        """Get a variable's value from this namespace"""
        return self.variables.get(name, {}).get("value")
        
    def describe(self, name):
        """Get a variable's description from this namespace"""
        return self.variables.get(name, {}).get("description")
        
    def list_all(self):
        """List all variables in this namespace"""
        return list(self.variables.keys())

More sophisticated implementations might include:

Nested namespaces for deeper hierarchies
Access control for variable modification
Type validation for variables
Import/export mechanisms between namespaces
Version tracking for variables within namespaces

Benefits of Variable Namespaces

1. Organized Knowledge Management

Benefit: Create a logical, intuitive structure for managing large numbers of variables across complex projects.

Example:

python

# Create namespaces for different aspects of a business
Marketing = Namespace("Marketing", "Marketing-related variables and metrics")
Marketing.add("TARGET_AUDIENCE", "Tech-savvy professionals aged 25-45", "Definition of our primary customer segment")
Marketing.add("VALUE_PROPOSITION", "Save 3 hours daily with our AI assistant", "Our core value proposition")
Marketing.add("BRAND_VOICE", "Professional yet conversational, emphasizing efficiency", "Our brand's tone and personality")

Product = Namespace("Product", "Product specifications and features")
Product.add("CORE_FEATURES", ["AI scheduling", "Smart notifications", "Team sync"], "List of our product's main features")
Product.add("PRICING_TIERS", {"Basic": 49, "Pro": 99, "Enterprise": 249}, "Our pricing structure")
Product.add("RELEASE_CYCLE", "6-week sprints with monthly patches", "Our product development cadence")

Finance = Namespace("Finance", "Financial data and projections")
Finance.add("CURRENT_RUNWAY", "18 months", "How long our current funding will last")
Finance.add("BURN_RATE", 125000, "Monthly cash burn in USD")
Finance.add("REVENUE_TARGETS", {"Q1": 350000, "Q2": 425000, "Q3": 550000, "Q4": 700000}, "Quarterly revenue goals")

Business Application: For entrepreneurs managing complex business planning, this structure provides clear organization:

Human: "What's our core value proposition for marketing materials?"
AI: [Accesses Marketing.get("VALUE_PROPOSITION")]
Human: "Let's update our pricing for the Pro tier to $119."
AI: [Updates Product.variables["PRICING_TIERS"]["value"]["Pro"] = 119]
Human: "What's our projected revenue for Q3?"
AI: [References Finance.get("REVENUE_TARGETS")["Q3"]]

This organized approach prevents confusion between variables with similar names but different contexts (like "Product.PRICING" vs. "Finance.PRICING_FORECAST") and makes information easier to locate and maintain across multiple conversations.

2. Contextual Relevance and Clarity

Benefit: Provide immediate context for variables, making their purpose and relationships clearer without additional documentation.

Example:

python

# Create namespaces for different target personas
Persona = Namespace("Persona", "Customer persona definitions")

EnterpriseCustomer = Namespace("EnterpriseCustomer", "Enterprise customer characteristics")
EnterpriseCustomer.add("BUDGET_RANGE", "$50K-250K annually", "Typical purchasing budget")
EnterpriseCustomer.add("DECISION_TIMELINE", "3-6 months", "Average time from first contact to purchase")
EnterpriseCustomer.add("KEY_CONCERNS", ["Security", "Scalability", "Support SLAs", "Integration capabilities"], "Primary purchase considerations")
EnterpriseCustomer.add("BUYING_COMMITTEE", ["CIO", "VP of Operations", "Security Officer", "End-user representatives"], "Typical stakeholders involved")

SmallBusinessCustomer = Namespace("SmallBusinessCustomer", "Small business customer characteristics")
SmallBusinessCustomer.add("BUDGET_RANGE", "$5K-25K annually", "Typical purchasing budget")
SmallBusinessCustomer.add("DECISION_TIMELINE", "2-4 weeks", "Average time from first contact to purchase") 
SmallBusinessCustomer.add("KEY_CONCERNS", ["Cost", "Ease of use", "Quick ROI", "Minimal training"], "Primary purchase considerations")
SmallBusinessCustomer.add("BUYING_COMMITTEE", ["Owner/CEO", "Office Manager"], "Typical stakeholders involved")

# Register these sub-namespaces within the parent namespace
Persona.add("ENTERPRISE", EnterpriseCustomer, "Enterprise customer persona")
Persona.add("SMALL_BUSINESS", SmallBusinessCustomer, "Small business customer persona")

Business Application: When developing sales strategies for different customer segments, namespaces provide clear context:

Human: "What's the typical buying timeline for our enterprise customers?"
AI: [Accesses Persona.get("ENTERPRISE").get("DECISION_TIMELINE")]
Human: "How does that compare with small business customers?"
AI: [Compares with Persona.get("SMALL_BUSINESS").get("DECISION_TIMELINE")]
Human: "Let's create different sales pitch templates based on these key concerns."
AI: [Uses each persona's KEY_CONCERNS to craft targeted messaging]

The namespace hierarchy immediately clarifies which customer type is being referenced, eliminating confusion and providing built-in context that would otherwise require lengthy explanations or qualifiers.

3. Conflict Prevention and Resolution

Benefit: Prevent naming collisions and confusion when different domains use similar terminology.

Example:

python

# Create namespaces for different departments that might use similar terms
Sales = Namespace("Sales", "Sales-related metrics and definitions")
Sales.add("CONVERSION_RATE", "Percentage of qualified leads that become customers", "Sales pipeline effectiveness measure")
Sales.add("GROWTH", "Year-over-year increase in revenue", "Revenue expansion metric")
Sales.add("CYCLE", "Average time from first contact to closed deal", "Sales process timeline")

Marketing = Namespace("Marketing", "Marketing-related metrics and definitions")
Marketing.add("CONVERSION_RATE", "Percentage of website visitors who sign up for trial", "Website effectiveness measure")
Marketing.add("GROWTH", "Month-over-month increase in marketing qualified leads", "Lead generation expansion")
Marketing.add("CYCLE", "Average time from first touch to marketing qualified lead", "Marketing nurture timeline")

Product = Namespace("Product", "Product-related metrics and definitions")
Product.add("CONVERSION_RATE", "Percentage of trial users who become paying customers", "Product adoption measure")
Product.add("GROWTH", "Quarter-over-quarter increase in feature usage", "Product engagement expansion")
Product.add("CYCLE", "Average time from feature concept to release", "Development timeline")

Business Application: When discussing metrics that have different meanings across departments, namespaces prevent confusion:

Human: "What's our current conversion rate?"
AI: "I can provide conversion rates from different departments. Which one are you interested in?
    - Sales.CONVERSION_RATE: Percentage of qualified leads that become customers
    - Marketing.CONVERSION_RATE: Percentage of website visitors who sign up for trial
    - Product.CONVERSION_RATE: Percentage of trial users who become paying customers"
Human: "Let's look at the marketing conversion rate."
AI: [Accesses Marketing.get("CONVERSION_RATE")]

This approach eliminates ambiguity when the same term means different things in different contexts, helping entrepreneurs avoid confusion and miscommunication when discussing cross-functional metrics.

4. Modularity and Reusability

Benefit: Create modular knowledge components that can be imported, exported, and reused across projects.

Example:

python

# Create a reusable namespace for SaaS metrics that can be applied to different products
SaaSMetrics = Namespace("SaaSMetrics", "Standard metrics for SaaS businesses")
SaaSMetrics.add("MRR", "Monthly Recurring Revenue", "Total predictable revenue normalized to a monthly value")
SaaSMetrics.add("ARR", "Annual Recurring Revenue", "MRR multiplied by 12")
SaaSMetrics.add("CAC", "Customer Acquisition Cost", "Total sales and marketing cost divided by new customers acquired")
SaaSMetrics.add("LTV", "Customer Lifetime Value", "Average revenue per customer multiplied by customer lifespan")
SaaSMetrics.add("CHURN_RATE", "Percentage of customers who cancel in a given period", "Measure of customer retention")
SaaSMetrics.add("NRR", "Net Revenue Retention", "Measure of revenue from existing customers accounting for upgrades, downgrades, and churn")

# Create product-specific namespaces that import from the metrics namespace
ProductA = Namespace("ProductA", "CRM product metrics")
ProductA.add("METRICS", SaaSMetrics, "Standard SaaS metrics applied to our CRM product")
ProductA.add("CURRENT_VALUES", {
    "MRR": 425000,
    "CAC": 1200,
    "LTV": 24000,
    "CHURN_RATE": 0.02,
    "NRR": 1.08
}, "Current metric values for ProductA")

ProductB = Namespace("ProductB", "Analytics product metrics")
ProductB.add("METRICS", SaaSMetrics, "Standard SaaS metrics applied to our Analytics product")
ProductB.add("CURRENT_VALUES", {
    "MRR": 310000,
    "CAC": 950,
    "LTV": 18500,
    "CHURN_RATE": 0.025,
    "NRR": 1.12
}, "Current metric values for ProductB")

Business Application: For entrepreneurs managing multiple products or business lines, this approach enables consistent analysis:

Human: "Let's compare the LTV to CAC ratio between our CRM and Analytics products."
AI: [Calculates ProductA.get("CURRENT_VALUES")["LTV"] / ProductA.get("CURRENT_VALUES")["CAC"] and compares with the same calculation for ProductB]
Human: "Which product has better net revenue retention?"
AI: [Compares ProductA.get("CURRENT_VALUES")["NRR"] with ProductB.get("CURRENT_VALUES")["NRR"]]

This modular approach ensures consistent metric definitions across products while allowing for product-specific values, enabling valid cross-product comparisons without redefining metrics for each product.

5. Hierarchical Organization for Complex Projects

Benefit: Create multiple levels of organization for large-scale projects with many variables.

Example:

python

# Create a multi-level namespace hierarchy for a complex business
Company = Namespace("Company", "Top-level company namespace")

# Department-level namespaces
Marketing = Namespace("Marketing", "Marketing department")
Sales = Namespace("Sales", "Sales department")
Product = Namespace("Product", "Product department")

# Sub-namespaces within departments
ContentMarketing = Namespace("ContentMarketing", "Content marketing team")
SocialMediaMarketing = Namespace("SocialMediaMarketing", "Social media marketing team")
SEOMarketing = Namespace("SEOMarketing", "Search engine optimization team")

NorthAmericaSales = Namespace("NorthAmericaSales", "North America sales region")
EuropeSales = Namespace("EuropeSales", "Europe sales region")
AsiaSales = Namespace("AsiaSales", "Asia sales region")

ProductA = Namespace("ProductA", "CRM product line")
ProductB = Namespace("ProductB", "Analytics product line")

# Add second-level namespaces to their parents
Marketing.add("CONTENT", ContentMarketing)
Marketing.add("SOCIAL", SocialMediaMarketing)
Marketing.add("SEO", SEOMarketing)

Sales.add("NA", NorthAmericaSales)
Sales.add("EU", EuropeSales)
Sales.add("ASIA", AsiaSales)

Product.add("CRM", ProductA)
Product.add("ANALYTICS", ProductB)

# Add department-level namespaces to the company
Company.add("MARKETING", Marketing)
Company.add("SALES", Sales)
Company.add("PRODUCT", Product)

# Add specific variables at different levels
Company.add("MISSION", "To empower businesses with intelligent software solutions")
Company.add("FOUNDED", 2018)

Marketing.get("CONTENT").add("BLOG_FREQUENCY", "3 posts per week")
Marketing.get("SOCIAL").add("PLATFORMS", ["LinkedIn", "Twitter", "Instagram"])

Sales.get("NA").add("Q2_TARGET", 2500000)
Product.get("CRM").add("NEXT_RELEASE", "2025-07-15")

Business Application: For entrepreneurs managing complex organizations, this hierarchical structure creates intuitive navigation:

Human: "What's our blog publishing frequency?"
AI: [Accesses Company.get("MARKETING").get("CONTENT").get("BLOG_FREQUENCY")]
Human: "What's our Q2 sales target for North America?"
AI: [References Company.get("SALES").get("NA").get("Q2_TARGET")]
Human: "When is the next CRM product release scheduled?"
AI: [Checks Company.get("PRODUCT").get("CRM").get("NEXT_RELEASE")]

This hierarchical organization mirrors the structure of the actual business, making it intuitive to locate information regardless of how complex the organization becomes.

Advanced Examples and Applications

Example 1: Cross-Namespace Variable References

Namespaces can be designed to allow variables to reference data from other namespaces:

python

# Create a system for cross-namespace references
class EnhancedNamespace(Namespace):
    def __init__(self, name, description=None):
        super().__init__(name, description)
        self._global_registry = {}  # Registry of all namespaces
    
    def register_namespace(self, namespace):
        """Register another namespace for cross-references"""
        self._global_registry[namespace.name] = namespace
        return self
    
    def ref(self, namespace_name, variable_name):
        """Create a reference to a variable in another namespace"""
        target_namespace = self._global_registry.get(namespace_name)
        if not target_namespace:
            return f"Reference Error: Namespace '{namespace_name}' not found"
        
        target_value = target_namespace.get(variable_name)
        if target_value is None:
            return f"Reference Error: Variable '{variable_name}' not found in namespace '{namespace_name}'"
            
        return target_value

# Create the enhanced namespaces with cross-references
Registry = EnhancedNamespace("Registry", "Global namespace registry")

ProductMetrics = EnhancedNamespace("ProductMetrics", "Product performance metrics")
MarketData = EnhancedNamespace("MarketData", "Market research and competitive data")
BusinessPlanning = EnhancedNamespace("BusinessPlanning", "Business forecasts and planning")

# Register namespaces with each other
Registry.register_namespace(ProductMetrics)
Registry.register_namespace(MarketData)
Registry.register_namespace(BusinessPlanning)

ProductMetrics.register_namespace(MarketData)
ProductMetrics.register_namespace(BusinessPlanning)
MarketData.register_namespace(ProductMetrics)
MarketData.register_namespace(BusinessPlanning)
BusinessPlanning.register_namespace(ProductMetrics)
BusinessPlanning.register_namespace(MarketData)

# Add variables to namespaces
ProductMetrics.add("ACTIVE_USERS", 24500, "Current monthly active users")
ProductMetrics.add("CONVERSION_RATE", 0.032, "Trial to paid conversion rate")
ProductMetrics.add("ARPU", 95, "Average revenue per user")

MarketData.add("TOTAL_ADDRESSABLE_MARKET", 4500000000, "Total addressable market in USD")
MarketData.add("MARKET_GROWTH_RATE", 0.18, "Annual market growth rate")
MarketData.add("COMPETITOR_PRICES", {"CompA": 79, "CompB": 129, "CompC": 59}, "Competitor pricing")

# Create variables that reference data from other namespaces
BusinessPlanning.add("REVENUE_PROJECTION", 
    lambda years=3: {
        f"Year {i+1}": round(
            ProductMetrics.get("ACTIVE_USERS") * 
            ProductMetrics.get("CONVERSION_RATE") * 
            ProductMetrics.get("ARPU") * 12 * 
            (1 + MarketData.get("MARKET_GROWTH_RATE")) ** i
        ) 
        for i in range(years)
    },
    "Multi-year revenue projection based on current metrics and market growth"
)

BusinessPlanning.add("MARKET_SHARE", 
    lambda: (ProductMetrics.get("ACTIVE_USERS") * 
             ProductMetrics.get("CONVERSION_RATE") * 
             ProductMetrics.get("ARPU") * 12) / 
            MarketData.get("TOTAL_ADDRESSABLE_MARKET"),
    "Current market share percentage"
)

BusinessPlanning.add("COMPETITIVE_PRICING_POSITION",
    lambda: {
        "our_price": ProductMetrics.get("ARPU"),
        "vs_competitors": {
            comp: round((ProductMetrics.get("ARPU") - price) / price * 100, 1)
            for comp, price in MarketData.get("COMPETITOR_PRICES").items()
        },
        "summary": get_pricing_position_summary(
            ProductMetrics.get("ARPU"),
            MarketData.get("COMPETITOR_PRICES")
        )
    },
    "Analysis of our pricing relative to competitors"
)

def get_pricing_position_summary(our_price, competitor_prices):
    """Helper function to generate pricing position summary"""
    avg_competitor_price = sum(competitor_prices.values()) / len(competitor_prices)
    position = our_price / avg_competitor_price
    
    if position < 0.8:
        return "Value-priced (significantly below market average)"
    elif position < 0.95:
        return "Competitively priced (slightly below market average)"
    elif position < 1.05:
        return "Market average pricing"
    elif position < 1.2:
        return "Premium-priced (slightly above market average)"
    else:
        return "Luxury-priced (significantly above market average)"

Business Application: For entrepreneurs doing business planning, this approach connects data across domains:

Human: "What's our projected revenue for the next three years?"
AI: [Calculates BusinessPlanning.get("REVENUE_PROJECTION")(), which dynamically uses values from ProductMetrics and MarketData]
Human: "How does our pricing compare to competitors?"
AI: [Provides BusinessPlanning.get("COMPETITIVE_PRICING_POSITION")(), which analyzes our price vs. competitor prices]
Human: "If we increase our conversion rate by 20%, how would that affect revenue projections?"
AI: [Updates ProductMetrics.variables["CONVERSION_RATE"]["value"] *= 1.2 and recalculates projections]

This cross-namespace referencing creates a dynamic business model where changes in one area automatically update all dependent calculations across different business domains.

Example 2: Domain-Specific Namespace Patterns

Different business domains can benefit from specialized namespace structures that reflect their unique characteristics:

python

# E-commerce business namespace pattern
def create_ecommerce_namespace_structure(business_name):
    """Create a structured namespace hierarchy for an e-commerce business"""
    # Top-level company namespace
    Company = Namespace(business_name, f"{business_name} E-commerce Business")
    
    # Core department namespaces
    Products = Namespace("Products", "Product catalog and inventory")
    Marketing = Namespace("Marketing", "Marketing and customer acquisition")
    Operations = Namespace("Operations", "Fulfillment and customer service")
    Finance = Namespace("Finance", "Financial metrics and projections")
    
    # Product catalog sub-namespaces (dynamically expandable)
    ProductCategories = {}
    
    # Marketing sub-namespaces
    Acquisition = Namespace("Acquisition", "Customer acquisition channels")
    Retention = Namespace("Retention", "Customer retention programs")
    Campaigns = Namespace("Campaigns", "Marketing campaigns")
    
    # Operations sub-namespaces
    Inventory = Namespace("Inventory", "Inventory management")
    Shipping = Namespace("Shipping", "Shipping and fulfillment")
    Returns = Namespace("Returns", "Returns and exchanges")
    CustomerService = Namespace("CustomerService", "Customer service metrics")
    
    # Finance sub-namespaces
    Revenue = Namespace("Revenue", "Revenue streams")
    Expenses = Namespace("Expenses", "Cost structure")
    Profitability = Namespace("Profitability", "Profitability metrics")
    
    # Add sub-namespaces to their parents
    Marketing.add("ACQUISITION", Acquisition)
    Marketing.add("RETENTION", Retention)
    Marketing.add("CAMPAIGNS", Campaigns)
    
    Operations.add("INVENTORY", Inventory)
    Operations.add("SHIPPING", Shipping)
    Operations.add("RETURNS", Returns)
    Operations.add("CUSTOMER_SERVICE", CustomerService)
    
    Finance.add("REVENUE", Revenue)
    Finance.add("EXPENSES", Expenses)
    Finance.add("PROFITABILITY", Profitability)
    
    # Add department-level namespaces to the company
    Company.add("PRODUCTS", Products)
    Company.add("MARKETING", Marketing)
    Company.add("OPERATIONS", Operations)
    Company.add("FINANCE", Finance)
    
    # Add methods to dynamically add product categories
    def add_product_category(category_name, description=None):
        category = Namespace(category_name, description or f"{category_name} product category")
        ProductCategories[category_name] = category
        Products.add(category_name.upper(), category)
        return category
    
    Company.add_product_category = add_product_category
    
    # Add common e-commerce metrics
    Finance.get("REVENUE").add("AOV", 0, "Average Order Value")
    Finance.get("PROFITABILITY").add("CONTRIBUTION_MARGIN", 0, "Revenue minus variable costs")
    Marketing.get("ACQUISITION").add("CAC", 0, "Customer Acquisition Cost")
    Marketing.get("RETENTION").add("REPEAT_PURCHASE_RATE", 0, "Percentage of customers who purchase again")
    Operations.get("INVENTORY").add("TURNOVER", 0, "Inventory turnover rate")
    
    return Company

# Create an e-commerce business with this pattern
FashionFastTrack = create_ecommerce_namespace_structure("FashionFastTrack")

# Add product categories
Apparel = FashionFastTrack.add_product_category("Apparel", "Clothing items")
Accessories = FashionFastTrack.add_product_category("Accessories", "Fashion accessories")
Footwear = FashionFastTrack.add_product_category("Footwear", "Shoes and boots")

# Add specific products to categories
Apparel.add("BESTSELLERS", ["Summer Breeze Dress", "Classic Fit Jeans", "Cozy Lounge Set"], "Top-selling apparel items")
Accessories.add("PROFIT_MARGIN", 0.68, "Average profit margin on accessories")
Footwear.add("RETURN_RATE", 0.15, "Return rate for footwear items")

# Add business metrics
FashionFastTrack.get("FINANCE").get("REVENUE").variables["AOV"]["value"] = 72.50
FashionFastTrack.get("MARKETING").get("ACQUISITION").variables["CAC"]["value"] = 28.75

Business Application: For e-commerce entrepreneurs, this domain-specific namespace structure reflects industry patterns:

Human: "What's our average order value?"
AI: [Accesses FashionFastTrack.get("FINANCE").get("REVENUE").get("AOV")]
Human: "What's our best-selling apparel item?"
AI: [References FashionFastTrack.get("PRODUCTS").get("APPAREL").get("BESTSELLERS")[0]]
Human: "Which product category has the highest return rate?"
AI: [Compares return rates across product categories]

This approach creates intuitive organization that matches how e-commerce businesses naturally think about their operations, making information easier to locate and update.

Example 3: Namespace Access Control and Permissions

For collaborative projects, namespaces can include access control:

python

# Define user roles for access control
class UserRole:
    VIEWER = "viewer"  # Can read variables but not modify
    EDITOR = "editor"  # Can read and modify existing variables
    ADMIN = "admin"    # Can read, modify, add, and delete variables

# Create a namespace with access control
class SecureNamespace(Namespace):
    def __init__(self, name, description=None):
        super().__init__(name, description)
        self.access_control = {}
        self.default_role = UserRole.VIEWER
        
    def set_access(self, user_id, role):
        """Set access level for a specific user"""
        self.access_control[user_id] = role
        return self
        
    def set_default_role(self, role):
        """Set default access level for users not explicitly listed"""
        self.default_role = role
        return self
        
    def get_user_role(self, user_id):
        """Get access level for a specific user"""
        return self.access_control.get(user_id, self.default_role)
        
    def add(self, name, value, description=None, user_id=None):
        """Add a variable with access control check"""
        if user_id and self.get_user_role(user_id) not in [UserRole.EDITOR, UserRole.ADMIN]:
            return f"Access Denied: User '{user_id}' does not have permission to add variables"
        return super().add(name, value, description)
        
    def get(self, name, user_id=None):
        """Get a variable with access control check"""
        if user_id and self.get_user_role(user_id) == UserRole.VIEWER:
            # Check if this variable is viewable by this user
            if name.startswith("PRIVATE_"):
                return "Access Denied: This variable is private"
        return super().get(name)
        
    def update(self, name, value, user_id=None):
        """Update a variable with access control check"""
        if user_id and self.get_user_role(user_id) not in [UserRole.EDITOR, UserRole.ADMIN]:
            return f"Access Denied: User '{user_id}' does not have permission to modify variables"
        if name in self.variables:
            self.variables[name]["value"] = value
            return f"Variable '{name}' updated successfully"
        return f"Variable '{name}' not found"

# Create a collaborative business plan with access control
BusinessPlan = SecureNamespace("BusinessPlan", "Company business plan with controlled access")

# Set up access control
BusinessPlan.set_access("founder@company.com", UserRole.ADMIN)
BusinessPlan.set_access("cfo@company.com", UserRole.ADMIN)
BusinessPlan.set_access("marketing@company.com", UserRole.EDITOR)
BusinessPlan.set_access("sales@company.com", UserRole.EDITOR)
BusinessPlan.set_access("investor@vc.com", UserRole.VIEWER)

# Add variables with different sensitivity levels
BusinessPlan.add("COMPANY_NAME", "TechSolutions Inc.", "Company legal name", "founder@company.com")
BusinessPlan.add("MISSION", "To revolutionize business productivity through AI", "Company mission", "founder@company.com")

BusinessPlan.add("REVENUE_FORECAST", 
                 {"Y1": 1200000, "Y2": 3600000, "Y3": 8500000}, 
                 "3-year revenue forecast", 
                 "cfo@company.com")

BusinessPlan.add("MARKETING_BUDGET", 
                 {"Q1": 120000, "Q2": 150000, "Q3": 180000, "Q4": 200000},
                 "Quarterly marketing budget",
                 "marketing@company.com")

BusinessPlan.add("PRIVATE_ACQUISITION_TARGET", 
                 "CompetitorX - preliminary discussions in progress",
                 "Confidential acquisition target",
                 "founder@company.com")

Business Application: For businesses with multiple stakeholders, this approach respects information sensitivity:

Human: "As our marketing director, I need to update our Q2 marketing budget to $175,000."
AI: [Verifies access and executes BusinessPlan.update("MARKETING_BUDGET", {"Q1": 120000, "Q2": 175000, "Q3": 180000, "Q4": 200000}, "marketing@company.com")]
Human: "As an investor, I'd like to see the current revenue forecast."
AI: [Verifies access and provides BusinessPlan.get("REVENUE_FORECAST", "investor@vc.com")]
Human: "As an investor, I'm curious about any acquisition targets."
AI: [Verifies access and returns "Access Denied: This variable is private" for BusinessPlan.get("PRIVATE_ACQUISITION_TARGET", "investor@vc.com")]

This approach ensures sensitive business information is appropriately protected while still enabling collaboration, mirroring real-world information access patterns in organizations.

Implementation in CRAFT Framework

To integrate variable namespaces into your CRAFT framework effectively, consider:

Creating a namespace registry as a central repository of all defined namespaces
Implementing namespace imports/exports to facilitate sharing between projects
Adding namespace visualization to help users understand the organizational structure
Developing namespace templates for common business domains (e-commerce, SaaS, service business)
Building namespace persistence to maintain organization across multiple AI sessions

This approach would significantly enhance the CRAFT framework's ability to manage complex projects with large numbers of variables, aligning perfectly with your goal of creating cumulative intelligence across AI interactions.

The examples above demonstrate how namespaces can transform variable management from a flat, potentially chaotic collection into an organized, intuitive hierarchy that reflects the natural structure of businesses and projects.

Dynamic Variables in CRAFT

Understanding Dynamic Variables

What Are Dynamic Variables?

Transform their values based on triggers or events
Track state changes over time
Adapt to conversation progress
Respond to contextual cues
Update related information automatically
Remember their history and evolution

Think of dynamic variables as "living" data structures that actively participate in conversations rather than passively storing information. They possess internal intelligence that allows them to evolve alongside the conversation, reducing the need for manual updates and maintenance.

Core Concepts Behind Dynamic Variables

Dynamic variables align with three key philosophical principles from your CRAFT framework:

Cumulative Intelligence: By tracking their own evolution, dynamic variables build knowledge across the conversation, creating a more cohesive experience.
Human-AI Partnership: Their self-updating nature creates a more responsive collaboration where the AI can adapt without constant human direction.
Conservation of Tokens: By handling updates automatically, dynamic variables eliminate repetitive instructions about how and when to modify values.

Technical Implementation

At a basic level, dynamic variables can be implemented as objects with update methods and trigger functions:

def create_dynamic_variable(name, initial_value, update_rules=None):
    """Creates a variable that can update itself based on defined rules or external triggers."""
    from datetime import datetime
    
    return {
        "name": name,
        "value": initial_value,
        "previous_values": [],
        "update_count": 0,
        "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "created_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "update_rules": update_rules or {},
        
        # Methods (represented as dictionary functions in this example)
        "get": lambda: dynamic_variable["value"],
        
        "set": lambda new_value, reason=None: dynamic_variable.update({
            "previous_values": dynamic_variable["previous_values"] + [(dynamic_variable["value"], dynamic_variable["last_updated"], reason or "manual update")],
            "value": new_value,
            "update_count": dynamic_variable["update_count"] + 1,
            "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }),
        
        "update_if": lambda condition, new_value, reason=None: 
            dynamic_variable["set"](new_value, reason) if condition else dynamic_variable["value"],
            
        "history": lambda: dynamic_variable["previous_values"],
        
        "apply_rule": lambda rule_name, *args: 
            dynamic_variable["update_rules"][rule_name](dynamic_variable, *args) 
            if rule_name in dynamic_variable["update_rules"] 
            else f"Rule '{rule_name}' not found"
    }

More sophisticated implementations might include:

Complex state machines
Event listeners for conversation triggers
Predictive behavior based on patterns
Cross-variable dependencies
Conditional branching logic

Benefits of Dynamic Variables

1. Conversation Stage Awareness

Benefit: Variables automatically track and adapt to the different stages of a conversation, helping maintain appropriate context.

Example:

# Create a dynamic variable that tracks conversation stage
CONVERSATION_STAGE = create_dynamic_variable(
    "CONVERSATION_STAGE",
    {
        "current": "introduction",
        "stages": ["introduction", "discovery", "solution", "objection_handling", "closing"],
        "stage_index": 0,
        "stage_descriptions": {
            "introduction": "Initial greeting and rapport building",
            "discovery": "Exploring needs and pain points",
            "solution": "Presenting relevant solutions",
            "objection_handling": "Addressing concerns and questions",
            "closing": "Moving toward commitment and next steps"
        },
        "duration": {
            "introduction": 0,
            "discovery": 0,
            "solution": 0,
            "objection_handling": 0,
            "closing": 0
        },
        "started_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    },
    {
        "advance": lambda var, reason=None: var["set"](
            {
                "current": var["value"]["stages"][min(var["value"]["stage_index"] + 1, len(var["value"]["stages"]) - 1)],
                "stages": var["value"]["stages"],
                "stage_index": min(var["value"]["stage_index"] + 1, len(var["value"]["stages"]) - 1),
                "stage_descriptions": var["value"]["stage_descriptions"],
                "duration": {
                    **var["value"]["duration"],
                    var["value"]["current"]: (datetime.now() - datetime.strptime(var["value"]["started_at"], "%Y-%m-%d %H:%M:%S")).seconds // 60
                },
                "started_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            },
            reason or f"Advanced from {var['value']['current']} to {var['value']['stages'][min(var['value']['stage_index'] + 1, len(var['value']['stages']) - 1)]}"
        ),
        
        "jump_to": lambda var, stage, reason=None: var["set"](
            {
                "current": stage if stage in var["value"]["stages"] else var["value"]["current"],
                "stages": var["value"]["stages"],
                "stage_index": var["value"]["stages"].index(stage) if stage in var["value"]["stages"] else var["value"]["stage_index"],
                "stage_descriptions": var["value"]["stage_descriptions"],
                "duration": {
                    **var["value"]["duration"],
                    var["value"]["current"]: (datetime.now() - datetime.strptime(var["value"]["started_at"], "%Y-%m-%d %H:%M:%S")).seconds // 60
                },
                "started_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            },
            reason or f"Jumped to {stage} stage"
        ) if stage in var["value"]["stages"] else f"Invalid stage: {stage}"
    }
)

Business Application: For sales conversations or customer support interactions, this variable tracks progress automatically:

Human: "Let's start discussing the client's needs."
AI: [Recognizes this as a transition to discovery, uses CONVERSATION_STAGE.apply_rule("advance", "Customer asked about needs")]
Human: "Based on what they've shared, what solutions should we propose?"
AI: [Identifies a transition to solution stage, updates CONVERSATION_STAGE again]
Human: "They're concerned about the implementation timeline."
AI: [Transitions to objection_handling stage]
Human: "What stage are we in now, and how much time have we spent in each stage?"
AI: [Provides current stage and duration metrics from CONVERSATION_STAGE]

This approach ensures appropriate responses that match the conversation's current context, while gathering valuable analytics about conversation flow without requiring manual tracking.

2. Progress-Tracking and Milestones

Benefit: Automatically monitor progress toward goals, update completion status, and recognize milestone achievements.

Example:

python

# Create a dynamic variable for tracking project progress
PROJECT_STATUS = create_dynamic_variable(
    "PROJECT_STATUS",
    {
        "name": "Website Redesign",
        "progress": 0.25, # 25% complete
        "milestones": [
            {"name": "Requirements gathering", "status": "completed", "completion_date": "2025-04-20"},
            {"name": "Design mockups", "status": "in_progress", "due_date": "2025-05-20"},
            {"name": "Frontend development", "status": "not_started", "due_date": "2025-06-15"},
            {"name": "Backend integration", "status": "not_started", "due_date": "2025-07-10"},
            {"name": "Testing", "status": "not_started", "due_date": "2025-07-25"},
            {"name": "Launch", "status": "not_started", "due_date": "2025-08-01"}
        ],
        "risks": [
            {"description": "Client feedback delays", "severity": "medium", "mitigation": "Weekly check-ins"},
            {"description": "Integration complexity", "severity": "high", "mitigation": "Early technical discovery"}
        ],
        "next_action": "Finalize homepage wireframes"
    },
    {
        "update_milestone": lambda var, milestone_name, new_status, reason=None: var["set"](
            {
                **var["value"],
                "milestones": [
                    {
                        **m,
                        "status": new_status,
                        "completion_date": datetime.now().strftime("%Y-%m-%d") if new_status == "completed" and m["status"] != "completed" else m.get("completion_date")
                    } if m["name"] == milestone_name else m
                    for m in var["value"]["milestones"]
                ],
                "progress": round(sum(1 for m in [
                    {
                        **m,
                        "status": new_status if m["name"] == milestone_name else m["status"]
                    } for m in var["value"]["milestones"]
                ] if m["status"] == "completed") / len(var["value"]["milestones"]), 2)
            },
            reason or f"Updated milestone '{milestone_name}' to '{new_status}'"
        ),
        
        "add_risk": lambda var, description, severity, mitigation, reason=None: var["set"](
            {
                **var["value"],
                "risks": var["value"]["risks"] + [
                    {"description": description, "severity": severity, "mitigation": mitigation}
                ]
            },
            reason or f"Added risk: {description}"
        ),
        
        "set_next_action": lambda var, action, reason=None: var["set"](
            {
                **var["value"],
                "next_action": action
            },
            reason or f"Updated next action to: {action}"
        ),
        
        "generate_status_report": lambda var: {
            "project": var["value"]["name"],
            "current_progress": f"{int(var["value"]["progress"] * 100)}%",
            "completed_milestones": [m["name"] for m in var["value"]["milestones"] if m["status"] == "completed"],
            "in_progress": [m["name"] for m in var["value"]["milestones"] if m["status"] == "in_progress"],
            "upcoming_due_dates": [{"milestone": m["name"], "due": m["due_date"]} for m in var["value"]["milestones"] if m["status"] != "completed"],
            "high_severity_risks": [r["description"] for r in var["value"]["risks"] if r["severity"] == "high"],
            "immediate_next_action": var["value"]["next_action"]
        }
    }
)

Business Application: For project management, this dynamic variable tracks progress and generates reports:

Human: "The design mockups milestone is now complete."
AI: [Executes PROJECT_STATUS.apply_rule("update_milestone", "Design mockups", "completed")]
Human: "We've identified a new risk around browser compatibility."
AI: [Adds risk with PROJECT_STATUS.apply_rule("add_risk", "Browser compatibility issues", "medium", "Cross-browser testing plan")]
Human: "Generate a status report for the stakeholder meeting."
AI: [Provides PROJECT_STATUS.apply_rule("generate_status_report")]

This variable automatically updates overall progress percentages, tracks completion dates, and maintains a comprehensive view of the project state without requiring manual calculations or updates.

3. Contextual Memory and Learning

Benefit: Variables remember past states and interactions, enabling them to learn from and adapt to conversation patterns.

Example:

python

# Create a dynamic variable that learns user preferences over time
USER_PREFERENCES = create_dynamic_variable(
    "USER_PREFERENCES",
    {
        "communication_style": {
            "detail_level": "medium",
            "formality": "casual",
            "examples": True,
            "visual_references": True
        },
        "topic_interests": {
            "marketing": 3,
            "product": 2,
            "finance": 1,
            "technology": 4,
            "strategy": 3
        },
        "interaction_patterns": {
            "average_response_length": 0,
            "response_count": 0,
            "questions_asked": 0,
            "conversation_count": 1
        },
        "detected_preferences": []
    },
    {
        "record_interaction": lambda var, response_length, contains_question, primary_topic=None, reason=None: var["set"](
            {
                "communication_style": var["value"]["communication_style"],
                "topic_interests": {
                    **var["value"]["topic_interests"],
                    primary_topic: var["value"]["topic_interests"].get(primary_topic, 0) + 1 if primary_topic else var["value"]["topic_interests"]
                },
                "interaction_patterns": {
                    "average_response_length": round(
                        (var["value"]["interaction_patterns"]["average_response_length"] * var["value"]["interaction_patterns"]["response_count"] + response_length) /
                        (var["value"]["interaction_patterns"]["response_count"] + 1)
                    ),
                    "response_count": var["value"]["interaction_patterns"]["response_count"] + 1,
                    "questions_asked": var["value"]["interaction_patterns"]["questions_asked"] + (1 if contains_question else 0),
                    "conversation_count": var["value"]["interaction_patterns"]["conversation_count"]
                },
                "detected_preferences": var["value"]["detected_preferences"]
            },
            reason or "Recorded new interaction data"
        ),
        
        "infer_preference": lambda var, preference, evidence, confidence, reason=None: var["set"](
            {
                **var["value"],
                "detected_preferences": var["value"]["detected_preferences"] + [
                    {"preference": preference, "evidence": evidence, "confidence": confidence, "detected_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
                ]
            },
            reason or f"Inferred new preference: {preference}"
        ),
        
        "update_communication_style": lambda var, attribute, value, reason=None: var["set"](
            {
                **var["value"],
                "communication_style": {
                    **var["value"]["communication_style"],
                    attribute: value
                }
            },
            reason or f"Updated communication style preference: {attribute} = {value}"
        ),
        
        "get_personalization_suggestions": lambda var: {
            "communication_suggestions": {
                "response_length": "shorter" if var["value"]["interaction_patterns"]["average_response_length"] < 100 else "longer" if var["value"]["interaction_patterns"]["average_response_length"] > 300 else "current",
                "detail_level": var["value"]["communication_style"]["detail_level"],
                "include_examples": var["value"]["communication_style"]["examples"]
            },
            "topic_recommendations": sorted(var["value"]["topic_interests"].items(), key=lambda x: x[1], reverse=True)[:3],
            "interaction_style": "inquisitive" if var["value"]["interaction_patterns"]["questions_asked"] / max(1, var["value"]["interaction_patterns"]["response_count"]) > 0.3 else "declarative"
        }
    }
)

Business Application: For ongoing customer relationships, this variable builds a preference model over time:

Human: "Can you provide a brief overview of our marketing metrics?"
AI: [Records interaction data with USER_PREFERENCES.apply_rule("record_interaction", len(response), True, "marketing")]
Human: "I prefer more detailed explanations with specific examples."
AI: [Updates with USER_PREFERENCES.apply_rule("update_communication_style", "detail_level", "high")]
AI: [Also updates USER_PREFERENCES.apply_rule("update_communication_style", "examples", True)]
Human: "How should we personalize future communications with this client?"
AI: [Provides recommendations from USER_PREFERENCES.apply_rule("get_personalization_suggestions")]

This approach creates a continuously improving model of user preferences without requiring explicit preference-setting conversations, making interactions progressively more personalized over time.

4. Intelligent Data Transformation

Benefit: Automatically transform data between formats, units, or representations based on contextual needs.

Example:

python

# Create a dynamic variable for financial data with automatic conversions
FINANCIAL_DATA = create_dynamic_variable(
    "FINANCIAL_DATA",
    {
        "revenue": {
            "value": 4750000,  # $4.75M
            "currency": "USD",
            "period": "annual",
            "fiscal_year": 2024
        },
        "growth_rate": 0.32,  # 32%
        "profit_margin": 0.24,  # 24%
        "customer_metrics": {
            "acquisition_cost": 425,  # $425
            "lifetime_value": 2800,  # $2,800
            "churn_rate": 0.15  # 15%
        },
        "display_format": "standard",  # Could be standard, compact, or detailed
        "display_currency": "USD"
    },
    {
        "set_display_format": lambda var, format, reason=None: var["set"](
            {
                **var["value"],
                "display_format": format
            },
            reason or f"Changed display format to {format}"
        ),
        
        "set_display_currency": lambda var, currency, reason=None: var["set"](
            {
                **var["value"],
                "display_currency": currency
            },
            reason or f"Changed display currency to {currency}"
        ),
        
        "update_revenue": lambda var, new_value, period=None, currency=None, fiscal_year=None, reason=None: var["set"](
            {
                **var["value"],
                "revenue": {
                    "value": new_value,
                    "currency": currency or var["value"]["revenue"]["currency"],
                    "period": period or var["value"]["revenue"]["period"],
                    "fiscal_year": fiscal_year or var["value"]["revenue"]["fiscal_year"]
                }
            },
            reason or f"Updated revenue to {new_value} {currency or var['value']['revenue']['currency']}"
        ),
        
        "get_formatted": lambda var: {
            "standard": {
                "revenue": f"${var['value']['revenue']['value']:,.0f} {var['value']['revenue']['period']}",
                "growth_rate": f"{var['value']['growth_rate']:.1%}",
                "profit_margin": f"{var['value']['profit_margin']:.1%}",
                "ltv_to_cac_ratio": f"{var['value']['customer_metrics']['lifetime_value'] / var['value']['customer_metrics']['acquisition_cost']:.1f}x"
            },
            "compact": {
                "revenue": f"${var['value']['revenue']['value']/1000000:.1f}M",
                "growth": f"+{var['value']['growth_rate']:.0%}",
                "margin": f"{var['value']['profit_margin']:.0%}",
                "ltv/cac": f"{var['value']['customer_metrics']['lifetime_value'] / var['value']['customer_metrics']['acquisition_cost']:.1f}x"
            },
            "detailed": {
                "revenue": {
                    "annual": var['value']['revenue']['value'],
                    "quarterly": var['value']['revenue']['value'] / 4,
                    "monthly": var['value']['revenue']['value'] / 12,
                    "growth_rate": var['value']['growth_rate'],
                    "year_over_year_change": var['value']['revenue']['value'] * var['value']['growth_rate']
                },
                "profitability": {
                    "gross_margin": var['value']['profit_margin'],
                    "annual_profit": var['value']['revenue']['value'] * var['value']['profit_margin'],
                    "monthly_profit": (var['value']['revenue']['value'] * var['value']['profit_margin']) / 12
                },
                "customer_economics": {
                    "cac": var['value']['customer_metrics']['acquisition_cost'],
                    "ltv": var['value']['customer_metrics']['lifetime_value'],
                    "ltv_to_cac_ratio": var['value']['customer_metrics']['lifetime_value'] / var['value']['customer_metrics']['acquisition_cost'],
                    "months_to_recover_cac": var['value']['customer_metrics']['acquisition_cost'] / ((var['value']['revenue']['value'] * var['value']['profit_margin']) / 12 / (var['value']['revenue']['value'] / var['value']['customer_metrics']['lifetime_value']))
                }
            }
        }[var["value"]["display_format"]],
        
        "convert_currency": lambda var, target_currency: {
            # Simplified currency conversion for example
            "USD": {
                "EUR": 0.92,
                "GBP": 0.78,
                "JPY": 152.5,
                "CAD": 1.36
            },
            "EUR": {
                "USD": 1.09,
                "GBP": 0.85,
                "JPY": 166.2,
                "CAD": 1.48
            }
            # Other conversion rates would be here
        }.get(var["value"]["revenue"]["currency"], {}).get(target_currency, 1) * var["value"]["revenue"]["value"]
    }
)

Business Application: For financial reporting, this variable adapts data presentation to different contexts:

Human: "Let me see our financial summary."
AI: [Uses FINANCIAL_DATA.apply_rule("get_formatted") to present data in current format]
Human: "For the board presentation, let's use the compact format."
AI: [Applies FINANCIAL_DATA.apply_rule("set_display_format", "compact")]
Human: "Our European investors need the revenue in Euros."
AI: [Calculates FINANCIAL_DATA.apply_rule("convert_currency", "EUR") for the conversion]
Human: "For the detailed budget planning, I need the full financial breakdown."
AI: [Changes to FINANCIAL_DATA.apply_rule("set_display_format", "detailed")]

This variable intelligently adapts how financial data is presented based on the audience and context, without requiring manual reformatting or conversion calculations.

5. State-Based Decision Logic

Benefit: Implement complex decision trees that evolve based on conversation history and user choices.

Example:

python

# Create a dynamic variable for a guided decision-making process
PRODUCT_RECOMMENDATION = create_dynamic_variable(
    "PRODUCT_RECOMMENDATION",
    {
        "current_state": "initial",
        "customer_profile": {
            "business_size": None,
            "industry": None,
            "primary_need": None,
            "budget_range": None,
            "technical_expertise": None
        },
        "questions_asked": [],
        "answers_received": {},
        "recommendation": None,
        "confidence": 0,
        "alternative_options": [],
        "state_flow": {
            "initial": {"next": "business_size", "asked": False},
            "business_size": {"next": "industry", "asked": False},
            "industry": {"next": "primary_need", "asked": False},
            "primary_need": {"next": "budget_range", "asked": False},
            "budget_range": {"next": "technical_expertise", "asked": False},
            "technical_expertise": {"next": "recommendation", "asked": False},
            "recommendation": {"next": "complete", "asked": False},
            "complete": {"next": None, "asked": False}
        }
    },
    {
        "get_next_question": lambda var: {
            "initial": "What size is your business (employees)?",
            "business_size": "What industry are you in?",
            "industry": "What's your primary need for this software?",
            "primary_need": "What's your budget range for this solution?",
            "budget_range": "How would you rate your team's technical expertise?",
            "technical_expertise": "Based on your answers, I have a recommendation. Would you like to see it?",
            "recommendation": "Would you like to explore any alternative options?",
            "complete": "Is there anything else you'd like to know about the recommended products?"
        }.get(var["value"]["current_state"], "No more questions."),
        
        "record_answer": lambda var, answer, reason=None: var["set"](
            {
                **var["value"],
                "customer_profile": {
                    **var["value"]["customer_profile"],
                    var["value"]["current_state"]: answer if var["value"]["current_state"] in var["value"]["customer_profile"] else var["value"]["customer_profile"]
                },
                "questions_asked": var["value"]["questions_asked"] + [var["value"]["current_state"]],
                "answers_received": {
                    **var["value"]["answers_received"],
                    var["value"]["current_state"]: answer
                },
                "state_flow": {
                    **var["value"]["state_flow"],
                    var["value"]["current_state"]: {
                        **var["value"]["state_flow"][var["value"]["current_state"]],
                        "asked": True
                    }
                }
            },
            reason or f"Recorded answer for {var['value']['current_state']}"
        ),
        
        "advance_state": lambda var, reason=None: var["set"](
            {
                **var["value"],
                "current_state": var["value"]["state_flow"][var["value"]["current_state"]]["next"]
            },
            reason or f"Advanced from {var['value']['current_state']} to {var['value']['state_flow'][var['value']['current_state']]['next']}"
        ),
        
        "generate_recommendation": lambda var: {
            # This is a simplified recommendation engine for example purposes
            # In reality, this would be more sophisticated
            "small_business_crm": {
                "match_conditions": {
                    "business_size": ["1-10", "11-50"],
                    "primary_need": ["customer management", "sales tracking"],
                    "budget_range": ["$0-5K", "$5K-15K"]
                },
                "product": "SmallBiz CRM",
                "price": "$99/month",
                "key_features": ["Contact Management", "Deal Tracking", "Email Integration"]
            },
            "enterprise_solution": {
                "match_conditions": {
                    "business_size": ["51-200", "201-1000", "1000+"],
                    "primary_need": ["customer management", "sales tracking", "analytics"],
                    "budget_range": ["$15K-50K", "$50K+"]
                },
                "product": "Enterprise Suite",
                "price": "$499/month",
                "key_features": ["Advanced Analytics", "Enterprise Integrations", "Dedicated Support"]
            },
            "marketing_automation": {
                "match_conditions": {
                    "primary_need": ["marketing automation", "lead generation"],
                    "industry": ["marketing", "e-commerce", "retail"]
                },
                "product": "MarketingPro",
                "price": "$299/month",
                "key_features": ["Email Campaigns", "Landing Pages", "Lead Scoring"]
            }
            # Additional product definitions would go here
        },
        
        "calculate_recommendation": lambda var, reason=None: 
            var["set"](
                {
                    **var["value"],
                    "recommendation": calculate_best_product_match(var["value"]["customer_profile"], var["apply_rule"]("generate_recommendation")),
                    "alternative_options": calculate_alternative_matches(var["value"]["customer_profile"], var["apply_rule"]("generate_recommendation")),
                    "confidence": calculate_match_confidence(var["value"]["customer_profile"], var["apply_rule"]("generate_recommendation"))
                },
                reason or "Calculated product recommendation"
            )
    }
)

# Helper functions for recommendation engine
def calculate_best_product_match(profile, products):
    """Calculate the best product match based on customer profile"""
    # Simplified scoring algorithm for example purposes
    scores = {}
    
    for product_id, product_info in products.items():
        score = 0
        match_conditions = product_info["match_conditions"]
        
        for criterion, acceptable_values in match_conditions.items():
            if profile.get(criterion) in acceptable_values:
                score += 1
                
        scores[product_id] = score
    
    best_match = max(scores.items(), key=lambda x: x[1])
    return {**products[best_match[0]], "id": best_match[0], "match_score": best_match[1]}

def calculate_alternative_matches(profile, products):
    """Calculate alternative product matches"""
    scores = {}
    
    for product_id, product_info in products.items():
        score = 0
        match_conditions = product_info["match_conditions"]
        
        for criterion, acceptable_values in match_conditions.items():
            if profile.get(criterion) in acceptable_values:
                score += 1
                
        scores[product_id] = score
    
    # Get top 2 alternatives that aren't the best match
    sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    best_match_id = sorted_scores[0][0]
    
    alternatives = []
    for product_id, score in sorted_scores[1:3]:  # Get next 2 best matches
        alternatives.append({**products[product_id], "id": product_id, "match_score": score})
        
    return alternatives

def calculate_match_confidence(profile, products):
    """Calculate confidence level in the recommendation"""
    # Simplified confidence calculation for example purposes
    fields_completed = sum(1 for value in profile.values() if value is not None)
    total_fields = len(profile)
    
    return round(fields_completed / total_fields * 100)

Business Application: For product recommendations or complex questionnaires, this approach guides the conversation flow:

Human: "I need help choosing the right software for my business."
AI: [Uses PRODUCT_RECOMMENDATION.apply_rule("get_next_question") to ask about business size]
Human: "We have about 30 employees."
AI: [Records with PRODUCT_RECOMMENDATION.apply_rule("record_answer", "11-50")]
AI: [Advances state with PRODUCT_RECOMMENDATION.apply_rule("advance_state")]
AI: [Asks next question about industry from PRODUCT_RECOMMENDATION.apply_rule("get_next_question")]
Human: "We're in the marketing industry."
AI: [Records answer and continues through the questionnaire]
Human: "Now that I've answered all your questions, what do you recommend?"
AI: [Calculates with PRODUCT_RECOMMENDATION.apply_rule("calculate_recommendation")]
AI: [Presents recommendation and alternatives]

This dynamic variable manages the entire conversation flow, tracking what's been asked, storing answers, and using logic to calculate recommendations based on the accumulated data.

Advanced Examples and Applications

Example 1: Collaborative Dashboard Variable

A dynamic variable that tracks real-time project collaboration and team activities:

python

# Create a dynamic dashboard for team collaboration
TEAM_DASHBOARD = create_dynamic_variable(
    "TEAM_DASHBOARD",
    {
        "project_name": "Q2 Marketing Campaign",
        "team_members": [
            {"name": "Alex", "role": "Project Manager", "tasks_assigned": 3, "tasks_completed": 1},
            {"name": "Jordan", "role": "Designer", "tasks_assigned": 5, "tasks_completed": 2},
            {"name": "Taylor", "role": "Content Writer", "tasks_assigned": 4, "tasks_completed": 0},
            {"name": "Morgan", "role": "Developer", "tasks_assigned": 2, "tasks_completed": 0}
        ],
        "tasks": [
            {"id": "T-001", "title": "Campaign Strategy", "assignee": "Alex", "status": "in_progress", "due_date": "2025-05-15", "priority": "high"},
            {"id": "T-002", "title": "Visual Mockups", "assignee": "Jordan", "status": "in_progress", "due_date": "2025-05-18", "priority": "high"},
            {"id": "T-003", "title": "Copy Development", "assignee": "Taylor", "status": "not_started", "due_date": "2025-05-20", "priority": "medium"},
            {"id": "T-004", "title": "Landing Page Development", "assignee": "Morgan", "status": "not_started", "due_date": "2025-05-25", "priority": "medium"},
            {"id": "T-005", "title": "Email Template Design", "assignee": "Jordan", "status": "in_progress", "due_date": "2025-05-22", "priority": "medium"},
            {"id": "T-006", "title": "Analytics Setup", "assignee": "Alex", "status": "not_started", "due_date": "2025-05-28", "priority": "low"},
            {"id": "T-007", "title": "Social Media Assets", "assignee": "Jordan", "status": "completed", "due_date": "2025-05-10", "priority": "high"},
            {"id": "T-008", "title": "Campaign Budget Approval", "assignee": "Alex", "status": "completed", "due_date": "2025-05-08", "priority": "high"},
            {"id": "T-009", "title": "Blog Content", "assignee": "Taylor", "status": "not_started", "due_date": "2025-05-24", "priority": "medium"},
            {"id": "T-010", "title": "Social Media Posts", "assignee": "Taylor", "status": "not_started", "due_date": "2025-05-26", "priority": "medium"},
            {"id": "T-011", "title": "A/B Test Setup", "assignee": "Morgan", "status": "not_started", "due_date": "2025-05-29", "priority": "low"},
            {"id": "T-012", "title": "Email Banners", "assignee": "Jordan", "status": "completed", "due_date": "2025-05-12", "priority": "medium"},
            {"id": "T-013", "title": "SEO Optimization", "assignee": "Taylor", "status": "not_started", "due_date": "2025-05-27", "priority": "low"},
            {"id": "T-014", "title": "Campaign Tracking", "assignee": "Morgan", "status": "not_started", "due_date": "2025-05-30", "priority": "low"}
        ],
        "milestones": [
            {"title": "Planning Phase Complete", "date": "2025-05-15", "status": "in_progress"},
            {"title": "Assets Ready", "date": "2025-05-22", "status": "not_started"},
            {"title": "Campaign Launch", "date": "2025-06-01", "status": "not_started"},
            {"title": "Mid-Campaign Assessment", "date": "2025-06-15", "status": "not_started"}
        ],
        "activity_log": [
            {"timestamp": "2025-05-08 10:30", "user": "Alex", "action": "Completed task T-008 (Campaign Budget Approval)"},
            {"timestamp": "2025-05-10 14:45", "user": "Jordan", "action": "Completed task T-007 (Social Media Assets)"},
            {"timestamp": "2025-05-11 09:15", "user": "Alex", "action": "Created task T-013 (SEO Optimization) assigned to Taylor"},
            {"timestamp": "2025-05-12 11:20", "user": "Jordan", "action": "Completed task T-012 (Email Banners)"}
        ],
        "last_updated": "2025-05-12 11:20"
    },
    {
        "update_task_status": lambda var, task_id, new_status, reason=None: var["set"](
            {
                **var["value"],
                "tasks": [
                    {**task, 
                     "status": new_status,
                     "completion_date": datetime.now().strftime("%Y-%m-%d") if new_status == "completed" and task["status"] != "completed" else task.get("completion_date")
                    } if task["id"] == task_id else task
                    for task in var["value"]["tasks"]
                ],
                "team_members": [
                    {**member, 
                     "tasks_completed": member["tasks_completed"] + (1 if new_status == "completed" and get_task_by_id(var["value"]["tasks"], task_id)["assignee"] == member["name"] and get_task_by_id(var["value"]["tasks"], task_id)["status"] != "completed" else 0)
                    } if member["name"] == get_task_by_id(var["value"]["tasks"], task_id)["assignee"] else member
                    for member in var["value"]["team_members"]
                ],
                "activity_log": [
                    {"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"), 
                     "user": get_task_by_id(var["value"]["tasks"], task_id)["assignee"], 
                     "action": f"Changed task {task_id} ({get_task_by_id(var['value']['tasks'], task_id)['title']}) status to {new_status}"
                    }
                ] + var["value"]["activity_log"],
                "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M")
            },
            reason or f"Updated task {task_id} status to {new_status}"
        ),
        
        "add_task": lambda var, title, assignee, due_date, priority="medium", reason=None: var["set"](
            {
                **var["value"],
                "tasks": var["value"]["tasks"] + [
                    {"id": f"T-{str(len(var['value']['tasks']) + 1).zfill(3)}", 
                     "title": title, 
                     "assignee": assignee, 
                     "status": "not_started", 
                     "due_date": due_date, 
                     "priority": priority
                    }
                ],
                "team_members": [
                    {**member, 
                     "tasks_assigned": member["tasks_assigned"] + (1 if member["name"] == assignee else 0)
                    }
                    for member in var["value"]["team_members"]
                ],
                "activity_log": [
                    {"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"), 
                     "user": "System", 
                     "action": f"Created new task: {title}, assigned to {assignee}"
                    }
                ] + var["value"]["activity_log"],
                "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M")
            },
            reason or f"Added new task: {title}"
        ),
        
        "generate_status_report": lambda var: {
            "project_name": var["value"]["project_name"],
            "completion_percentage": round(
                sum(1 for task in var["value"]["tasks"] if task["status"] == "completed") / 
                len(var["value"]["tasks"]) * 100
            ),
            "team_productivity": {
                member["name"]: {
                    "completion_rate": f"{round(member['tasks_completed'] / max(1, member['tasks_assigned']) * 100)}%" if member["tasks_assigned"] > 0 else "N/A",
                    "remaining_tasks": member["tasks_assigned"] - member["tasks_completed"]
                }
                for member in var["value"]["team_members"]
            },
            "upcoming_deadlines": [
                {"id": task["id"], "title": task["title"], "assignee": task["assignee"], "due_date": task["due_date"]}
                for task in var["value"]["tasks"]
                if task["status"] != "completed" and 
                   (datetime.strptime(task["due_date"], "%Y-%m-%d") - datetime.now()).days <= 7
            ],
            "status_breakdown": {
                "not_started": sum(1 for task in var["value"]["tasks"] if task["status"] == "not_started"),
                "in_progress": sum(1 for task in var["value"]["tasks"] if task["status"] == "in_progress"),
                "completed": sum(1 for task in var["value"]["tasks"] if task["status"] == "completed")
            },
            "priority_breakdown": {
                "high": sum(1 for task in var["value"]["tasks"] if task["priority"] == "high" and task["status"] != "completed"),
                "medium": sum(1 for task in var["value"]["tasks"] if task["priority"] == "medium" and task["status"] != "completed"),
                "low": sum(1 for task in var["value"]["tasks"] if task["priority"] == "low" and task["status"] != "completed")
            },
            "next_milestone": next(
                (milestone for milestone in var["value"]["milestones"] if milestone["status"] != "completed"),
                {"title": "No upcoming milestones", "date": "N/A"}
            ),
            "recent_activity": var["value"]["activity_log"][:5]
        }
    }
)

# Helper function for task lookup
def get_task_by_id(tasks, task_id):
    """Get a task by its ID"""
    for task in tasks:
        if task["id"] == task_id:
            return task
    return None

Business Application: For team project management, this variable creates a real-time dashboard:

Human: "Jordan has completed the visual mockups task."
AI: [Updates TEAM_DASHBOARD.apply_rule("update_task_status", "T-002", "completed")]
Human: "We need to add a new task for competitive analysis, assigned to Alex, due next week."
AI: [Adds task with TEAM_DASHBOARD.apply_rule("add_task", "Competitive Analysis", "Alex", (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d"), "high")]
Human: "Generate a status report for today's team meeting."
AI: [Provides TEAM_DASHBOARD.apply_rule("generate_status_report")]

This dashboard variable automatically tracks team productivity, task completion rates, upcoming deadlines, and project progress, acting as a central coordination hub that updates in real-time as new information emerges in the conversation.

Example 2: Evolutionary Strategy Variable

A dynamic variable that adapts business strategy based on changing market conditions:

python

# Create a dynamic variable for adaptive business strategy
BUSINESS_STRATEGY = create_dynamic_variable(
    "BUSINESS_STRATEGY",
    {
        "current_phase": "market_validation",
        "phase_history": [
            {"phase": "ideation", "started": "2025-01-15", "completed": "2025-03-10", "key_outcomes": ["Identified target market", "Developed initial value proposition", "Created MVP specification"]}
        ],
        "market_conditions": {
            "competitive_landscape": "emerging",  # emerging, established, saturated
            "market_growth": "high",  # high, moderate, low
            "regulatory_environment": "favorable",  # favorable, neutral, restrictive
            "economic_outlook": "positive"  # positive, neutral, negative
        },
        "strategic_priorities": [
            {"id": "SP-001", "area": "product_development", "description": "Develop MVP with core functionality", "status": "completed"},
            {"id": "SP-002", "area": "customer_acquisition", "description": "Implement initial customer acquisition channels", "status": "in_progress"},
            {"id": "SP-003", "area": "market_validation", "description": "Validate product-market fit with early adopters", "status": "in_progress"},
            {"id": "SP-004", "area": "funding", "description": "Secure seed funding round", "status": "not_started"}
        ],
        "key_metrics": {
            "customer_acquisition_cost": 250,
            "customer_lifetime_value": 1200,
            "monthly_recurring_revenue": 45000,
            "churn_rate": 0.08,
            "active_users": 350
        },
        "strategic_options": {
            "growth": {
                "description": "Focus on rapid market expansion and user acquisition",
                "suitable_conditions": {
                    "market_growth": ["high", "moderate"],
                    "competitive_landscape": ["emerging"],
                    "economic_outlook": ["positive", "neutral"]
                },
                "resource_allocation": {
                    "marketing": 0.40,
                    "product_development": 0.30,
                    "operations": 0.15,
                    "sales": 0.15
                },
                "key_initiatives": [
                    "Increase marketing budget by 50%",
                    "Expand to adjacent markets",
                    "Accelerate feature development",
                    "Pursue strategic partnerships"
                ]
            },
            "optimization": {
                "description": "Focus on improving unit economics and operational efficiency",
                "suitable_conditions": {
                    "market_growth": ["moderate", "low"],
                    "competitive_landscape": ["established", "saturated"],
                    "economic_outlook": ["neutral", "negative"]
                },
                "resource_allocation": {
                    "marketing": 0.20,
                    "product_development": 0.25,
                    "operations": 0.35,
                    "sales": 0.20
                },
                "key_initiatives": [
                    "Reduce customer acquisition cost by 20%",
                    "Improve retention metrics",
                    "Streamline operational processes",
                    "Optimize pricing strategy"
                ]
            },
            "innovation": {
                "description": "Focus on differentiation through new product development",
                "suitable_conditions": {
                    "market_growth": ["high", "moderate"],
                    "competitive_landscape": ["established", "saturated"],
                    "economic_outlook": ["positive", "neutral"]
                },
                "resource_allocation": {
                    "marketing": 0.20,
                    "product_development": 0.50,
                    "operations": 0.15,
                    "sales": 0.15
                },
                "key_initiatives": [
                    "Increase R&D investment by 35%",
                    "Develop new product verticals",
                    "Acquire or partner for technology access",
                    "Build innovation-focused team"
                ]
            }
        },
        "current_strategy": "growth",
        "adaptation_history": []
    },
    {
        "advance_phase": lambda var, new_phase, key_outcomes, reason=None: var["set"](
            {
                **var["value"],
                "current_phase": new_phase,
                "phase_history": var["value"]["phase_history"] + [
                    {"phase": var["value"]["current_phase"], 
                     "started": var["value"]["phase_history"][-1]["completed"] if var["value"]["phase_history"] else "2025-01-01", 
                     "completed": datetime.now().strftime("%Y-%m-%d"), 
                     "key_outcomes": key_outcomes
                    }
                ]
            },
            reason or f"Advanced from {var['value']['current_phase']} to {new_phase} phase"
        ),
        
        "update_market_conditions": lambda var, condition, new_value, reason=None: var["set"](
            {
                **var["value"],
                "market_conditions": {
                    **var["value"]["market_conditions"],
                    condition: new_value
                }
            },
            reason or f"Updated market condition: {condition} = {new_value}"
        ),
        
        "update_metrics": lambda var, metric, new_value, reason=None: var["set"](
            {
                **var["value"],
                "key_metrics": {
                    **var["value"]["key_metrics"],
                    metric: new_value
                }
            },
            reason or f"Updated key metric: {metric} = {new_value}"
        ),
        
        "evaluate_strategy": lambda var: {
            option: sum(
                1 for condition, values in strategy_info["suitable_conditions"].items()
                if var["value"]["market_conditions"].get(condition) in values
            ) / len(strategy_info["suitable_conditions"])
            for option, strategy_info in var["value"]["strategic_options"].items()
        },
        
        "adapt_strategy": lambda var, reason=None:
            var["set"](
                {
                    **var["value"],
                    "current_strategy": max(var["apply_rule"]("evaluate_strategy").items(), key=lambda x: x[1])[0],
                    "adaptation_history": var["value"]["adaptation_history"] + [
                        {"date": datetime.now().strftime("%Y-%m-%d"),
                         "previous_strategy": var["value"]["current_strategy"],
                         "new_strategy": max(var["apply_rule"]("evaluate_strategy").items(), key=lambda x: x[1])[0],
                         "market_conditions": var["value"]["market_conditions"],
                         "key_metrics": var["value"]["key_metrics"]
                        }
                    ]
                },
                reason or "Adapted strategy based on current conditions"
            ) if max(var["apply_rule"]("evaluate_strategy").items(), key=lambda x: x[1])[0] != var["value"]["current_strategy"] else "Strategy remains optimal for current conditions",
        
        "generate_strategic_recommendations": lambda var: {
            "current_strategy": var["value"]["current_strategy"],
            "strategy_fit_score": max(var["apply_rule"]("evaluate_strategy").items(), key=lambda x: x[1])[1],
            "recommended_resource_allocation": var["value"]["strategic_options"][var["value"]["current_strategy"]]["resource_allocation"],
            "key_initiatives": var["value"]["strategic_options"][var["value"]["current_strategy"]]["key_initiatives"],
            "strategic_priorities": [
                {"id": priority["id"], "area": priority["area"], "description": priority["description"]}
                for priority in var["value"]["strategic_priorities"]
                if priority["status"] != "completed"
            ],
            "phase_recommendation": suggest_phase_progression(var["value"]["current_phase"], var["value"]["key_metrics"]),
            "metric_insights": analyze_key_metrics(var["value"]["key_metrics"]),
            "market_opportunities": identify_opportunities(var["value"]["market_conditions"], var["value"]["current_strategy"])
        }
    }
)

# Helper functions
def suggest_phase_progression(current_phase, metrics):
    """Suggest when to move to the next business phase"""
    phases = ["ideation", "market_validation", "growth", "expansion", "maturity"]
    
    # Simplified phase progression logic for example purposes
    if current_phase == "ideation":
        return "Ready to progress to market validation"
    elif current_phase == "market_validation" and metrics["active_users"] > 300:
        return "Consider advancing to growth phase as active users exceed 300"
    elif current_phase == "growth" and metrics["monthly_recurring_revenue"] > 100000:
        return "Consider advancing to expansion phase as MRR exceeds $100K"
    elif current_phase == "expansion" and metrics["monthly_recurring_revenue"] > 500000:
        return "Consider advancing to maturity phase as MRR exceeds $500K"
    else:
        return f"Continue executing on {current_phase} phase priorities"

def analyze_key_metrics(metrics):
    """Analyze key business metrics for insights"""
    insights = []
    
    # Simplified analysis logic for example purposes
    if metrics["customer_lifetime_value"] / metrics["customer_acquisition_cost"] < 3:
        insights.append("LTV:CAC ratio below target of 3:1 - consider improving retention or reducing acquisition costs")
    else:
        insights.append(f"Healthy LTV:CAC ratio of {metrics['customer_lifetime_value'] / metrics['customer_acquisition_cost']:.1f}:1")
    
    if metrics["churn_rate"] > 0.05:
        insights.append(f"Churn rate of {metrics['churn_rate']:.1%} exceeds target of 5% - prioritize retention initiatives")
    
    if metrics["monthly_recurring_revenue"] / metrics["active_users"] < 100:
        insights.append("Average revenue per user below $100 benchmark - consider pricing optimization")
    
    return insights

def identify_opportunities(market_conditions, current_strategy):
    """Identify strategic opportunities based on market conditions"""
    opportunities = []
    
    # Simplified opportunity identification for example purposes
    if market_conditions["market_growth"] == "high" and current_strategy != "growth":
        opportunities.append("Market growth is high - consider shifting more resources to expansion")
    
    if market_conditions["competitive_landscape"] == "saturated" and current_strategy != "innovation":
        opportunities.append("Market is saturated - increased differentiation through innovation may be necessary")
    
    if market_conditions["economic_outlook"] == "negative" and current_strategy != "optimization":
        opportunities.append("Economic outlook is negative - consider focusing on efficiency and profitability")
    
    if market_conditions["regulatory_environment"] == "favorable" and current_strategy == "growth":
        opportunities.append("Favorable regulatory environment - accelerate expansion before conditions change")
    
    return opportunities

Business Application: For strategic planning, this dynamic variable helps entrepreneurs adapt to changing conditions:

Human: "Our most recent data shows that market growth has slowed to moderate levels."
AI: [Updates BUSINESS_STRATEGY.apply_rule("update_market_conditions", "market_growth", "moderate")]
Human: "The competitive landscape is now established with several major players."
AI: [Updates BUSINESS_STRATEGY.apply_rule("update_market_conditions", "competitive_landscape", "established")]
Human: "Our active users have grown to 450, with monthly recurring revenue of $62,000."
AI: [Updates metrics and then evaluates with BUSINESS_STRATEGY.apply_rule("adapt_strategy")]
Human: "Based on these changes, what strategic adjustments should we make?"
AI: [Provides recommendations from BUSINESS_STRATEGY.apply_rule("generate_strategic_recommendations")]

This approach creates a living strategic framework that evolves with the business, automatically suggesting shifts in resource allocation, priorities, and initiatives as market conditions and business metrics change.

Implementation in CRAFT Framework

To integrate dynamic variables into your CRAFT framework effectively, consider:

Creating a dynamic variable base class with standard methods for state tracking and updates
Implementing event triggers that can automatically update variables based on conversation events
Developing a variable state persistence mechanism to maintain state across multiple AI sessions
Building visualization tools to track variable evolution over time

This approach would significantly enhance the CRAFT framework's ability to maintain coherent, evolving conversations, aligning perfectly with your goal of creating cumulative intelligence across AI interactions.

The examples above demonstrate how dynamic variables can transform AI interactions from stateless exchanges into progressive, context-aware conversations that build upon each other, creating a truly collaborative experience for entrepreneurs.

Variable Inheritance in CRAFT

Understanding Variable Inheritance

What Is Variable Inheritance?

Variable inheritance is a powerful concept borrowed from object-oriented programming (OOP) that allows new variables to inherit and extend the properties, structures, and behaviors of existing variables. This creates a parent-child relationship between variables, where child variables automatically receive all the characteristics of their parent while having the ability to override or extend these characteristics.

Unlike traditional variables that are created independently of each other, inherited variables establish a hierarchical relationship that enables:

Creating specialized versions of existing variables
Extending content without duplicating common elements
Establishing consistent patterns with room for variation
Building complex hierarchies of related variables
Centralizing updates through parent-child relationships

Think of variable inheritance as creating variable "families" where child variables share DNA with their parent but can develop their own unique features – similar to how all smartphones share core functionality but different models add their own specialized capabilities.

Core Concepts Behind Variable Inheritance

Variable inheritance aligns with several key philosophical principles from your CRAFT framework:

Structure Enables Freedom: Inheritance creates predictable structures that make it easier to create variations without reinventing the wheel.
Code as Conversation: Borrowing this programming concept makes relationships between related content more explicit and easier to understand.
Conservation of Tokens: Inheritance eliminates repetition by allowing child variables to reference parent structures rather than duplicating them.

Technical Implementation

At a basic level, inheritance can be implemented through extension functions that combine parent and child properties:

python

def inherit(parent_variable, extensions=None, overrides=None):
    """Create a new variable that inherits from a parent variable."""
    # Start with a deep copy of the parent
    if isinstance(parent_variable, dict):
        result = copy.deepcopy(parent_variable)
        
        # Apply extensions (add new properties)
        if extensions and isinstance(extensions, dict):
            for key, value in extensions.items():
                if key not in result:
                    result[key] = value
                elif isinstance(result[key], dict) and isinstance(value, dict):
                    result[key] = {**result[key], **value}
                elif isinstance(result[key], list) and isinstance(value, list):
                    result[key] = result[key] + value
                else:
                    result[key] = value
        
        # Apply overrides (replace existing properties)
        if overrides and isinstance(overrides, dict):
            for key, value in overrides.items():
                result[key] = value
                
        return result
    elif isinstance(parent_variable, list):
        return copy.deepcopy(parent_variable) + (extensions or [])
    elif isinstance(parent_variable, str):
        return parent_variable + "\n\n" + (extensions or "")
    else:
        # For primitive types, just override with extensions if provided
        return extensions if extensions is not None else copy.deepcopy(parent_variable)

More sophisticated implementations might include:

Multi-level inheritance chains
Multiple inheritance from several parents
Abstract variables (templates that must be extended)
Interface-like contracts for variable structures
Polymorphic behavior across variable families

Benefits of Variable Inheritance

1. Template Customization Without Repetition

Benefit: Create specialized versions of common templates without duplicating shared elements.

Example:

python

# Define a base email template
BASE_EMAIL_TEMPLATE = {
    "structure": {
        "greeting": "Dear {recipient_name},",
        "intro": "I hope this email finds you well.",
        "body": "{main_content}",
        "closing": "Looking forward to your response.",
        "signature": "Best regards,\n{sender_name}\n{sender_title}\n{company_name}"
    },
    "styling": {
        "font_family": "Arial, sans-serif",
        "font_size": "12pt",
        "color_scheme": "professional"
    },
    "metadata": {
        "type": "email",
        "version": "1.0",
        "last_updated": "2025-05-01"
    },
    "render": lambda template, context: "\n\n".join([
        template["structure"]["greeting"].format(**context),
        template["structure"]["intro"],
        template["structure"]["body"].format(**context),
        template["structure"]["closing"],
        template["structure"]["signature"].format(**context)
    ])
}

# Create specialized email templates through inheritance
SALES_FOLLOW_UP_EMAIL = inherit(
    BASE_EMAIL_TEMPLATE,
    extensions={
        "structure": {
            "intro": "Thank you for taking the time to meet with me yesterday to discuss how {company_name} can help {recipient_company} streamline your {topic} processes.",
            "closing": "I've attached additional information about our {product_name} solution. Would you be available for a quick follow-up call this week to address any questions?"
        },
        "subject_line": "Following up on our {topic} discussion | {company_name}",
        "recommended_timing": "1-2 business days after initial meeting"
    }
)

MEETING_REQUEST_EMAIL = inherit(
    BASE_EMAIL_TEMPLATE,
    extensions={
        "structure": {
            "intro": "I'm reaching out to request a meeting to discuss {topic} and how {company_name} might help {recipient_company} achieve your objectives in this area.",
            "body": "Based on {context}, I believe our {product_name} solution could provide significant value by {value_proposition}.\n\nI would appreciate the opportunity to discuss this further at your convenience. Would you be available for a {meeting_duration}-minute {meeting_type} meeting on {proposed_date} at {proposed_time}?",
            "closing": "If this time doesn't work for you, please let me know what would be more convenient for your schedule."
        },
        "subject_line": "Request to discuss {topic} | {company_name}",
        "meeting_options": {
            "durations": ["15", "30", "45", "60"],
            "types": ["phone", "video", "in-person"]
        }
    }
)

COLD_OUTREACH_EMAIL = inherit(
    BASE_EMAIL_TEMPLATE,
    extensions={
        "structure": {
            "intro": "I'm {sender_name} from {company_name}, and we specialize in helping companies like {recipient_company} {value_proposition}.",
            "body": "Based on {research_point}, I thought you might be interested in how we've helped similar organizations:\n\n- {case_study_1}\n- {case_study_2}\n\nWe've consistently delivered {key_benefit}, resulting in {typical_result}.",
            "closing": "I'd love to share more about how we might be able to help {recipient_company}. Would you be open to a brief conversation next week?"
        },
        "subject_line": "{personalized_hook} for {recipient_company}",
        "best_practices": [
            "Research the recipient thoroughly before sending",
            "Personalize the first line with a specific observation",
            "Keep the email under 200 words",
            "Include specific results from similar companies"
        ]
    },
    overrides={
        "structure": {
            "greeting": "Hi {recipient_first_name},"
        }
    }
)

Business Application: When communicating with prospects and customers, entrepreneurs can maintain consistent branding while adapting to specific scenarios:

Human: "I need to send a follow-up email after yesterday's demo with Acme Corp."
AI: [Uses SALES_FOLLOW_UP_EMAIL structure to create a consistent, properly formatted email]
Human: "Now I need to reach out to a new prospect at TechStart Inc."
AI: [Uses COLD_OUTREACH_EMAIL, which inherits the same professional structure with appropriate modifications]

This approach ensures all communications follow brand guidelines and include necessary elements, while allowing for appropriate customization for each specific communication scenario.

2. Content Specialization and Extension

Benefit: Build upon existing content by adding specialized information without altering the original structure.

Example:

python

# Define a base product description
BASE_PRODUCT = {
    "name": "TaskMaster Pro",
    "category": "Productivity Software",
    "core_features": [
        {"name": "Task Management", "description": "Create, organize, and prioritize tasks"},
        {"name": "Team Collaboration", "description": "Share tasks and projects with team members"},
        {"name": "Progress Tracking", "description": "Monitor completion status and deadlines"}
    ],
    "benefits": [
        "Improve team productivity",
        "Reduce missed deadlines",
        "Enhance project visibility"
    ],
    "pricing": {
        "model": "subscription",
        "currency": "USD",
        "plans": [
            {"name": "Basic", "price": 9.99, "billing": "monthly", "users": 1},
            {"name": "Team", "price": 49.99, "billing": "monthly", "users": 5},
            {"name": "Business", "price": 199.99, "billing": "monthly", "users": 25}
        ]
    },
    "get_summary": lambda product: f"{product['name']} is a {product['category']} that helps you {product['core_features'][0]['description'].lower()}. Starting at ${product['pricing']['plans'][0]['price']:.2f} per month."
}

# Create specialized product variants through inheritance
TASKMASTER_PRO_HEALTHCARE = inherit(
    BASE_PRODUCT,
    extensions={
        "name": "TaskMaster Pro for Healthcare",
        "industry_specific_features": [
            {"name": "HIPAA Compliance", "description": "Secure patient data handling and audit trails"},
            {"name": "Medical Scheduling", "description": "Specialized calendar for patient appointments"},
            {"name": "Clinical Workflows", "description": "Templates for common healthcare procedures"}
        ],
        "case_studies": [
            {"client": "City Medical Center", "outcome": "Reduced administrative time by 32%"},
            {"client": "Westside Clinic Group", "outcome": "Improved patient scheduling efficiency by 45%"}
        ],
        "compliance": ["HIPAA", "HITECH", "SOC 2"],
        "get_industry_pitch": lambda product: f"Designed specifically for healthcare providers, {product['name']} combines essential task management with {len(product['industry_specific_features'])} healthcare-specific features including {product['industry_specific_features'][0]['name']} and {product['industry_specific_features'][1]['name']}."
    },
    overrides={
        "pricing": {
            "model": "subscription",
            "currency": "USD",
            "plans": [
                {"name": "Provider", "price": 19.99, "billing": "monthly", "users": 1},
                {"name": "Clinic", "price": 79.99, "billing": "monthly", "users": 5},
                {"name": "Hospital", "price": 299.99, "billing": "monthly", "users": 25}
            ]
        }
    }
)

TASKMASTER_PRO_EDUCATION = inherit(
    BASE_PRODUCT,
    extensions={
        "name": "TaskMaster Pro for Education",
        "industry_specific_features": [
            {"name": "Assignment Tracking", "description": "Manage student assignments and grading workflows"},
            {"name": "Classroom Management", "description": "Organize tasks by classes and student groups"},
            {"name": "Academic Calendar", "description": "Sync with school year and academic deadlines"}
        ],
        "case_studies": [
            {"client": "Westfield High School", "outcome": "97% reduction in missed assignment submissions"},
            {"client": "Riverdale School District", "outcome": "Teachers saved average of 5 hours weekly on administrative tasks"}
        ],
        "integrations": ["Google Classroom", "Canvas", "Blackboard", "Microsoft Education"],
        "discount_programs": {
            "k12": "40% off all plans",
            "higher_education": "25% off all plans",
            "student": "Free for individual students"
        }
    }
)

Business Application: When creating industry-specific marketing materials, entrepreneurs can maintain core product information while showcasing specialized benefits:

Human: "We need to create a product page for our healthcare version."
AI: [Uses TASKMASTER_PRO_HEALTHCARE, which combines core features with healthcare-specific extensions]
Human: "Let's also prepare a pitch for educational institutions."
AI: [Leverages TASKMASTER_PRO_EDUCATION with education-specific content while maintaining consistent core messaging]

This inherited structure ensures consistent presentation of the core product while allowing for industry-specific customization, maintaining brand cohesion across different market segments.

3. Centralized Updates and Maintenance

Benefit: Update parent variables to automatically propagate changes to all child variables, ensuring consistency without manual updates.

Example:

python

# Define base company information
COMPANY_INFORMATION = {
    "name": "InnovateTech Solutions",
    "founded": 2018,
    "headquarters": "Boston, MA",
    "mission": "To empower businesses with intelligent software solutions that drive efficiency and growth",
    "leadership": [
        {"name": "Sarah Chen", "title": "CEO", "bio": "Former VP at TechCorp, 15+ years in enterprise software"},
        {"name": "Michael Rodriguez", "title": "CTO", "bio": "Previously led engineering at CloudScale, AI/ML specialist"}
    ],
    "employees": 85,
    "locations": ["Boston", "San Francisco", "Austin"],
    "contact": {
        "general": "info@innovatetech.com",
        "support": "support@innovatetech.com",
        "press": "media@innovatetech.com",
        "phone": "+1 (617) 555-8900",
        "address": "100 Innovation Drive, Boston, MA 02110"
    },
    "social_media": {
        "linkedin": "linkedin.com/company/innovatetech",
        "twitter": "twitter.com/innovatetech",
        "facebook": "facebook.com/innovatetechsolutions"
    },
    "boilerplate": "InnovateTech Solutions is a leading software company specializing in AI-powered productivity tools for modern businesses. Founded in 2018, the company serves over 500 clients across diverse industries, helping them streamline operations and unlock new efficiencies."
}

# Create inherited variables for different content types
COMPANY_WEBSITE_ABOUT = inherit(
    COMPANY_INFORMATION,
    extensions={
        "page_sections": [
            {"title": "Our Story", "content": "InnovateTech was founded in {founded} with a vision to transform how businesses operate through intelligent software..."},
            {"title": "Our Mission", "content": "{mission}"},
            {"title": "Leadership Team", "content": "Our company is led by industry veterans with deep expertise in enterprise software and artificial intelligence."},
            {"title": "Company Culture", "content": "At InnovateTech, we believe in fostering innovation through collaboration, diversity of thought, and continuous learning."}
        ],
        "testimonials": [
            {"quote": "InnovateTech's solutions have transformed our operations, saving us countless hours and reducing costs by 23%.", "source": "Jennifer Torres, COO, GlobalServices Inc."},
            {"quote": "The team at InnovateTech truly understands the challenges modern businesses face and delivers solutions that make a real difference.", "source": "David Kim, CIO, Nexus Healthcare"}
        ],
        "awards": [
            {"name": "Top 50 Most Innovative Companies", "organization": "TechForward Magazine", "year": 2024},
            {"name": "Best Workplace Culture", "organization": "EmployerChoice Awards", "year": 2023}
        ]
    }
)

PRESS_RELEASE_TEMPLATE = inherit(
    COMPANY_INFORMATION,
    extensions={
        "press_release_structure": {
            "headline": "[ANNOUNCEMENT]: {headline}",
            "dateline": "BOSTON, MA — {release_date}",
            "intro_paragraph": "{announcement_summary}",
            "quote_paragraph": ""{quote_text}" said {quote_attribution}.",
            "detail_paragraphs": "{announcement_details}",
            "about_company": "{boilerplate}",
            "contact_information": "Media Contact: {contact[press]}, {contact[phone]}"
        },
        "distribution_channels": [
            "Business Wire",
            "PR Newswire",
            "Company Website",
            "LinkedIn",
            "Twitter"
        ],
        "embargoed": False
    }
)

INVESTOR_RELATIONS = inherit(
    COMPANY_INFORMATION,
    extensions={
        "financial_highlights": {
            "revenue_growth": "42% YoY (2024)",
            "annual_recurring_revenue": "$12.5M (Q1 2025)",
            "gross_margin": "78%",
            "customer_retention": "94%"
        },
        "funding_history": [
            {"round": "Seed", "amount": "$1.2M", "date": "April 2018", "lead_investor": "Horizon Ventures"},
            {"round": "Series A", "amount": "$7.5M", "date": "January 2021", "lead_investor": "Ascend Capital Partners"},
            {"round": "Series B", "amount": "$18M", "date": "March 2024", "lead_investor": "Growth Ventures Fund"}
        ],
        "investor_contacts": {
            "primary": "Sarah Chen, CEO (sarah.chen@innovatetech.com)",
            "investor_relations": "James Wilson, CFO (james.wilson@innovatetech.com)"
        }
    }
)

Business Application: When company information changes, updates to the parent variable automatically propagate to all derived content:

Human: "We need to update our company information. We've grown to 110 employees and opened a new office in Chicago."
AI: [Updates COMPANY_INFORMATION with new employee count and adds Chicago to locations]
Human: "Let's prepare an updated About Us page for the website."
AI: [Uses COMPANY_WEBSITE_ABOUT which now automatically reflects the updated employee count and locations]
Human: "We also need to update our investor materials with this information."
AI: [Uses INVESTOR_RELATIONS which also inherits the updated company information]

This central update approach ensures consistency across all materials, eliminating the risk of outdated information appearing in some channels while being updated in others – a common challenge for growing businesses.

4. Multi-Level Specialization

Benefit: Create hierarchies of increasingly specialized variables to address specific use cases while maintaining inheritance chains.

Example:

python

# Base product documentation template
BASE_DOCUMENTATION = {
    "structure": {
        "title": "{product_name} Documentation",
        "introduction": "Welcome to the {product_name} documentation. This guide will help you get started with our product and explore its features.",
        "table_of_contents": ["Overview", "Getting Started", "Features", "API Reference", "Troubleshooting"],
        "sections": {
            "overview": {
                "title": "Overview",
                "content": "{product_name} is a {product_type} that helps you {primary_benefit}."
            },
            "getting_started": {
                "title": "Getting Started",
                "content": "This section guides you through the initial setup process.",
                "subsections": ["Installation", "Configuration", "First Steps"]
            },
            "features": {
                "title": "Features",
                "content": "Explore the key features of {product_name}.",
                "feature_list": []
            },
            "api_reference": {
                "title": "API Reference",
                "content": "Detailed information about the {product_name} API.",
                "endpoints": []
            },
            "troubleshooting": {
                "title": "Troubleshooting",
                "content": "Common issues and their solutions.",
                "common_issues": []
            }
        },
        "footer": "© {current_year} {company_name}. All rights reserved."
    },
    "styling": {
        "theme": "light",
        "primary_color": "#3366cc",
        "font_family": "Inter, sans-serif",
        "code_highlighting": True
    },
    "metadata": {
        "version": "1.0.0",
        "last_updated": "2025-05-01",
        "contributors": []
    },
    "render_format": "HTML"
}

# First level inheritance: Documentation type
DEVELOPER_DOCUMENTATION = inherit(
    BASE_DOCUMENTATION,
    extensions={
        "audience": "developers",
        "prerequisites": ["Programming knowledge", "API familiarity"],
        "structure": {
            "sections": {
                "architecture": {
                    "title": "Architecture",
                    "content": "Technical overview of {product_name} architecture.",
                    "subsections": ["Components", "Data Flow", "Integration Points"]
                },
                "api_authentication": {
                    "title": "Authentication",
                    "content": "Learn how to authenticate with the {product_name} API.",
                    "authentication_methods": ["API Key", "OAuth 2.0", "JWT"]
                }
            }
        },
        "code_examples": {
            "languages": ["JavaScript", "Python", "Ruby", "Java", "Go"],
            "examples": {}
        }
    }
)

# Second level inheritance: Specific API documentation
REST_API_DOCUMENTATION = inherit(
    DEVELOPER_DOCUMENTATION,
    extensions={
        "api_type": "REST",
        "structure": {
            "sections": {
                "api_reference": {
                    "title": "REST API Reference",
                    "content": "Detailed information about the {product_name} REST API endpoints.",
                    "conventions": {
                        "http_methods": ["GET", "POST", "PUT", "DELETE"],
                        "status_codes": [200, 201, 400, 401, 403, 404, 500],
                        "response_format": "JSON"
                    }
                },
                "rate_limiting": {
                    "title": "Rate Limiting",
                    "content": "Understanding API rate limits and quotas.",
                    "limits": {
                        "default": "100 requests per minute",
                        "premium": "500 requests per minute"
                    }
                }
            }
        },
        "code_examples": {
            "rest_patterns": {
                "authentication": "curl -H \"Authorization: Bearer {api_key}\" {base_url}/endpoint",
                "get_request": "curl {base_url}/endpoint",
                "post_request": "curl -X POST -H \"Content-Type: application/json\" -d '{\"key\": \"value\"}' {base_url}/endpoint"
            }
        }
    }
)

# Third level inheritance: Product-specific API documentation
TASKMASTER_API_DOCUMENTATION = inherit(
    REST_API_DOCUMENTATION,
    extensions={
        "product_name": "TaskMaster Pro",
        "product_type": "task management solution",
        "primary_benefit": "streamline team workflows and increase productivity",
        "base_url": "https://api.taskmasterpro.com/v1",
        "structure": {
            "sections": {
                "api_reference": {
                    "endpoints": [
                        {
                            "name": "List Tasks",
                            "method": "GET",
                            "endpoint": "/tasks",
                            "description": "Retrieve a list of tasks",
                            "parameters": [
                                {"name": "status", "type": "string", "description": "Filter tasks by status (open, in_progress, completed)"},
                                {"name": "assignee", "type": "string", "description": "Filter tasks by assignee ID"}
                            ],
                            "response_example": "{\n  \"tasks\": [\n    {\n      \"id\": \"task_123\",\n      \"title\": \"Complete API documentation\",\n      \"status\": \"in_progress\",\n      \"assignee\": \"user_456\"\n    }\n  ]\n}"
                        },
                        {
                            "name": "Create Task",
                            "method": "POST",
                            "endpoint": "/tasks",
                            "description": "Create a new task",
                            "request_example": "{\n  \"title\": \"New task\",\n  \"description\": \"Task description\",\n  \"assignee\": \"user_456\"\n}",
                            "response_example": "{\n  \"id\": \"task_789\",\n  \"title\": \"New task\",\n  \"description\": \"Task description\",\n  \"status\": \"open\",\n  \"assignee\": \"user_456\"\n}"
                        }
                    ]
                }
            }
        },
        "code_examples": {
            "examples": {
                "javascript": {
                    "list_tasks": "fetch('https://api.taskmasterpro.com/v1/tasks', {\n  headers: {\n    'Authorization': 'Bearer YOUR_API_KEY'\n  }\n})\n.then(response => response.json())\n.then(data => console.log(data))",
                    "create_task": "fetch('https://api.taskmasterpro.com/v1/tasks', {\n  method: 'POST',\n  headers: {\n    'Authorization': 'Bearer YOUR_API_KEY',\n    'Content-Type': 'application/json'\n  },\n  body: JSON.stringify({\n    title: 'New task',\n    description: 'Task description',\n    assignee: 'user_456'\n  })\n})\n.then(response => response.json())\n.then(data => console.log(data))"
                },
                "python": {
                    "list_tasks": "import requests\n\nheaders = {\n    'Authorization': 'Bearer YOUR_API_KEY'\n}\n\nresponse = requests.get('https://api.taskmasterpro.com/v1/tasks', headers=headers)\ndata = response.json()\nprint(data)",
                    "create_task": "import requests\n\nheaders = {\n    'Authorization': 'Bearer YOUR_API_KEY',\n    'Content-Type': 'application/json'\n}\n\ndata = {\n    'title': 'New task',\n    'description': 'Task description',\n    'assignee': 'user_456'\n}\n\nresponse = requests.post('https://api.taskmasterpro.com/v1/tasks', headers=headers, json=data)\nresult = response.json()\nprint(result)"
                }
            }
        },
        "metadata": {
            "version": "2.1.0",
            "last_updated": "2025-05-10",
            "contributors": ["API Team", "Developer Relations"]
        }
    }
)

Business Application: This multi-level inheritance enables precise documentation customization while maintaining consistency:

Human: "We're launching a GraphQL API for TaskMaster. Let's create documentation for it."
AI: [Creates TASKMASTER_GRAPHQL_DOCUMENTATION by inheriting from DEVELOPER_DOCUMENTATION but changing API specifics]
Human: "Now we need to update all our API documentation with our new rate limits."
AI: [Updates rate limits in REST_API_DOCUMENTATION, which automatically propagates to TASKMASTER_API_DOCUMENTATION]

This approach allows for both horizontal specialization (different documentation types) and vertical specialization (increasingly specific implementations), all while maintaining consistency through the inheritance chain – crucial for technical documentation that must remain accurate across products and versions.

5. Interface-Based Consistency

Benefit: Define standard interfaces that inherited variables must implement, ensuring consistency across different implementations.

Example:

python

# Define a marketing campaign interface
CAMPAIGN_INTERFACE = {
    "required_attributes": [
        "name",
        "target_audience",
        "objectives",
        "key_messages",
        "channels",
        "budget",
        "timeline",
        "success_metrics"
    ],
    "methods": [
        "calculate_roi",
        "generate_brief",
        "get_timeline",
        "estimate_reach"
    ],
    "validate": lambda campaign: all(attr in campaign for attr in CAMPAIGN_INTERFACE["required_attributes"])
}

# Base campaign template implementing the interface
BASE_CAMPAIGN = {
    "name": "",
    "target_audience": {
        "demographics": {},
        "psychographics": {},
        "behaviors": {}
    },
    "objectives": [],
    "key_messages": [],
    "channels": [],
    "budget": {
        "total": 0,
        "allocation": {}
    },
    "timeline": {
        "start_date": "",
        "end_date": "",
        "phases": []
    },
    "success_metrics": {},
    
    # Method implementations
    "calculate_roi": lambda campaign, estimated_revenue: (estimated_revenue - campaign["budget"]["total"]) / campaign["budget"]["total"] * 100,
    
    "generate_brief": lambda campaign: {
        "title": f"Campaign Brief: {campaign['name']}",
        "sections": [
            {"heading": "Overview", "content": f"Campaign: {campaign['name']}\nDuration: {campaign['timeline']['start_date']} to {campaign['timeline']['end_date']}\nBudget: ${campaign['budget']['total']:,.2f}"},
            {"heading": "Objectives", "content": "\n- " + "\n- ".join(campaign["objectives"])},
            {"heading": "Target Audience", "content": describe_audience(campaign["target_audience"])},
            {"heading": "Key Messages", "content": "\n- " + "\n- ".join(campaign["key_messages"])},
            {"heading": "Channels", "content": "\n- " + "\n- ".join(campaign["channels"])},
            {"heading": "Timeline", "content": describe_timeline(campaign["timeline"])},
            {"heading": "Success Metrics", "content": describe_metrics(campaign["success_metrics"])}
        ]
    },
    
    "get_timeline": lambda campaign, format="weeks": generate_timeline(campaign["timeline"], format),
    
    "estimate_reach": lambda campaign: calculate_campaign_reach(campaign)
}

# Helper functions for the campaign methods
def describe_audience(audience):
    """Generate a text description of the target audience"""
    # Simplified implementation for example purposes
    description = "This campaign targets "
    
    if audience["demographics"].get("age"):
        description += f"{audience['demographics']['age']} year-olds"
    
    if audience["demographics"].get("gender"):
        description += f", primarily {audience['demographics']['gender']}"
        
    if audience["demographics"].get("location"):
        description += f", in {audience['demographics']['location']}"
    
    if audience["psychographics"]:
        description += f". Their interests include {', '.join(audience['psychographics'].get('interests', []))}."
    
    return description

def describe_timeline(timeline):
    """Generate a text description of the campaign timeline"""
    # Simplified implementation for example purposes
    description = f"Campaign runs from {timeline['start_date']} to {timeline['end_date']}.\n\nKey phases:\n"
    
    for phase in timeline["phases"]:
        description += f"- {phase['name']}: {phase['start_date']} to {phase['end_date']}\n"
    
    return description

def describe_metrics(metrics):
    """Generate a text description of success metrics"""
    # Simplified implementation for example purposes
    description = "This campaign will be measured by:\n"
    
    for metric, target in metrics.items():
        description += f"- {metric}: Target {target}\n"
    
    return description

def generate_timeline(timeline, format):
    """Generate a structured timeline in the requested format"""
    # Simplified implementation for example purposes
    return {"format": format, "data": timeline}

def calculate_campaign_reach(campaign):
    """Calculate estimated campaign reach based on channels and budget"""
    # Simplified implementation for example purposes
    reach_estimates = {
        "social_media": 10 * campaign["budget"]["allocation"].get("social_media", 0),
        "email": 5 * campaign["budget"]["allocation"].get("email", 0),
        "search": 8 * campaign["budget"]["allocation"].get("search", 0),
        "display": 15 * campaign["budget"]["allocation"].get("display", 0)
    }
    
    return {
        "total_estimated_reach": sum(reach_estimates.values()),
        "channel_breakdown": reach_estimates
    }

# Specialized campaign types through inheritance
PRODUCT_LAUNCH_CAMPAIGN = inherit(
    BASE_CAMPAIGN,
    extensions={
        "campaign_type": "product_launch",
        "product_details": {
            "name": "",
            "unique_selling_points": [],
            "pricing": {},
            "availability_date": ""
        },
        "launch_phases": [
            {"name": "Pre-launch teaser", "duration": "2 weeks", "activities": ["Email teasers", "Social media countdown", "Influencer previews"]},
            {"name": "Launch day", "duration": "1 day", "activities": ["Press release", "Social media announcement", "Email blast", "Virtual event"]},
            {"name": "Post-launch promotion", "duration": "2 weeks", "activities": ["Customer testimonials", "Use case highlights", "Limited-time offer"]}
        ],
        
        "generate_launch_plan": lambda campaign: {
            "product": campaign["product_details"]["name"],
            "launch_date": campaign["product_details"]["availability_date"],
            "phases": [
                {
                    "phase": phase["name"],
                    "start_date": calculate_phase_date(campaign["timeline"]["start_date"], phase, campaign["launch_phases"]),
                    "end_date": calculate_phase_end_date(campaign["timeline"]["start_date"], phase, campaign["launch_phases"]),
                    "activities": phase["activities"],
                    "channels": campaign["channels"],
                    "deliverables": generate_phase_deliverables(phase["name"], campaign["channels"])
                }
                for phase in campaign["launch_phases"]
            ]
        }
    }
)

BRAND_AWARENESS_CAMPAIGN = inherit(
    BASE_CAMPAIGN,
    extensions={
        "campaign_type": "brand_awareness",
        "brand_assets": {
            "logo_variants": [],
            "key_visuals": [],
            "taglines": [],
            "brand_colors": []
        },
        "content_strategy": {
            "themes": [],
            "content_types": [],
            "tone_and_voice": "",
            "content_calendar": []
        },
        
        "generate_content_plan": lambda campaign: {
            "weekly_schedule": generate_content_calendar(campaign["timeline"], campaign["content_strategy"]),
            "themes_by_channel": {
                channel: [theme for theme in campaign["content_strategy"]["themes"]]
                for channel in campaign["channels"]
            }
        }
    }
)

LEAD_GENERATION_CAMPAIGN = inherit(
    BASE_CAMPAIGN,
    extensions={
        "campaign_type": "lead_generation",
        "lead_capture": {
            "landing_pages": [],
            "forms": [],
            "lead_magnets": [],
            "qualification_criteria": {}
        },
        "lead_nurturing": {
            "email_sequences": [],
            "follow_up_process": {},
            "lead_scoring": {}
        },
        "conversion_goal": {
            "primary_action": "",
            "secondary_actions": [],
            "typical_conversion_rate": 0
        },
        
        "calculate_cpl": lambda campaign, leads_generated: campaign["budget"]["total"] / max(1, leads_generated),
        
        "estimate_conversions": lambda campaign, leads: {
            "estimated_leads": leads,
            "qualified_leads": int(leads * 0.4),  # Simplified qualification rate
            "conversions": int(leads * 0.4 * campaign["conversion_goal"].get("typical_conversion_rate", 0.05)),
            "cost_per_lead": campaign["budget"]["total"] / max(1, leads),
            "cost_per_acquisition": campaign["budget"]["total"] / max(1, int(leads * 0.4 * campaign["conversion_goal"].get("typical_conversion_rate", 0.05)))
        }
    }
)

# Helper functions for specific campaign types
def calculate_phase_date(start_date, phase, all_phases):
    """Calculate the start date for a specific campaign phase"""
    # Simplified implementation for example purposes
    return "2025-06-01"  # This would actually calculate based on phase sequence

def calculate_phase_end_date(start_date, phase, all_phases):
    """Calculate the end date for a specific campaign phase"""
    # Simplified implementation for example purposes
    return "2025-06-15"  # This would actually calculate based on phase duration

def generate_phase_deliverables(phase_name, channels):
    """Generate deliverables needed for a specific campaign phase"""
    # Simplified implementation for example purposes
    deliverables = []
    
    if "social_media" in channels:
        deliverables.append(f"{phase_name} social media posts")
    
    if "email" in channels:
        deliverables.append(f"{phase_name} email template")
    
    return deliverables

def generate_content_calendar(timeline, content_strategy):
    """Generate a content calendar based on campaign timeline and content strategy"""
    # Simplified implementation for example purposes
    return {
        "weeks": [
            {
                "week": f"Week {i+1}",
                "theme": content_strategy["themes"][i % len(content_strategy["themes"])],
                "content": [
                    {"type": content_type, "topic": f"{content_strategy['themes'][i % len(content_strategy['themes'])]} {content_type}"}
                    for content_type in content_strategy["content_types"][:2]  # Limit to 2 content types per week for example
                ]
            }
            for i in range(4)  # Assuming 4-week campaign for example
        ]
    }

Business Application: This interface-based approach ensures all campaign types maintain consistent structures while adding specialized functionality:

Human: "Let's create a product launch campaign for our new TaskMaster Pro Enterprise solution."
AI: [Creates a campaign using PRODUCT_LAUNCH_CAMPAIGN, ensures all required attributes from CAMPAIGN_INTERFACE are included]
Human: "What would be our estimated cost per lead if we used this budget for a lead generation campaign instead?"
AI: [Creates alternate campaign plan using LEAD_GENERATION_CAMPAIGN with same budget and uses calculate_cpl method]
Human: "I need a campaign brief for the product launch to share with stakeholders."
AI: [Uses generate_brief method that's common across all campaign types]

This approach ensures that regardless of campaign type, key methods like ROI calculation and brief generation work consistently, while each campaign type can add specialized functionality like launch plans or lead scoring.

Advanced Examples and Applications

Example 1: Polymorphic Behavior with Conditional Inheritance

Creating variables that adapt their behavior based on specific conditions:

python

# Base content template with conditional inheritance behavior
def create_content_template(content_type, base_structure, audience_adaptations):
    """Create a content template that adapts to different audiences."""
    
    base_template = {
        "content_type": content_type,
        "structure": base_structure,
        "audience_adaptations": audience_adaptations,
        
        # Polymorphic method that changes behavior based on audience
        "generate": lambda template, content, audience=None: {
            # Select the appropriate adaptation based on audience
            adaptation = template["audience_adaptations"].get(audience, template["audience_adaptations"].get("default", {}))
            
            # Apply the structure with audience-specific adaptations
            result = {}
            
            for section, format_string in template["structure"].items():
                # Check if this section has an audience-specific override
                if section in adaptation:
                    # Use the audience-specific format
                    format_string = adaptation[section]
                
                # Generate the content for this section
                if isinstance(format_string, str):
                    result[section] = format_string.format(**content)
                elif callable(format_string):
                    result[section] = format_string(content, audience)
                else:
                    result[section] = format_string
            
            return result
        }
    }
    
    return base_template

# Create value proposition template with audience adaptations
VALUE_PROPOSITION = create_content_template(
    "value_proposition",
    {
        "headline": "{product_name}: {primary_benefit}",
        "subheadline": "{secondary_benefit} without {pain_point}.",
        "explanation": "{product_name} helps you {primary_benefit_verb} by {mechanism}.",
        "evidence": "Our customers report {proof_point}.",
        "call_to_action": "{cta_verb} to {cta_benefit}."
    },
    {
        "default": {},
        "technical": {
            "headline": "{product_name}: Technically Superior {primary_benefit}",
            "explanation": "Built on {technology}, {product_name} {technical_explanation}.",
            "evidence": "Benchmark tests show {technical_proof_point}."
        },
        "executive": {
            "headline": "Drive {business_outcome} with {product_name}",
            "subheadline": "Strategic solution for {executive_concern}.",
            "evidence": "Companies like yours achieve {roi_metric}."
        },
        "marketing": {
            "headline": "Stand Out with {product_name}",
            "explanation": "Create compelling {marketing_deliverable} that {marketing_benefit}.",
            "call_to_action": "See how {competitor_comparison}."
        }
    }
)

# Create an educational content template with audience adaptations
EDUCATIONAL_CONTENT = create_content_template(
    "educational_content",
    {
        "title": "Understanding {topic}",
        "introduction": "{topic} is {definition}. In this guide, you'll learn {learning_objectives}.",
        "key_concepts": ["Understanding {concept_1}", "Exploring {concept_2}", "Mastering {concept_3}"],
        "step_by_step": "{numbered_steps}",
        "practical_example": "For example, {example_scenario}",
        "common_mistakes": "Avoid these common pitfalls: {mistakes}",
        "conclusion": "By implementing these practices, you'll {outcome}."
    },
    {
        "default": {},
        "beginner": {
            "introduction": "New to {topic}? This beginner-friendly guide explains {topic} in simple terms and helps you {beginner_objective}.",
            "key_concepts": ["What is {concept_1}?", "Why {concept_2} matters", "How to get started with {concept_3}"],
            "common_mistakes": "As a beginner, watch out for: {beginner_mistakes}"
        },
        "intermediate": {
            "title": "Improving Your {topic} Skills",
            "introduction": "Ready to take your {topic} skills to the next level? This guide covers intermediate techniques for {intermediate_objective}.",
            "key_concepts": ["Advanced approaches to {concept_1}", "Optimizing your {concept_2}", "Integrating {concept_3} with other skills"]
        },
        "expert": {
            "title": "Mastering {topic}: Advanced Techniques",
            "introduction": "This expert-level guide explores cutting-edge approaches to {topic} for professionals seeking to {expert_objective}.",
            "key_concepts": ["Innovation in {concept_1}", "Solving complex {concept_2} challenges", "Leading practices in {concept_3}"],
            "practical_example": "In a high-performance scenario: {advanced_example}"
        }
    }
)

Business Application: These polymorphic templates adapt their behavior based on the audience:

Human: "We need to create value propositions for our AI analytics platform targeted at different stakeholders."
AI: [Uses VALUE_PROPOSITION.generate with different audience parameters to create tailored messaging]
Human: "Let's also create educational content about data analytics for different skill levels."
AI: [Uses EDUCATIONAL_CONTENT.generate with "beginner" and "expert" parameters to adapt content complexity]

This approach allows for audience-specific customization while maintaining consistent underlying structures, enabling efficient creation of tailored content without duplicating the base template logic.

Example 2: Composable Inheritance with Mixins

Creating reusable "mixins" that can be combined to build complex variables:

python

# Define reusable mixins as independent components
TIMESTAMP_MIXIN = {
    "created_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    "updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
    "update_timestamp": lambda obj: obj.update({"updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")})
}

VERSIONING_MIXIN = {
    "version": "1.0.0",
    "version_history": [],
    "increment_version": lambda obj, type="patch": obj.update({
        "version": increment_semver(obj["version"], type),
        "version_history": obj["version_history"] + [{"version": obj["version"], "date": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]
    })
}

METADATA_MIXIN = {
    "metadata": {
        "author": "System",
        "tags": [],
        "status": "draft",
        "visibility": "private"
    },
    "add_tag": lambda obj, tag: obj["metadata"]["tags"].append(tag) if tag not in obj["metadata"]["tags"] else None,
    "set_status": lambda obj, status: obj["metadata"].update({"status": status}),
    "set_visibility": lambda obj, visibility: obj["metadata"].update({"visibility": visibility})
}

SHARING_MIXIN = {
    "sharing": {
        "shared_with": [],
        "share_link": None,
        "permissions": {}
    },
    "share_with": lambda obj, user_id, permission="view": obj["sharing"]["shared_with"].append({"user": user_id, "permission": permission}),
    "generate_share_link": lambda obj: obj["sharing"].update({"share_link": f"https://example.com/share/{uuid.uuid4().hex}"})
}

# Function to compose inheritance from multiple mixins
def compose(*mixins, base=None):
    """Compose a new object by applying multiple mixins to a base object."""
    result = {} if base is None else copy.deepcopy(base)
    
    for mixin in mixins:
        for key, value in mixin.items():
            # If the key exists in both, merge dictionaries
            if key in result and isinstance(result[key], dict) and isinstance(value, dict):
                result[key] = {**result[key], **value}
            # Otherwise just set the value
            else:
                result[key] = copy.deepcopy(value)
    
    return result

# Helper function for versioning
def increment_semver(version, increment_type):
    """Increment a semantic version based on the specified type (major, minor, patch)."""
    major, minor, patch = map(int, version.split('.'))
    
    if increment_type == "major":
        return f"{major + 1}.0.0"
    elif increment_type == "minor":
        return f"{major}.{minor + 1}.0"
    else:  # patch
        return f"{major}.{minor}.{patch + 1}"

# Create a document template with selected mixins
DOCUMENT_TEMPLATE = compose(
    TIMESTAMP_MIXIN,
    VERSIONING_MIXIN,
    METADATA_MIXIN,
    {
        "title": "",
        "content": "",
        "format": "markdown",
        "word_count": lambda doc: len(doc["content"].split()),
        "reading_time": lambda doc: math.ceil(len(doc["content"].split()) / 200)  # ~200 words per minute
    }
)

# Create a sharable document with additional mixins
SHARABLE_DOCUMENT = compose(
    DOCUMENT_TEMPLATE,
    SHARING_MIXIN,
    {
        "comments": [],
        "add_comment": lambda doc, user_id, comment_text: doc["comments"].append({
            "user": user_id,
            "text": comment_text,
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        })
    }
)

# Create a product spec document with specific extensions
PRODUCT_SPEC_DOCUMENT = compose(
    SHARABLE_DOCUMENT,
    {
        "spec_type": "product",
        "sections": {
            "overview": "",
            "goals": [],
            "user_stories": [],
            "requirements": {
                "functional": [],
                "non_functional": []
            },
            "technical_details": "",
            "design_notes": "",
            "success_criteria": []
        },
        "add_requirement": lambda doc, req_type, requirement: doc["sections"]["requirements"][req_type].append(requirement),
        "add_user_story": lambda doc, story: doc["sections"]["user_stories"].append(story),
        "generate_summary": lambda doc: f"Product Spec: {doc['title']} (v{doc['version']})\nContains {len(doc['sections']['requirements']['functional'])} functional requirements and {len(doc['sections']['user_stories'])} user stories."
    }
)

Business Application: This composable approach allows for flexible creation of specialized document types:

Human: "Let's create a product specification for our new mobile app feature."
AI: [Creates a document using PRODUCT_SPEC_DOCUMENT with all its inherited functionality]
Human: "We need to share this spec with the engineering team."
AI: [Uses share_with function from SHARING_MIXIN]
Human: "Let's update the functional requirements section and increment the version."
AI: [Updates content and uses increment_version from VERSIONING_MIXIN]

This mixin-based approach enables entrepreneurs to create precisely the document types they need by combining relevant functionality, rather than being limited to rigid inheritance hierarchies or duplicating functionality across similar document types.

Implementation in CRAFT Framework

To integrate variable inheritance into your CRAFT framework effectively, consider:

Creating an inheritance mechanism that supports both extension and override behavior
Implementing mixin composition for more flexible inheritance patterns
Adding interface validation to ensure inherited variables maintain required structure
Supporting multi-level inheritance chains with proper resolution of conflicts

This approach would significantly enhance the CRAFT framework's ability to create sophisticated, reusable content structures while eliminating repetition, aligning perfectly with your goals of structure enabling freedom and conservation of tokens.

The examples above demonstrate how variable inheritance can transform content creation from repetitive, error-prone work into a modular system of consistent, reusable components – ideal for entrepreneurs who need to maintain professional consistency while adapting to different business contexts.

Time-Sensitive Variables in CRAFT

Understanding Time-Sensitive Variables

What Are Time-Sensitive Variables?

Automatically expire after a set duration
Transform their values based on temporal conditions
Track their own creation and modification history
Evolve through predefined stages over time
Adjust their behavior based on time of day, day of week, or calendar dates
Maintain time-stamped version histories
Handle scheduled changes to their values

Think of time-sensitive variables as having an internal clock or calendar that governs their behavior, making them "alive" in the sense that they change and evolve even without direct modification.

Core Concepts Behind Time-Sensitive Variables

Time-sensitive variables align perfectly with three key philosophical principles of your CRAFT framework:

Cumulative Intelligence: By incorporating time awareness, these variables build knowledge that evolves naturally over the course of a project.
Structure Enables Freedom: Predefined time-based behaviors free users from having to manually track and update time-dependent information.
Conservation of Tokens: Automatic time-based updates eliminate repetitive instructions about when and how to modify values.

Technical Implementation

At a basic level, time-sensitive variables can be implemented with temporal attributes and expiration logic:

python

def create_time_sensitive_variable(name, initial_value, expiration=None, evolution_stages=None):
    """Creates a variable with time-based behaviors like expiration and evolution."""
    import time
    from datetime import datetime, timedelta
    
    current_time = time.time()
    creation_time = datetime.now()
    
    # Calculate expiration time if provided
    expiration_time = None
    if expiration:
        if isinstance(expiration, int):
            # Expiration in seconds from now
            expiration_time = current_time + expiration
        elif isinstance(expiration, str):
            # Expiration as ISO date string
            expiration_time = datetime.fromisoformat(expiration).timestamp()
        elif isinstance(expiration, datetime):
            # Expiration as datetime object
            expiration_time = expiration.timestamp()
    
    return {
        "name": name,
        "value": initial_value,
        "created_at": creation_time.isoformat(),
        "updated_at": creation_time.isoformat(),
        "expires_at": datetime.fromtimestamp(expiration_time).isoformat() if expiration_time else None,
        "evolution_stages": evolution_stages or [],
        "current_stage_index": 0,
        "access_count": 0,
        "modification_history": [],
        
        # Methods
        "get": lambda var, default=None: (
            track_access(var) if not is_expired(var) else default
        ),
        
        "set": lambda var, new_value, reason=None: (
            update_value(var, new_value, reason) if not is_expired(var) else "Variable has expired"
        ),
        
        "is_expired": lambda var: is_expired(var),
        
        "time_remaining": lambda var: get_time_remaining(var),
        
        "extend_expiration": lambda var, extension_seconds: (
            extend_expiry(var, extension_seconds) if not is_expired(var) else "Cannot extend expired variable"
        ),
        
        "evolve": lambda var, reason=None: (
            advance_stage(var, reason) if not is_expired(var) else "Variable has expired"
        ),
        
        "get_history": lambda var: var["modification_history"]
    }

# Helper functions
def is_expired(variable):
    """Check if the variable has expired"""
    import time
    from datetime import datetime
    
    if variable["expires_at"]:
        expiration_time = datetime.fromisoformat(variable["expires_at"]).timestamp()
        return time.time() > expiration_time
    return False

def get_time_remaining(variable):
    """Get the remaining time before expiration in seconds"""
    import time
    from datetime import datetime
    
    if variable["expires_at"]:
        expiration_time = datetime.fromisoformat(variable["expires_at"]).timestamp()
        remaining = expiration_time - time.time()
        return max(0, remaining)
    return None

def track_access(variable):
    """Track access to the variable and return its value"""
    variable["access_count"] += 1
    return variable["value"]

def update_value(variable, new_value, reason=None):
    """Update the variable's value and record the change"""
    from datetime import datetime
    
    old_value = variable["value"]
    variable["value"] = new_value
    variable["updated_at"] = datetime.now().isoformat()
    
    # Record the modification
    variable["modification_history"].append({
        "timestamp": variable["updated_at"],
        "old_value": old_value,
        "new_value": new_value,
        "reason": reason or "Manual update"
    })
    
    return new_value

def extend_expiry(variable, extension_seconds):
    """Extend the expiration time by the specified number of seconds"""
    from datetime import datetime, timedelta
    
    if variable["expires_at"]:
        current_expiry = datetime.fromisoformat(variable["expires_at"])
        new_expiry = current_expiry + timedelta(seconds=extension_seconds)
        variable["expires_at"] = new_expiry.isoformat()
        
        # Record the modification
        variable["modification_history"].append({
            "timestamp": datetime.now().isoformat(),
            "action": "extend_expiration",
            "extension": f"+{extension_seconds} seconds",
            "new_expiry": variable["expires_at"]
        })
        
        return variable["expires_at"]
    return "No expiration set"

def advance_stage(variable, reason=None):
    """Advance the variable to its next evolution stage if available"""
    from datetime import datetime
    
    if variable["evolution_stages"] and variable["current_stage_index"] < len(variable["evolution_stages"]) - 1:
        old_stage = variable["current_stage_index"]
        variable["current_stage_index"] += 1
        new_stage = variable["current_stage_index"]
        
        # Update value to the new stage
        old_value = variable["value"]
        variable["value"] = variable["evolution_stages"][new_stage]
        variable["updated_at"] = datetime.now().isoformat()
        
        # Record the evolution
        variable["modification_history"].append({
            "timestamp": variable["updated_at"],
            "action": "evolution",
            "old_stage": old_stage,
            "new_stage": new_stage,
            "old_value": old_value,
            "new_value": variable["value"],
            "reason": reason or "Scheduled evolution"
        })
        
        return variable["value"]
    return "No further evolution stages available"

More sophisticated implementations might include:

Calendar-aware behaviors (workday vs. weekend, holidays)
Recurring schedules (daily, weekly, monthly patterns)
Time zone awareness for global applications
Complex state machines with time-triggered transitions
Predictive time-based adjustments

Benefits of Time-Sensitive Variables

1. Automatic Content Freshness

Benefit: Ensure information stays current by automatically updating or flagging content based on age.

Example:

python

# Create a time-sensitive market data variable
MARKET_ANALYSIS = create_time_sensitive_variable(
    "MARKET_ANALYSIS",
    {
        "industry": "SaaS",
        "market_size": "$157 billion (2025)",
        "growth_rate": "12.5% annually",
        "key_trends": [
            "Increased adoption of vertical-specific SaaS solutions",
            "Growing importance of AI and machine learning integration",
            "Rising focus on user experience and simplified interfaces",
            "Shift toward specialized micro-SaaS products"
        ],
        "competitive_landscape": {
            "leaders": ["Salesforce", "Microsoft", "Adobe", "SAP"],
            "challengers": ["HubSpot", "Zoho", "ServiceNow"],
            "disruptors": ["NotionHQ", "Airtable", "Monday.com"]
        },
        "investment_activity": {
            "total_funding": "$42.1 billion (last 12 months)",
            "notable_deals": [
                {"company": "TechCloud", "amount": "$850M", "date": "2025-03-15", "investors": ["Sequoia", "Accel"]},
                {"company": "DataSphere", "amount": "$620M", "date": "2025-02-22", "investors": ["A16Z", "Tiger Global"]},
                {"company": "SalesAI", "amount": "$410M", "date": "2025-01-08", "investors": ["SoftBank", "Insight Partners"]}
            ]
        },
        "freshness_status": "current"
    },
    # Expires in 90 days
    expiration=60 * 60 * 24 * 90,
    # Evolution stages as data ages
    evolution_stages=[
        # Initial state (current)
        {
            "industry": "SaaS",
            "market_size": "$157 billion (2025)",
            "growth_rate": "12.5% annually",
            "key_trends": [
                "Increased adoption of vertical-specific SaaS solutions",
                "Growing importance of AI and machine learning integration",
                "Rising focus on user experience and simplified interfaces",
                "Shift toward specialized micro-SaaS products"
            ],
            "competitive_landscape": {
                "leaders": ["Salesforce", "Microsoft", "Adobe", "SAP"],
                "challengers": ["HubSpot", "Zoho", "ServiceNow"],
                "disruptors": ["NotionHQ", "Airtable", "Monday.com"]
            },
            "investment_activity": {
                "total_funding": "$42.1 billion (last 12 months)",
                "notable_deals": [
                    {"company": "TechCloud", "amount": "$850M", "date": "2025-03-15", "investors": ["Sequoia", "Accel"]},
                    {"company": "DataSphere", "amount": "$620M", "date": "2025-02-22", "investors": ["A16Z", "Tiger Global"]},
                    {"company": "SalesAI", "amount": "$410M", "date": "2025-01-08", "investors": ["SoftBank", "Insight Partners"]}
                ]
            },
            "freshness_status": "current"
        },
        # After 30 days
        {
            "industry": "SaaS",
            "market_size": "$157 billion (2025)",
            "growth_rate": "12.5% annually",
            "key_trends": [
                "Increased adoption of vertical-specific SaaS solutions",
                "Growing importance of AI and machine learning integration",
                "Rising focus on user experience and simplified interfaces",
                "Shift toward specialized micro-SaaS products"
            ],
            "competitive_landscape": {
                "leaders": ["Salesforce", "Microsoft", "Adobe", "SAP"],
                "challengers": ["HubSpot", "Zoho", "ServiceNow"],
                "disruptors": ["NotionHQ", "Airtable", "Monday.com"]
            },
            "investment_activity": {
                "total_funding": "$42.1 billion (last 12 months)",
                "notable_deals": [
                    {"company": "TechCloud", "amount": "$850M", "date": "2025-03-15", "investors": ["Sequoia", "Accel"]},
                    {"company": "DataSphere", "amount": "$620M", "date": "2025-02-22", "investors": ["A16Z", "Tiger Global"]},
                    {"company": "SalesAI", "amount": "$410M", "date": "2025-01-08", "investors": ["SoftBank", "Insight Partners"]}
                ]
            },
            "freshness_status": "aging - recommend refreshing market data"
        },
        # After 60 days
        {
            "industry": "SaaS",
            "market_size": "$157 billion (2025)",
            "growth_rate": "12.5% annually",
            "key_trends": [
                "Increased adoption of vertical-specific SaaS solutions",
                "Growing importance of AI and machine learning integration",
                "Rising focus on user experience and simplified interfaces",
                "Shift toward specialized micro-SaaS products"
            ],
            "competitive_landscape": {
                "leaders": ["Salesforce", "Microsoft", "Adobe", "SAP"],
                "challengers": ["HubSpot", "Zoho", "ServiceNow"],
                "disruptors": ["NotionHQ", "Airtable", "Monday.com"]
            },
            "investment_activity": {
                "total_funding": "$42.1 billion (last 12 months)",
                "notable_deals": [
                    {"company": "TechCloud", "amount": "$850M", "date": "2025-03-15", "investors": ["Sequoia", "Accel"]},
                    {"company": "DataSphere", "amount": "$620M", "date": "2025-02-22", "investors": ["A16Z", "Tiger Global"]},
                    {"company": "SalesAI", "amount": "$410M", "date": "2025-01-08", "investors": ["SoftBank", "Insight Partners"]}
                ]
            },
            "freshness_status": "outdated - data is over 60 days old and should be updated for critical decisions"
        }
    ]
)

Business Application: For entrepreneurs relying on market intelligence, this variable ensures awareness of data freshness:

Human: "Let's include the latest market trends in our investor pitch."
AI: [Checks MARKET_ANALYSIS.is_expired() and stage status before using]
AI: "Based on current market analysis, the key trends in SaaS include vertical-specific solutions, AI integration, UX focus, and micro-SaaS products. The market is valued at $157 billion with 12.5% annual growth."
Human: "When was this analysis last updated?"
AI: [Calculates time since creation] "This market analysis is from [creation date], which makes it [X] days old. Its freshness status is currently marked as '[current/aging/outdated]'."

If accessing this variable weeks later, the AI would automatically acknowledge the aging data and recommend refreshing it before making critical business decisions. This eliminates the risk of unknowingly using outdated market intelligence in important materials.

2. Time-Limited Offers and Opportunities

Benefit: Create promotional content with built-in expiration to avoid outdated offers remaining in materials.

Example:

python

# Create a time-sensitive promotional offer
SUMMER_PROMO = create_time_sensitive_variable(
    "SUMMER_PROMO",
    {
        "name": "Summer Productivity Bundle",
        "headline": "Save 40% on our Summer Productivity Bundle",
        "description": "For a limited time, get our premium plan plus all power-user extensions at 40% off the regular price.",
        "discount_percentage": 40,
        "original_price": 99.99,
        "promotional_price": 59.99,
        "promo_code": "SUMMER2025",
        "included_items": [
            "12-month Premium Plan subscription",
            "Analytics Dashboard extension",
            "Team Collaboration tools",
            "Advanced Automation pack"
        ],
        "terms": [
            "Limited time offer, valid until September 1, 2025",
            "Available for new customers and plan upgrades",
            "Cannot be combined with other promotions"
        ],
        "cta_text": "Claim Your 40% Discount",
        "status": "active",
        "targeting": ["website_visitors", "email_list", "social_media"],
        "marketing_assets": {
            "banner_url": "https://example.com/summer-promo-2025-banner.jpg",
            "email_template": "summer_promo_2025_email.html",
            "social_images": ["summer_promo_ig.jpg", "summer_promo_fb.jpg", "summer_promo_tw.jpg"]
        }
    },
    # Expires September 1, 2025
    expiration=datetime(2025, 9, 1).timestamp(),
    # Evolution stages based on time remaining
    evolution_stages=[
        # Initial stage (normal)
        {
            "name": "Summer Productivity Bundle",
            "headline": "Save 40% on our Summer Productivity Bundle",
            "description": "For a limited time, get our premium plan plus all power-user extensions at 40% off the regular price.",
            "discount_percentage": 40,
            "original_price": 99.99,
            "promotional_price": 59.99,
            "promo_code": "SUMMER2025",
            "included_items": [
                "12-month Premium Plan subscription",
                "Analytics Dashboard extension",
                "Team Collaboration tools",
                "Advanced Automation pack"
            ],
            "terms": [
                "Limited time offer, valid until September 1, 2025",
                "Available for new customers and plan upgrades",
                "Cannot be combined with other promotions"
            ],
            "cta_text": "Claim Your 40% Discount",
            "status": "active",
            "targeting": ["website_visitors", "email_list", "social_media"],
            "marketing_assets": {
                "banner_url": "https://example.com/summer-promo-2025-banner.jpg",
                "email_template": "summer_promo_2025_email.html",
                "social_images": ["summer_promo_ig.jpg", "summer_promo_fb.jpg", "summer_promo_tw.jpg"]
            }
        },
        # Last 14 days (urgency)
        {
            "name": "Summer Productivity Bundle",
            "headline": "LAST CHANCE: 40% Off Summer Bundle Ends Soon",
            "description": "Time is running out! Our biggest discount of the year ends on September 1. Lock in 40% savings on our complete productivity suite now.",
            "discount_percentage": 40,
            "original_price": 99.99,
            "promotional_price": 59.99,
            "promo_code": "SUMMER2025",
            "included_items": [
                "12-month Premium Plan subscription",
                "Analytics Dashboard extension",
                "Team Collaboration tools",
                "Advanced Automation pack"
            ],
            "terms": [
                "Offer expires September 1, 2025",
                "Available for new customers and plan upgrades",
                "Cannot be combined with other promotions"
            ],
            "cta_text": "Don't Miss Out - Save 40% Now",
            "status": "ending_soon",
            "targeting": ["website_visitors", "email_list", "social_media", "cart_abandoners"],
            "marketing_assets": {
                "banner_url": "https://example.com/summer-promo-2025-last-chance.jpg",
                "email_template": "summer_promo_2025_final_reminder.html",
                "social_images": ["summer_promo_last_chance_ig.jpg", "summer_promo_last_chance_fb.jpg", "summer_promo_last_chance_tw.jpg"]
            }
        },
        # After expiration (transition to new offer)
        {
            "name": "Fall Productivity Special",
            "headline": "Missed our Summer offer? Try our Fall Special",
            "description": "While our Summer promotion has ended, you can still save 25% on annual plans this fall.",
            "discount_percentage": 25,
            "original_price": 99.99,
            "promotional_price": 74.99,
            "promo_code": "FALL2025",
            "included_items": [
                "12-month Premium Plan subscription",
                "Analytics Dashboard extension",
                "Team Collaboration tools"
            ],
            "terms": [
                "Limited time offer, valid until November 30, 2025",
                "Available for new customers and plan upgrades",
                "Cannot be combined with other promotions"
            ],
            "cta_text": "Save 25% This Fall",
            "status": "active",
            "targeting": ["website_visitors", "email_list", "social_media"],
            "marketing_assets": {
                "banner_url": "https://example.com/fall-promo-2025-banner.jpg",
                "email_template": "fall_promo_2025_email.html",
                "social_images": ["fall_promo_ig.jpg", "fall_promo_fb.jpg", "fall_promo_tw.jpg"]
            }
        }
    ]
)

Business Application: For marketing campaigns, this approach ensures promotions automatically adapt based on time remaining:

Human: "Let's create an email about our current promotion for the newsletter."
AI: [Checks time remaining on SUMMER_PROMO and uses appropriate stage content]
Human: "What happens if customers try to use this promotion after it expires?"
AI: "After September 1st, this promotion will no longer be valid. The system will automatically transition to our Fall Productivity Special, which offers 25% off annual plans."

This variable would automatically advance to the "urgency" stage in the final two weeks of the promotion, changing headline and CTA messaging to emphasize the limited time remaining. After expiration, it transitions to the next seasonal offer without requiring manual updates to marketing materials.

3. Project Phase Transitions

Benefit: Automatically track project timelines and advance through development phases based on calendar dates.

Example:

python

# Create a time-sensitive project timeline variable
PRODUCT_ROADMAP = create_time_sensitive_variable(
    "PRODUCT_ROADMAP",
    {
        "product_name": "TaskMaster Pro 4.0",
        "current_phase": "planning",
        "phase_dates": {
            "planning": {"start": "2025-05-01", "end": "2025-06-15"},
            "development": {"start": "2025-06-16", "end": "2025-08-31"},
            "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
            "launch": {"start": "2025-10-01", "end": "2025-10-15"},
            "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
        },
        "key_milestones": [
            {"name": "Feature specification complete", "due": "2025-06-01", "status": "completed"},
            {"name": "Design system finalized", "due": "2025-06-15", "status": "in_progress"},
            {"name": "Alpha version ready", "due": "2025-07-31", "status": "not_started"},
            {"name": "Beta version ready", "due": "2025-08-31", "status": "not_started"},
            {"name": "Marketing site updated", "due": "2025-09-15", "status": "not_started"},
            {"name": "Public launch", "due": "2025-10-01", "status": "not_started"}
        ],
        "current_priorities": [
            "Finalize UI/UX redesign",
            "Complete technical architecture documentation",
            "Begin frontend component development",
            "Establish beta tester recruitment criteria"
        ],
        "team_assignments": {
            "design": ["User interface redesign", "Design system documentation"],
            "frontend": ["Component library development", "Responsive layout implementation"],
            "backend": ["API architecture", "Database optimization", "Authentication system"],
            "qa": ["Test plan development", "Automated testing framework"],
            "marketing": ["Launch strategy", "Beta program communication plan"]
        },
        "status_updates": [
            {"date": "2025-05-10", "update": "Completed initial user research with 12 key customers", "author": "Sarah K."},
            {"date": "2025-05-18", "update": "Feature prioritization workshop completed with stakeholders", "author": "Michael R."},
            {"date": "2025-05-25", "update": "First draft of technical architecture approved", "author": "David L."}
        ],
        "next_meeting": "2025-06-05: Weekly Sprint Planning"
    },
    # Expiration set to project end
    expiration=datetime(2025, 12, 31).timestamp(),
    # Evolution stages based on project phases
    evolution_stages=[
        # Planning phase
        {
            "product_name": "TaskMaster Pro 4.0",
            "current_phase": "planning",
            "phase_dates": {
                "planning": {"start": "2025-05-01", "end": "2025-06-15"},
                "development": {"start": "2025-06-16", "end": "2025-08-31"},
                "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
                "launch": {"start": "2025-10-01", "end": "2025-10-15"},
                "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
            },
            "current_priorities": [
                "Finalize UI/UX redesign",
                "Complete technical architecture documentation",
                "Begin frontend component development", 
                "Establish beta tester recruitment criteria"
            ],
            "team_focus": "Planning and design",
            "key_metrics": ["Design completion percentage", "Specification clarity score", "Stakeholder alignment"],
            "risk_factors": ["Feature scope creep", "Resource allocation", "Technical feasibility uncertainty"]
        },
        # Development phase
        {
            "product_name": "TaskMaster Pro 4.0",
            "current_phase": "development",
            "phase_dates": {
                "planning": {"start": "2025-05-01", "end": "2025-06-15"},
                "development": {"start": "2025-06-16", "end": "2025-08-31"}, 
                "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
                "launch": {"start": "2025-10-01", "end": "2025-10-15"},
                "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
            },
            "current_priorities": [
                "Feature implementation",
                "Component integration",
                "Internal testing and bug fixing",
                "Performance optimization",
                "Beta testing preparation"
            ],
            "team_focus": "Implementation and testing",
            "key_metrics": ["Sprint velocity", "Code coverage", "Bug resolution rate", "Feature completion percentage"],
            "risk_factors": ["Technical debt", "Integration challenges", "Timeline slippage", "Resource constraints"]
        },
        # Beta testing phase
        {
            "product_name": "TaskMaster Pro 4.0",
            "current_phase": "beta_testing",
            "phase_dates": {
                "planning": {"start": "2025-05-01", "end": "2025-06-15"},
                "development": {"start": "2025-06-16", "end": "2025-08-31"},
                "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
                "launch": {"start": "2025-10-01", "end": "2025-10-15"},
                "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
            },
            "current_priorities": [
                "Beta tester onboarding",
                "Feedback collection and analysis",
                "Critical bug fixing",
                "Performance fine-tuning",
                "Documentation completion",
                "Launch preparedness"
            ],
            "team_focus": "User validation and refinement",
            "key_metrics": ["Beta user engagement", "Feature adoption rate", "Bug severity distribution", "NPS from beta users"],
            "risk_factors": ["Critical bugs discovery", "Poor user experience", "Feature gaps", "Server load issues"]
        },
        # Launch phase
        {
            "product_name": "TaskMaster Pro 4.0",
            "current_phase": "launch",
            "phase_dates": {
                "planning": {"start": "2025-05-01", "end": "2025-06-15"},
                "development": {"start": "2025-06-16", "end": "2025-08-31"},
                "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
                "launch": {"start": "2025-10-01", "end": "2025-10-15"},
                "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
            },
            "current_priorities": [
                "Marketing campaign execution",
                "Customer communication",
                "Server scaling and monitoring",
                "Support team readiness",
                "Launch event coordination",
                "Real-time issue resolution"
            ],
            "team_focus": "Go-to-market execution",
            "key_metrics": ["User acquisition rate", "Server uptime", "Support ticket volume", "Media coverage"],
            "risk_factors": ["Service disruptions", "Negative reviews", "Competitor responses", "Marketing message reception"]
        },
        # Post-launch phase
        {
            "product_name": "TaskMaster Pro 4.0",
            "current_phase": "post_launch",
            "phase_dates": {
                "planning": {"start": "2025-05-01", "end": "2025-06-15"},
                "development": {"start": "2025-06-16", "end": "2025-08-31"},
                "beta_testing": {"start": "2025-09-01", "end": "2025-09-30"},
                "launch": {"start": "2025-10-01", "end": "2025-10-15"},
                "post_launch": {"start": "2025-10-16", "end": "2025-12-31"}
            },
            "current_priorities": [
                "User retention optimization",
                "Feature adoption monitoring",
                "Performance tuning",
                "Minor enhancement implementation",
                "Community engagement",
                "Planning for next release cycle"
            ],
            "team_focus": "Stabilization and growth",
            "key_metrics": ["Daily active users", "Retention rate", "Feature adoption depth", "Support ticket resolution time", "Revenue impact"],
            "risk_factors": ["User churn", "Unexpected usage patterns", "Performance issues at scale", "Competitive pressure"]
        }
    ]
)

Business Application: For product development, this variable automatically adjusts focus and priorities based on the project timeline:

Human: "What are our current priorities for the TaskMaster Pro 4.0 project?"
AI: [Checks current date against PRODUCT_ROADMAP phase dates and provides the appropriate stage information]
Human: "When do we move into the beta testing phase?"
AI: "The beta testing phase is scheduled to begin on September 1, 2025, which is [X] days from now. During that phase, our priorities will shift to beta tester onboarding, feedback collection, and critical bug fixing."

This approach ensures that project documentation always reflects the current phase of development without manual updates. The variable would automatically advance through planning, development, beta testing, launch, and post-launch phases based on the calendar dates, providing phase-appropriate priorities, metrics, and risk factors.

4. Seasonal and Contextual Adaptation

Benefit: Automatically adjust content and recommendations based on time of year, day of week, or time of day.

Example:

python

# Create a time-sensitive variable for seasonal marketing recommendations
MARKETING_RECOMMENDATIONS = create_time_sensitive_variable(
    "MARKETING_RECOMMENDATIONS",
    get_current_season_marketing(),  # Function that returns current season's marketing
    # Expires at end of current season
    expiration=get_end_of_current_season(),  # Function that calculates end of season
    # Evolution stages based on seasons
    evolution_stages=[
        # Spring (March-May)
        {
            "season": "spring",
            "themes": ["Renewal", "Growth", "Fresh Start", "Spring Cleaning"],
            "color_palette": ["#a8e6cf", "#dcedc1", "#ffd3b6", "#ffaaa5", "#ff8b94"],
            "imagery": ["Blooming flowers", "Green landscapes", "Outdoor activities", "Bright mornings"],
            "messaging_tone": "Energetic, optimistic, forward-looking",
            "promotional_angles": [
                "Spring refresh for your [product/service]",
                "New season, new opportunities with [product/service]",
                "Spring into action with our [seasonal offer]",
                "Clean up and optimize your [business process] this spring"
            ],
            "content_ideas": [
                "Spring cleaning checklist for your [business area]",
                "X ways to refresh your [business process] for the new season",
                "Spring trends in [industry]",
                "How to grow your [business metric] this season"
            ],
            "seasonal_offers": [
                {"name": "Spring Starter Pack", "discount": "20% off first 3 months", "messaging": "Plant the seeds for success"},
                {"name": "Spring Cleaning Bundle", "discount": "Free system audit with annual plan", "messaging": "Refresh and optimize"}
            ],
            "best_channels": ["Email", "Instagram", "LinkedIn", "Google Ads"],
            "timing_recommendations": {
                "best_days": ["Tuesday", "Wednesday", "Thursday"],
                "best_times": ["10:00 AM", "2:00 PM"],
                "key_dates": [
                    {"date": "March 20", "occasion": "First Day of Spring"},
                    {"date": "April 22", "occasion": "Earth Day"},
                    {"date": "May 1", "occasion": "May Day"},
                    {"date": "May 28", "occasion": "Memorial Day Weekend"}
                ]
            }
        },
        # Summer (June-August)
        {
            "season": "summer",
            "themes": ["Adventure", "Freedom", "Outdoors", "Energy", "Relaxation"],
            "color_palette": ["#f9ed69", "#f08a5d", "#b83b5e", "#6a2c70", "#08d9d6"],
            "imagery": ["Beach scenes", "Outdoor adventures", "Bright sunshine", "Travel moments"],
            "messaging_tone": "Casual, exciting, bold, playful",
            "promotional_angles": [
                "Summer special - limited time offer",
                "Beat the heat with our [summer solution]",
                "Summer productivity boost",
                "Take your [business goal] to new heights this summer"
            ],
            "content_ideas": [
                "Summer reading list for [professionals in your industry]",
                "How to stay productive during summer months",
                "X cool ways to use [product] this summer",
                "Summer checklist for [business process]"
            ],
            "seasonal_offers": [
                {"name": "Summer Flash Sale", "discount": "40% off for 72 hours only", "messaging": "Hot deals for the hottest season"},
                {"name": "Summer Growth Package", "discount": "Bundle and save 25%", "messaging": "Fuel your summer growth"}
            ],
            "best_channels": ["Instagram", "TikTok", "Email", "Facebook"],
            "timing_recommendations": {
                "best_days": ["Monday", "Thursday", "Friday"],
                "best_times": ["11:00 AM", "3:00 PM", "7:00 PM"],
                "key_dates": [
                    {"date": "June 21", "occasion": "First Day of Summer"},
                    {"date": "July 4", "occasion": "Independence Day"},
                    {"date": "August 1", "occasion": "Back to School Season Begins"},
                    {"date": "September 4", "occasion": "Labor Day Weekend"}
                ]
            }
        },
        # Fall (September-November)
        {
            "season": "fall",
            "themes": ["Preparation", "Transformation", "Efficiency", "Harvest", "Coziness"],
            "color_palette": ["#ff9a00", "#e05780", "#7d3c98", "#a4133c", "#604d53"],
            "imagery": ["Autumn leaves", "Warm drinks", "Productive workspaces", "Harvest scenes"],
            "messaging_tone": "Warm, practical, thoughtful, strategic",
            "promotional_angles": [
                "Fall into savings with our [seasonal offer]",
                "Prepare for year-end success",
                "Harvest the benefits of [product feature]",
                "Transform your [business area] this fall"
            ],
            "content_ideas": [
                "Fall checklist: Preparing your [business] for Q4",
                "X ways to transform your [business process] this season",
                "Harvesting insights: Fall analysis of [industry trend]",
                "Cozy productivity: Setting up your ideal [work environment]"
            ],
            "seasonal_offers": [
                {"name": "Fall Preparation Bundle", "discount": "Annual plan at 30% off", "messaging": "Set yourself up for year-end success"},
                {"name": "Harvest Special", "discount": "Buy one, get one 50% off", "messaging": "Reap what you sow"}
            ],
            "best_channels": ["LinkedIn", "Email", "Webinars", "Google Ads"],
            "timing_recommendations": {
                "best_days": ["Tuesday", "Wednesday", "Thursday"],
                "best_times": ["9:00 AM", "1:00 PM", "4:00 PM"],
                "key_dates": [
                    {"date": "September 22", "occasion": "First Day of Fall"},
                    {"date": "October 31", "occasion": "Halloween"},
                    {"date": "November 24", "occasion": "Thanksgiving"},
                    {"date": "November 27", "occasion": "Black Friday"}
                ]
            }
        },
        # Winter (December-February)
        {
            "season": "winter",
            "themes": ["Reflection", "Planning", "Celebration", "Warmth", "New Beginnings"],
            "color_palette": ["#caf0f8", "#90e0ef", "#00b4d8", "#0077b6", "#03045e"],
            "imagery": ["Snowy landscapes", "Cozy indoor settings", "Planning sessions", "Celebration moments"],
            "messaging_tone": "Reflective, inspiring, forward-thinking, warm",
            "promotional_angles": [
                "Winter special offers to warm up your [business area]",
                "End the year strong with [product/service]",
                "New year, new opportunities with our [January special]",
                "Cozy up with savings on [product/service]"
            ],
            "content_ideas": [
                "Year in review: Top [industry] trends of 2025",
                "Winter planning guide for [business function]",
                "X ways to set powerful goals for the new year",
                "Keeping your [business process] warm during the cold months"
            ],
            "seasonal_offers": [
                {"name": "Holiday Bundle", "discount": "Gift 3 months free with annual subscription", "messaging": "Give yourself the gift of [key benefit]"},
                {"name": "New Year Kickstarter", "discount": "25% off plus free strategy session", "messaging": "Begin 2026 with momentum"}
            ],
            "best_channels": ["Email", "LinkedIn", "Webinars", "Direct Mail"],
            "timing_recommendations": {
                "best_days": ["Monday", "Tuesday", "Wednesday"],
                "best_times": ["10:00 AM", "2:00 PM"],
                "key_dates": [
                    {"date": "December 21", "occasion": "First Day of Winter"},
                    {"date": "December 25", "occasion": "Christmas"},
                    {"date": "January 1", "occasion": "New Year's Day"},
                    {"date": "February 14", "occasion": "Valentine's Day"}
                ]
            }
        }
    ]
)

# Helper functions
def get_current_season():
    """Determine the current season based on the date"""
    current_month = datetime.now().month
    
    if 3 <= current_month <= 5:
        return "spring"
    elif 6 <= current_month <= 8:
        return "summer"
    elif 9 <= current_month <= 11:
        return "fall"
    else:
        return "winter"

def get_current_season_marketing():
    """Get the marketing recommendations for the current season"""
    season = get_current_season()
    
    # This would retrieve the appropriate season's marketing data
    # Simplified for example purposes
    if season == "spring":
        return {"season": "spring", "themes": ["Renewal", "Growth"], "color_palette": ["#a8e6cf", "#dcedc1"]}
    elif season == "summer":
        return {"season": "summer", "themes": ["Adventure", "Freedom"], "color_palette": ["#f9ed69", "#f08a5d"]}
    elif season == "fall":
        return {"season": "fall", "themes": ["Preparation", "Transformation"], "color_palette": ["#ff9a00", "#e05780"]}
    else:  # winter
        return {"season": "winter", "themes": ["Reflection", "Planning"], "color_palette": ["#caf0f8", "#90e0ef"]}

def get_end_of_current_season():
    """Calculate the end date of the current season"""
    current_month = datetime.now().month
    current_year = datetime.now().year
    
    if 3 <= current_month <= 5:  # Spring
        return datetime(current_year, 5, 31, 23, 59, 59).timestamp()
    elif 6 <= current_month <= 8:  # Summer
        return datetime(current_year, 8, 31, 23, 59, 59).timestamp()
    elif 9 <= current_month <= 11:  # Fall
        return datetime(current_year, 11, 30, 23, 59, 59).timestamp()
    else:  # Winter
        # If January or February, winter ends in the current year
        if current_month <= 2:
            return datetime(current_year, 2, 28, 23, 59, 59).timestamp()
        # If December, winter ends in the next year
        else:
            return datetime(current_year + 1, 2, 28, 23, 59, 59).timestamp()

Business Application: For marketing planning, this variable provides seasonally appropriate recommendations without manual updates:

Human: "What marketing themes should we focus on for our upcoming campaign?"
AI: [Checks current date and provides season-appropriate themes from MARKETING_RECOMMENDATIONS]
Human: "Which colors would work well with these themes?"
AI: [Provides the seasonal color palette from the current season]
Human: "Are there any important dates coming up that we should consider?"
AI: [Lists key_dates from the timing_recommendations of the current season]

This approach ensures that marketing recommendations are always seasonally appropriate, with the variable automatically transitioning from spring to summer to fall to winter themes and strategies based on the calendar date.

5. Version Control and Deprecation Management

Benefit: Manage software feature deprecation, API changes, and version transitions with time-aware variables.

Example:

python

# Create a time-sensitive variable for API version management
API_VERSION_INFO = create_time_sensitive_variable(
    "API_VERSION_INFO",
    {
        "current_version": "v2.5",
        "status": "current",
        "latest_stable": "v2.5",
        "latest_beta": "v3.0-beta.2",
        "versions": {
            "v1.0": {
                "release_date": "2023-04-15",
                "end_of_life": "2024-04-15",
                "status": "deprecated",
                "documentation_url": "https://api.example.com/docs/v1",
                "major_features": ["Basic CRUD operations", "Authentication", "Rate limiting"],
                "breaking_changes_from_previous": []
            },
            "v2.0": {
                "release_date": "2024-02-01",
                "end_of_life": "2025-08-01",
                "status": "maintenance",
                "documentation_url": "https://api.example.com/docs/v2",
                "major_features": ["Batch operations", "Webhooks", "Extended metadata", "Advanced filtering"],
                "breaking_changes_from_previous": [
                    "Authentication method changed from API key to OAuth2",
                    "Response format standardized to envelope pattern",
                    "Rate limits implemented per endpoint instead of globally"
                ]
            },
            "v2.5": {
                "release_date": "2025-01-10",
                "end_of_life": "2026-07-10",
                "status": "current",
                "documentation_url": "https://api.example.com/docs/v2.5",
                "major_features": ["Real-time notifications", "Expanded search capabilities", "Performance improvements"],
                "breaking_changes_from_previous": [
                    "Search parameter format changed",
                    "Pagination tokens now required for lists over 100 items"
                ]
            },
            "v3.0-beta.1": {
                "release_date": "2025-04-01",
                "status": "beta",
                "documentation_url": "https://api.example.com/docs/v3-beta",
                "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics"],
                "breaking_changes_from_previous": [
                    "Complete overhaul of resource paths",
                    "New authentication system using JWT",
                    "Response format changed to support GraphQL"
                ]
            },
            "v3.0-beta.2": {
                "release_date": "2025-05-05",
                "status": "beta",
                "documentation_url": "https://api.example.com/docs/v3-beta",
                "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics", "Improved error handling"],
                "breaking_changes_from_previous": [
                    "Error response format changed",
                    "Rate limiting logic adjusted"
                ]
            }
        },
        "deprecation_schedule": [
            {"version": "v1.0", "deprecation_date": "2024-04-15", "removal_date": "2024-07-15", "migration_guide": "https://api.example.com/migration/v1-to-v2"},
            {"version": "v2.0", "deprecation_date": "2025-08-01", "removal_date": "2026-02-01", "migration_guide": "https://api.example.com/migration/v2-to-v2.5"}
        ],
        "version_compatibility": {
            "v1.0": ["v1.0"],
            "v2.0": ["v2.0", "v2.5"],
            "v2.5": ["v2.0", "v2.5", "v3.0-beta.1", "v3.0-beta.2"]
        },
        "upgrade_recommendations": {
            "v1.0": {
                "recommended_target": "v2.5",
                "effort_level": "High",
                "key_considerations": [
                    "Authentication system migration required",
                    "Response handling needs to be updated",
                    "Rate limit handling needs review"
                ]
            },
            "v2.0": {
                "recommended_target": "v2.5",
                "effort_level": "Low",
                "key_considerations": [
                    "Search parameter format updates",
                    "Pagination handling for large lists"
                ]
            },
            "v2.5": {
                "recommended_target": "Stay on v2.5 until v3.0 is stable",
                "effort_level": "N/A",
                "key_considerations": [
                    "Start planning for v3.0 migration",
                    "Consider joining beta program to provide feedback"
                ]
            }
        }
    },
    # Expires when v3.0 becomes stable
    expiration=datetime(2025, 9, 1).timestamp(),
    # Evolution stages based on API lifecycle
    evolution_stages=[
        # Current stage (v2.5 current, v3.0 in beta)
        {
            "current_version": "v2.5",
            "status": "current",
            "latest_stable": "v2.5",
            "latest_beta": "v3.0-beta.2",
            "versions": {
                "v1.0": {
                    "release_date": "2023-04-15",
                    "end_of_life": "2024-04-15",
                    "status": "deprecated",
                    "documentation_url": "https://api.example.com/docs/v1",
                    "major_features": ["Basic CRUD operations", "Authentication", "Rate limiting"],
                    "breaking_changes_from_previous": []
                },
                "v2.0": {
                    "release_date": "2024-02-01",
                    "end_of_life": "2025-08-01",
                    "status": "maintenance",
                    "documentation_url": "https://api.example.com/docs/v2",
                    "major_features": ["Batch operations", "Webhooks", "Extended metadata", "Advanced filtering"],
                    "breaking_changes_from_previous": [
                        "Authentication method changed from API key to OAuth2",
                        "Response format standardized to envelope pattern",
                        "Rate limits implemented per endpoint instead of globally"
                    ]
                },
                "v2.5": {
                    "release_date": "2025-01-10",
                    "end_of_life": "2026-07-10",
                    "status": "current",
                    "documentation_url": "https://api.example.com/docs/v2.5",
                    "major_features": ["Real-time notifications", "Expanded search capabilities", "Performance improvements"],
                    "breaking_changes_from_previous": [
                        "Search parameter format changed",
                        "Pagination tokens now required for lists over 100 items"
                    ]
                },
                "v3.0-beta.1": {
                    "release_date": "2025-04-01",
                    "status": "beta",
                    "documentation_url": "https://api.example.com/docs/v3-beta",
                    "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics"],
                    "breaking_changes_from_previous": [
                        "Complete overhaul of resource paths",
                        "New authentication system using JWT",
                        "Response format changed to support GraphQL"
                    ]
                },
                "v3.0-beta.2": {
                    "release_date": "2025-05-05",
                    "status": "beta",
                    "documentation_url": "https://api.example.com/docs/v3-beta",
                    "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics", "Improved error handling"],
                    "breaking_changes_from_previous": [
                        "Error response format changed",
                        "Rate limiting logic adjusted"
                    ]
                }
            },
            "recommended_version_for_new_integrations": "v2.5",
            "developer_message": "If you're starting a new integration, we recommend using v2.5. While v3.0 is in beta and offers exciting new features, it may still change before final release."
        },
        # After v3.0 release
        {
            "current_version": "v3.0",
            "status": "current",
            "latest_stable": "v3.0",
            "latest_beta": "v3.1-beta.1",
            "versions": {
                "v1.0": {
                    "release_date": "2023-04-15",
                    "end_of_life": "2024-04-15",
                    "status": "deprecated",
                    "documentation_url": "https://api.example.com/docs/v1",
                    "major_features": ["Basic CRUD operations", "Authentication", "Rate limiting"],
                    "breaking_changes_from_previous": []
                },
                "v2.0": {
                    "release_date": "2024-02-01",
                    "end_of_life": "2025-08-01",
                    "status": "deprecated",
                    "documentation_url": "https://api.example.com/docs/v2",
                    "major_features": ["Batch operations", "Webhooks", "Extended metadata", "Advanced filtering"],
                    "breaking_changes_from_previous": [
                        "Authentication method changed from API key to OAuth2",
                        "Response format standardized to envelope pattern",
                        "Rate limits implemented per endpoint instead of globally"
                    ]
                },
                "v2.5": {
                    "release_date": "2025-01-10",
                    "end_of_life": "2026-07-10",
                    "status": "maintenance",
                    "documentation_url": "https://api.example.com/docs/v2.5",
                    "major_features": ["Real-time notifications", "Expanded search capabilities", "Performance improvements"],
                    "breaking_changes_from_previous": [
                        "Search parameter format changed",
                        "Pagination tokens now required for lists over 100 items"
                    ]
                },
                "v3.0": {
                    "release_date": "2025-07-15",
                    "end_of_life": "2027-07-15",
                    "status": "current",
                    "documentation_url": "https://api.example.com/docs/v3",
                    "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics", "Improved error handling"],
                    "breaking_changes_from_previous": [
                        "Complete overhaul of resource paths",
                        "New authentication system using JWT",
                        "Response format changed to support GraphQL",
                        "Error response format changed",
                        "Rate limiting logic adjusted"
                    ]
                },
                "v3.1-beta.1": {
                    "release_date": "2025-08-20",
                    "status": "beta",
                    "documentation_url": "https://api.example.com/docs/v3.1-beta",
                    "major_features": ["AI-assisted query generation", "Advanced caching mechanisms", "New notification channels"],
                    "breaking_changes_from_previous": []
                }
            },
            "recommended_version_for_new_integrations": "v3.0",
            "developer_message": "v3.0 is now our current stable version and recommended for all new integrations. v2.5 remains supported but is now in maintenance mode."
        },
        # After v2.0 end of life
        {
            "current_version": "v3.0",
            "status": "current",
            "latest_stable": "v3.0",
            "latest_beta": "v3.1-beta.2",
            "versions": {
                "v1.0": {
                    "release_date": "2023-04-15",
                    "end_of_life": "2024-04-15",
                    "status": "removed",
                    "documentation_url": "https://api.example.com/docs/archived/v1",
                    "major_features": ["Basic CRUD operations", "Authentication", "Rate limiting"],
                    "breaking_changes_from_previous": []
                },
                "v2.0": {
                    "release_date": "2024-02-01",
                    "end_of_life": "2025-08-01",
                    "status": "removed",
                    "documentation_url": "https://api.example.com/docs/archived/v2",
                    "major_features": ["Batch operations", "Webhooks", "Extended metadata", "Advanced filtering"],
                    "breaking_changes_from_previous": [
                        "Authentication method changed from API key to OAuth2",
                        "Response format standardized to envelope pattern",
                        "Rate limits implemented per endpoint instead of globally"
                    ]
                },
                "v2.5": {
                    "release_date": "2025-01-10",
                    "end_of_life": "2026-07-10",
                    "status": "maintenance",
                    "documentation_url": "https://api.example.com/docs/v2.5",
                    "major_features": ["Real-time notifications", "Expanded search capabilities", "Performance improvements"],
                    "breaking_changes_from_previous": [
                        "Search parameter format changed",
                        "Pagination tokens now required for lists over 100 items"
                    ]
                },
                "v3.0": {
                    "release_date": "2025-07-15",
                    "end_of_life": "2027-07-15",
                    "status": "current",
                    "documentation_url": "https://api.example.com/docs/v3",
                    "major_features": ["GraphQL support", "Streaming responses", "Advanced analytics", "Improved error handling"],
                    "breaking_changes_from_previous": [
                        "Complete overhaul of resource paths",
                        "New authentication system using JWT",
                        "Response format changed to support GraphQL",
                        "Error response format changed",
                        "Rate limiting logic adjusted"
                    ]
                },
                "v3.1-beta.2": {
                    "release_date": "2025-09-10",
                    "status": "beta",
                    "documentation_url": "https://api.example.com/docs/v3.1-beta",
                    "major_features": ["AI-assisted query generation", "Advanced caching mechanisms", "New notification channels", "Enhanced security features"],
                    "breaking_changes_from_previous": []
                }
            },
            "deprecation_schedule": [
                {"version": "v1.0", "deprecation_date": "2024-04-15", "removal_date": "2024-07-15", "migration_guide": "https://api.example.com/migration/v1-to-v2"},
                {"version": "v2.0", "deprecation_date": "2025-08-01", "removal_date": "2026-02-01", "migration_guide": "https://api.example.com/migration/v2-to-v2.5"},
                {"version": "v2.5", "deprecation_date": "2026-07-10", "removal_date": "2027-01-10", "migration_guide": "https://api.example.com/migration/v2.5-to-v3"}
            ],
            "recommended_version_for_new_integrations": "v3.0",
            "developer_message": "IMPORTANT: v2.0 has reached end-of-life and is no longer accessible. All v2.0 integrations must migrate to v2.5 or v3.0 immediately. v2.5 will continue to be supported until July 2026."
        }
    ]
)

Business Application: For software developers managing API documentation, this variable ensures accurate version information:

Human: "Which API version should we use for our new integration?"
AI: [Checks current date against API_VERSION_INFO stages and provides appropriate recommendation]
Human: "How long will v2.0 be supported?"
AI: "Based on the current deprecation schedule, v2.0 will reach end-of-life on August 1, 2025, which is [X] days from now. It will be completely removed from service on February 1, 2026."
Human: "What new features will be available in v3.0?"
AI: [Provides feature information from the current API_VERSION_INFO stage]

This approach ensures that API documentation and recommendations automatically evolve as versions move through their lifecycle, from beta to current to maintenance to deprecated to removed, without requiring manual updates as these transitions occur.

Advanced Examples and Applications

Example 1: Time-Based Content Publishing Schedule

A time-sensitive variable that manages a content calendar with automatic publishing status updates:

# Create a time-sensitive content publishing schedule
CONTENT_CALENDAR = create_time_sensitive_variable(
    "CONTENT_CALENDAR",
    {
        "content_items": [
            {
                "id": "BLOG-2025-05-01",
                "title": "Introduction to Time-Sensitive Variables in AI Conversations",
                "type": "blog_post",
                "author": "Sarah Chen",
                "status": "published",
                "scheduled_date": "2025-05-01T09:00:00",
                "publish_date": "2025-05-01T09:05:23",
                "categories": ["AI", "Programming", "Tutorials"],
                "summary": "Learn how time-sensitive variables can transform your AI conversations by adding temporal awareness and automatic evolution.",
                "url": "https://example.com/blog/time-sensitive-variables-introduction",
                "performance_metrics": {
                    "views": 1250,
                    "shares": 87,
                    "comments": 32,
                    "conversion_rate": 0.038
                }
            },
            {
                "id": "VIDEO-2025-05-08",
                "title": "5 Ways to Optimize Your Summer Marketing Strategy",
                "type": "video",
                "author": "Michael Rodriguez",
                "status": "scheduled",
                "scheduled_date": "2025-05-15T14:00:00",
                "categories": ["Marketing", "Seasonal", "Strategy"],
                "summary": "Discover five proven approaches to make your summer marketing campaigns more effective while maintaining budget efficiency.",
                "thumbnail_url": "https://example.com/thumbnails/summer-marketing.jpg",
                "video_length": "18:45",
                "publishing_platform": "YouTube"
            },
            {
                "id": "NEWSLETTER-2025-05-22",
                "title": "May Product Updates and Industry Trends",
                "type": "newsletter",
                "author": "Product Team",
                "status": "draft",
                "scheduled_date": "2025-05-22T07:00:00",
                "categories": ["Product Updates", "Industry News"],
                "summary": "Monthly roundup of new features, improvements, and insights into the latest industry developments.",
                "completion_percentage": 65,
                "assigned_editor": "Alex Johnson",
                "review_deadline": "2025-05-18T17:00:00"
            },
            {
                "id": "WEBINAR-2025-06-05",
                "title": "Advanced Techniques for Customer Retention",
                "type": "webinar",
                "speakers": ["Sarah Chen", "David Kim"],
                "status": "planning",
                "scheduled_date": "2025-06-05T11:00:00",
                "categories": ["Customer Success", "Training"],
                "summary": "Expert strategies for reducing churn and increasing customer lifetime value in subscription businesses.",
                "registration_url": "https://example.com/webinars/customer-retention",
                "target_attendance": 500,
                "current_registrations": 86,
                "preparation_tasks": [
                    {"task": "Finalize presentation deck", "assignee": "Sarah Chen", "deadline": "2025-05-28", "status": "in_progress"},
                    {"task": "Create promotional email sequence", "assignee": "Marketing Team", "deadline": "2025-05-20", "status": "not_started"},
                    {"task": "Set up webinar registration page", "assignee": "Web Team", "deadline": "2025-05-15", "status": "completed"}
                ]
            }
        ],
        "content_types": {
            "blog_post": {"frequency": "weekly", "average_production_time": 5, "team": "Content", "approval_required": true},
            "video": {"frequency": "bi-weekly", "average_production_time": 10, "team": "Media", "approval_required": true},
            "newsletter": {"frequency": "monthly", "average_production_time": 3, "team": "Marketing", "approval_required": true},
            "webinar": {"frequency": "monthly", "average_production_time": 15, "team": "Education", "approval_required": true},
            "social_media": {"frequency": "daily", "average_production_time": 1, "team": "Social", "approval_required": false}
        },
        "upcoming_schedule": [
            {"week": "May 13-19", "planned_content": ["VIDEO-2025-05-15", "Social media posts x5"]},
            {"week": "May 20-26", "planned_content": ["NEWSLETTER-2025-05-22", "Blog post: Customer Success Stories", "Social media posts x5"]},
            {"week": "May 27-June 2", "planned_content": ["Blog post: Summer Product Preview

Time-Based Content Publishing Schedule

Let me continue with the advanced example for a time-sensitive content publishing schedule:

# Create a time-sensitive content publishing schedule (continued)
CONTENT_CALENDAR = create_time_sensitive_variable(
    "CONTENT_CALENDAR",
    {
        "content_items": [
            {
                "id": "BLOG-2025-05-01",
                "title": "Introduction to Time-Sensitive Variables in AI Conversations",
                "type": "blog_post",
                "author": "Sarah Chen",
                "status": "published",
                "scheduled_date": "2025-05-01T09:00:00",
                "publish_date": "2025-05-01T09:05:23",
                "categories": ["AI", "Programming", "Tutorials"],
                "summary": "Learn how time-sensitive variables can transform your AI conversations by adding temporal awareness and automatic evolution.",
                "url": "https://example.com/blog/time-sensitive-variables-introduction",
                "performance_metrics": {
                    "views": 1250,
                    "shares": 87,
                    "comments": 32,
                    "conversion_rate": 0.038
                }
            },
            {
                "id": "VIDEO-2025-05-08",
                "title": "5 Ways to Optimize Your Summer Marketing Strategy",
                "type": "video",
                "author": "Michael Rodriguez",
                "status": "scheduled",
                "scheduled_date": "2025-05-15T14:00:00",
                "categories": ["Marketing", "Seasonal", "Strategy"],
                "summary": "Discover five proven approaches to make your summer marketing campaigns more effective while maintaining budget efficiency.",
                "thumbnail_url": "https://example.com/thumbnails/summer-marketing.jpg",
                "video_length": "18:45",
                "publishing_platform": "YouTube"
            },
            {
                "id": "NEWSLETTER-2025-05-22",
                "title": "May Product Updates and Industry Trends",
                "type": "newsletter",
                "author": "Product Team",
                "status": "draft",
                "scheduled_date": "2025-05-22T07:00:00",
                "categories": ["Product Updates", "Industry News"],
                "summary": "Monthly roundup of new features, improvements, and insights into the latest industry developments.",
                "completion_percentage": 65,
                "assigned_editor": "Alex Johnson",
                "review_deadline": "2025-05-18T17:00:00"
            },
            {
                "id": "WEBINAR-2025-06-05",
                "title": "Advanced Techniques for Customer Retention",
                "type": "webinar",
                "speakers": ["Sarah Chen", "David Kim"],
                "status": "planning",
                "scheduled_date": "2025-06-05T11:00:00",
                "categories": ["Customer Success", "Training"],
                "summary": "Expert strategies for reducing churn and increasing customer lifetime value in subscription businesses.",
                "registration_url": "https://example.com/webinars/customer-retention",
                "target_attendance": 500,
                "current_registrations": 86,
                "preparation_tasks": [
                    {"task": "Finalize presentation deck", "assignee": "Sarah Chen", "deadline": "2025-05-28", "status": "in_progress"},
                    {"task": "Create promotional email sequence", "assignee": "Marketing Team", "deadline": "2025-05-20", "status": "not_started"},
                    {"task": "Set up webinar registration page", "assignee": "Web Team", "deadline": "2025-05-15", "status": "completed"}
                ]
            }
        ],
        "content_types": {
            "blog_post": {"frequency": "weekly", "average_production_time": 5, "team": "Content", "approval_required": true},
            "video": {"frequency": "bi-weekly", "average_production_time": 10, "team": "Media", "approval_required": true},
            "newsletter": {"frequency": "monthly", "average_production_time": 3, "team": "Marketing", "approval_required": true},
            "webinar": {"frequency": "monthly", "average_production_time": 15, "team": "Education", "approval_required": true},
            "social_media": {"frequency": "daily", "average_production_time": 1, "team": "Social", "approval_required": false}
        },
        "upcoming_schedule": [
            {"week": "May 13-19", "planned_content": ["VIDEO-2025-05-15", "Social media posts x5"]},
            {"week": "May 20-26", "planned_content": ["NEWSLETTER-2025-05-22", "Blog post: Customer Success Stories", "Social media posts x5"]},
            {"week": "May 27-June 2", "planned_content": ["Blog post: Summer Product Preview", "Social media posts x5"]},
            {"week": "June 3-9", "planned_content": ["WEBINAR-2025-06-05", "Blog post: Webinar Recap", "Social media posts x5"]}
        ],
        "last_update": "2025-05-11T15:30:45",
        "status_update_functions": {
            "check_publish_status": lambda item, current_time: 
                "published" if current_time > datetime.fromisoformat(item["scheduled_date"]) else item["status"],
                
            "check_deadline_status": lambda item, current_time:
                "overdue" if item.get("review_deadline") and current_time > datetime.fromisoformat(item["review_deadline"]) and item["status"] == "draft" else item["status"]
        }
    },
    # Expires at end of quarter
    expiration=datetime(2025, 6, 30).timestamp(),
    # Evolution stages based on publishing calendar progression
    evolution_stages=[
        # Current stage (May 11, 2025)
        {
            "period": "Mid-May 2025",
            "focus_themes": ["Summer Preparation", "Product Updates", "Customer Success"],
            "priority_content": ["VIDEO-2025-05-15", "NEWSLETTER-2025-05-22"],
            "upcoming_deadlines": [
                {"content_id": "VIDEO-2025-05-15", "deadline_type": "publish", "date": "2025-05-15T14:00:00"},
                {"content_id": "NEWSLETTER-2025-05-22", "deadline_type": "review", "date": "2025-05-18T17:00:00"},
                {"content_id": "NEWSLETTER-2025-05-22", "deadline_type": "publish", "date": "2025-05-22T07:00:00"}
            ],
            "content_status_summary": {
                "published": 1,
                "scheduled": 1,
                "draft": 1,
                "planning": 1
            },
            "recommendations": [
                "Complete review of May newsletter by May 18",
                "Begin planning for June content calendar",
                "Promote upcoming webinar registration"
            ]
        },
        # Late May (after newsletter publication)
        {
            "period": "Late-May 2025",
            "focus_themes": ["Summer Strategy", "Webinar Preparation", "Customer Success"],
            "priority_content": ["WEBINAR-2025-06-05", "Blog post: Summer Product Preview"],
            "upcoming_deadlines": [
                {"content_id": "WEBINAR-2025-06-05", "deadline_type": "presentation", "date": "2025-05-28T17:00:00"},
                {"content_id": "Blog post: Summer Product Preview", "deadline_type": "draft", "date": "2025-05-29T17:00:00"},
                {"content_id": "WEBINAR-2025-06-05", "deadline_type": "event", "date": "2025-06-05T11:00:00"}
            ],
            "content_status_summary": {
                "published": 3,  # Blog, Video, Newsletter now published
                "scheduled": 0,
                "draft": 0,
                "planning": 1  # Webinar still in planning
            },
            "recommendations": [
                "Complete webinar preparation tasks",
                "Begin planning for July content calendar",
                "Analyze performance of May content for insights"
            ]
        },
        # Early June (after webinar)
        {
            "period": "Early-June 2025",
            "focus_themes": ["Summer Implementation", "Webinar Follow-up", "Q3 Planning"],
            "priority_content": ["Blog post: Webinar Recap", "July content planning"],
            "upcoming_deadlines": [
                {"content_id": "Blog post: Webinar Recap", "deadline_type": "publish", "date": "2025-06-07T09:00:00"},
                {"content_id": "July Newsletter", "deadline_type": "planning", "date": "2025-06-10T17:00:00"},
                {"content_id": "Q3 Content Calendar", "deadline_type": "draft", "date": "2025-06-15T17:00:00"}
            ],
            "content_status_summary": {
                "published": 4,  # Blog, Video, Newsletter, Webinar now published
                "scheduled": 0,
                "draft": 0,
                "planning": 0
            },
            "recommendations": [
                "Prepare Q3 content strategy based on Q2 performance",
                "Plan summer campaign content series",
                "Review webinar attendance and engagement metrics"
            ]
        }
    ]
)

# Function to update content status based on current time
def update_content_status(content_calendar):
    """Update the status of content items based on the current time"""
    from datetime import datetime
    
    current_time = datetime.now()
    updated = False
    
    for item in content_calendar["content_items"]:
        # Check if item should be published
        if item["status"] == "scheduled" and current_time > datetime.fromisoformat(item["scheduled_date"]):
            item["status"] = "published"
            item["publish_date"] = current_time.isoformat()
            updated = True
            
        # Check if review is overdue
        if item["status"] == "draft" and "review_deadline" in item and current_time > datetime.fromisoformat(item["review_deadline"]):
            item["status"] = "review_overdue"
            updated = True
            
    if updated:
        content_calendar["last_update"] = current_time.isoformat()
        
    return content_calendar

# Function to generate content reports
def generate_content_report(content_calendar, report_type="summary"):
    """Generate different types of content reports"""
    from datetime import datetime
    
    current_time = datetime.now()
    
    if report_type == "summary":
        # Count items by status
        status_counts = {}
        for item in content_calendar["content_items"]:
            status_counts[item["status"]] = status_counts.get(item["status"], 0) + 1
            
        # Find upcoming deadlines
        upcoming_deadlines = []
        for item in content_calendar["content_items"]:
            if "scheduled_date" in item and item["status"] != "published":
                days_until = (datetime.fromisoformat(item["scheduled_date"]) - current_time).days
                if days_until >= 0:
                    upcoming_deadlines.append({
                        "id": item["id"],
                        "title": item["title"],
                        "deadline": item["scheduled_date"],
                        "days_until": days_until,
                        "status": item["status"]
                    })
        
        # Sort by days until deadline
        upcoming_deadlines.sort(key=lambda x: x["days_until"])
        
        return {
            "as_of": current_time.isoformat(),
            "content_by_status": status_counts,
            "upcoming_deadlines": upcoming_deadlines[:5],  # Top 5 upcoming deadlines
            "current_focus": get_current_focus(content_calendar, current_time)
        }
    
    elif report_type == "performance":
        # Get performance metrics for published content
        performance = []
        for item in content_calendar["content_items"]:
            if item["status"] == "published" and "performance_metrics" in item:
                performance.append({
                    "id": item["id"],
                    "title": item["title"],
                    "type": item["type"],
                    "publish_date": item.get("publish_date"),
                    "metrics": item["performance_metrics"]
                })
                
        # Sort by publish date (newest first)
        performance.sort(key=lambda x: x.get("publish_date", ""), reverse=True)
        
        return {
            "as_of": current_time.isoformat(),
            "performance_data": performance,
            "average_metrics": calculate_average_metrics(performance)
        }
    
    return {"error": "Unknown report type"}

# Helper function to get current focus based on date
def get_current_focus(content_calendar, current_time):
    """Determine the current content focus based on date"""
    # Find which evolution stage we're in based on date
    for stage in reversed(content_calendar.get("evolution_stages", [])):
        if "period" in stage:
            # Simple string matching for example purposes
            # In a real implementation, this would use actual date ranges
            if "Early-June" in stage["period"] and current_time.month == 6 and current_time.day < 15:
                return stage["focus_themes"]
            elif "Late-May" in stage["period"] and (current_time.month == 5 and current_time.day >= 20):
                return stage["focus_themes"]
            elif "Mid-May" in stage["period"] and (current_time.month == 5 and 10 <= current_time.day < 20):
                return stage["focus_themes"]
    
    return ["Content creation", "Audience engagement"]  # Default focus

# Helper function to calculate average metrics
def calculate_average_metrics(performance_data):
    """Calculate average performance metrics across content"""
    if not performance_data:
        return {}
        
    metrics = {}
    counts = {}
    
    for item in performance_data:
        for metric, value in item["metrics"].items():
            if isinstance(value, (int, float)):
                metrics[metric] = metrics.get(metric, 0) + value
                counts[metric] = counts.get(metric, 0) + 1
    
    # Calculate averages
    averages = {metric: metrics[metric] / counts[metric] for metric in metrics}
    
    return averages

Business Application: For content marketing teams, this variable automatically updates content status based on publishing schedules:

Human: "What content is scheduled to publish this week?"
AI: [Checks current date against CONTENT_CALENDAR and updates statuses using update_content_status before responding]
Human: "Is the newsletter on track for publishing on May 22nd?"
AI: [Checks newsletter status and deadlines] "The May newsletter is currently in draft status with a 65% completion rate. Its review deadline is May 18th, which is in [X] days. The assigned editor is Alex Johnson."
Human: "Generate a content performance report for published items."
AI: [Creates report using generate_content_report(CONTENT_CALENDAR, "performance")]

This time-sensitive variable automatically advances content items through their workflow stages as time passes, transitioning items from "scheduled" to "published" when their publishing date arrives and flagging items as "review_overdue" when deadlines are missed. It also automatically evolves its focus themes and recommendations as the content calendar progresses through different periods.

Example 2: Dynamic Regulatory Compliance Calendar

A time-sensitive variable that tracks complex regulatory deadlines and requirements:

python

# Create a dynamic regulatory compliance calendar
COMPLIANCE_CALENDAR = create_time_sensitive_variable(
    "COMPLIANCE_CALENDAR",
    {
        "company_profile": {
            "industry": "Financial Technology",
            "size": "Medium (250 employees)",
            "jurisdictions": ["United States", "European Union", "United Kingdom", "Australia"],
            "data_types": ["PII", "Financial", "Transaction", "Behavioral"],
            "services": ["Payment Processing", "Lending", "Investment Management", "Financial Analytics"]
        },
        "current_year": 2025,
        "regulatory_frameworks": [
            {
                "name": "GDPR",
                "full_name": "General Data Protection Regulation",
                "jurisdiction": "European Union",
                "category": "Data Privacy",
                "compliance_status": "Compliant",
                "last_audit": "2025-02-15",
                "next_audit": "2026-02-15",
                "key_requirements": [
                    "Data processing register",
                    "Privacy impact assessments",
                    "Data subject access request mechanism",
                    "72-hour breach notification process",
                    "Data protection officer appointment"
                ],
                "documentation_url": "https://example.com/compliance/gdpr"
            },
            {
                "name": "PCI DSS",
                "full_name": "Payment Card Industry Data Security Standard",
                "jurisdiction": "Global",
                "category": "Payment Security",
                "compliance_status": "Compliant",
                "last_audit": "2024-11-30",
                "next_audit": "2025-11-30",
                "certification_expiry": "2025-11-30",
                "key_requirements": [
                    "Network security controls",
                    "Vulnerability management program",
                    "Strong access controls",
                    "Regular security testing",
                    "Information security policy"
                ],
                "documentation_url": "https://example.com/compliance/pcidss"
            },
            {
                "name": "SOC 2",
                "full_name": "Service Organization Control 2",
                "jurisdiction": "United States",
                "category": "Security & Privacy Controls",
                "compliance_status": "Renewal in progress",
                "last_audit": "2024-07-15",
                "next_audit": "2025-07-15",
                "certification_expiry": "2025-07-31",
                "key_requirements": [
                    "Security, availability, and confidentiality controls",
                    "Risk management procedures",
                    "Vendor management program",
                    "Incident response planning",
                    "Business continuity preparations"
                ],
                "documentation_url": "https://example.com/compliance/soc2"
            },
            {
                "name": "AML/KYC",
                "full_name": "Anti-Money Laundering/Know Your Customer",
                "jurisdiction": "Global",
                "category": "Financial Crime Prevention",
                "compliance_status": "Compliant",
                "last_audit": "2025-01-20",
                "next_audit": "2026-01-20",
                "key_requirements": [
                    "Customer identification program",
                    "Transaction monitoring system",
                    "Suspicious activity reporting",
                    "Risk-based customer due diligence",
                    "Ongoing monitoring procedures"
                ],
                "documentation_url": "https://example.com/compliance/amlkyc"
            }
        ],
        "upcoming_deadlines": [
            {
                "title": "SOC 2 Type II Audit Preparation",
                "framework": "SOC 2",
                "deadline": "2025-05-30",
                "priority": "high",
                "description": "Complete pre-audit readiness assessment and documentation review for upcoming SOC 2 Type II audit.",
                "assigned_to": "Security & Compliance Team",
                "status": "in_progress",
                "completion_percentage": 45
            },
            {
                "title": "Quarterly PCI Vulnerability Scan",
                "framework": "PCI DSS",
                "deadline": "2025-06-30",
                "priority": "medium",
                "description": "Complete Q2 2025 vulnerability scanning by approved scanning vendor.",
                "assigned_to": "Security Operations",
                "status": "scheduled",
                "completion_percentage": 0
            },
            {
                "title": "GDPR Data Processing Inventory Update",
                "framework": "GDPR",
                "deadline": "2025-08-15",
                "priority": "medium",
                "description": "Semi-annual review and update of data processing activities register to reflect new products and services.",
                "assigned_to": "Data Protection Officer",
                "status": "not_started",
                "completion_percentage": 0
            },
            {
                "title": "AML Transaction Monitoring System Upgrade",
                "framework": "AML/KYC",
                "deadline": "2025-09-30",
                "priority": "high",
                "description": "Implement enhanced transaction monitoring capabilities to meet updated regulatory guidelines.",
                "assigned_to": "Financial Crime Prevention Team",
                "status": "planning",
                "completion_percentage": 10
            }
        ],
        "regulatory_updates": [
            {
                "framework": "PCI DSS",
                "update_title": "PCI DSS 4.0 Transition Deadline Approaching",
                "publication_date": "2025-03-15",
                "effective_date": "2026-03-31",
                "summary": "PCI DSS 4.0 includes significant changes to authentication requirements, encryption standards, and introduces customized implementation options. All organizations must transition from PCI DSS 3.2.1 to 4.0 by March 31, 2026.",
                "impact_assessment": "High - Requires significant updates to security controls and documentation",
                "action_items": [
                    "Complete gap analysis between current controls and PCI DSS 4.0 (due 2025-06-30)",
                    "Develop transition roadmap with milestones (due 2025-07-31)",
                    "Begin implementing enhanced authentication requirements (due 2025-12-31)"
                ],
                "documentation_url": "https://example.com/compliance/pcidss4transition"
            },
            {
                "framework": "GDPR",
                "update_title": "Updated Guidelines on AI and Automated Decision Making",
                "publication_date": "2025-04-10",
                "effective_date": "2025-10-10",
                "summary": "The European Data Protection Board has released updated guidelines on artificial intelligence and automated decision-making under GDPR Article 22, establishing new requirements for algorithm transparency, human oversight, and impact assessments.",
                "impact_assessment": "Medium - Affects AI-powered financial analytics services",
                "action_items": [
                    "Review AI analytics systems for compliance with new guidelines (due 2025-07-31)",
                    "Update algorithm documentation and transparency disclosures (due 2025-08-31)",
                    "Implement enhanced human oversight protocols for automated decisions (due 2025-09-30)"
                ],
                "documentation_url": "https://example.com/compliance/gdpr-ai-guidelines"
            }
        ],
        "recurring_obligations": [
            {
                "title": "PCI Quarterly Vulnerability Scan",
                "framework": "PCI DSS",
                "frequency": "quarterly",
                "next_occurrences": [
                    "2025-06-30",
                    "2025-09-30",
                    "2025-12-31",
                    "2026-03-31"
                ],
                "description": "Approved scanning vendor must perform external vulnerability scan of all in-scope systems.",
                "assigned_to": "Security Operations",
                "evidence_required": ["Scan reports", "Remediation documentation"]
            },
            {
                "title": "AML Transaction Monitoring Review",
                "framework": "AML/KYC",
                "frequency": "monthly",
                "next_occurrences": [
                    "2025-05-31",
                    "2025-06-30",
                    "2025-07-31",
                    "2025-08-31"
                ],
                "description": "Review flagged transactions and suspicious activity reports.",
                "assigned_to": "Financial Crime Prevention Team",
                "evidence_required": ["Review logs", "Investigation documentation", "SAR filing records"]
            },
            {
                "title": "Security Awareness Training",
                "framework": "Multiple",
                "frequency": "quarterly",
                "next_occurrences": [
                    "2025-06-30",
                    "2025-09-30",
                    "2025-12-31",
                    "2026-03-31"
                ],
                "description": "Conduct security awareness training for all employees covering data protection, incident reporting, and social engineering defenses.",
                "assigned_to": "Security & Compliance Team",
                "evidence_required": ["Training materials", "Attendance records", "Quiz results"]
            }
        ],
        "last_update": "2025-05-11"
    },
    # Expires at end of year
    expiration=datetime(2025, 12, 31).timestamp(),
    # Evolution stages based on time periods
    evolution_stages=[
        # Current stage (May 2025 - SOC 2 Preparation Focus)
        {
            "period": "Q2 2025 - SOC 2 Preparation",
            "high_priority_frameworks": ["SOC 2", "PCI DSS"],
            "key_milestones": [
                {"title": "SOC 2 Type II Audit Preparation", "deadline": "2025-05-30"},
                {"title": "Quarterly PCI Vulnerability Scan", "deadline": "2025-06-30"},
                {"title": "PCI DSS 4.0 Gap Analysis", "deadline": "2025-06-30"}
            ],
            "risk_assessment": {
                "overall_compliance_risk": "Moderate",
                "highest_risk_areas": [
                    "SOC 2 certification renewal timeline",
                    "PCI DSS 4.0 transition planning",
                    "AI-powered analytics GDPR compliance"
                ],
                "mitigation_recommendations": [
                    "Accelerate SOC 2 readiness assessment completion",
                    "Begin PCI DSS 4.0 gap analysis earlier than planned",
                    "Conduct focused review of AI systems against new GDPR guidelines"
                ]
            }
        },
        # Q3 2025 - GDPR and AML Focus
        {
            "period": "Q3 2025 - GDPR and AML Focus",
            "high_priority_frameworks": ["GDPR", "AML/KYC"],
            "key_milestones": [
                {"title": "GDPR Data Processing Inventory Update", "deadline": "2025-08-15"},
                {"title": "GDPR AI Guidelines Implementation", "deadline": "2025-09-30"},
                {"title": "AML Transaction Monitoring System Upgrade", "deadline": "2025-09-30"},
                {"title": "Quarterly PCI Vulnerability Scan", "deadline": "2025-09-30"}
            ],
            "risk_assessment": {
                "overall_compliance_risk": "Moderate",
                "highest_risk_areas": [
                    "GDPR AI and automated decision-making compliance",
                    "AML transaction monitoring system enhancements",
                    "Cross-border data transfers under updated regulations"
                ],
                "mitigation_recommendations": [
                    "Prioritize AI transparency documentation updates",
                    "Accelerate AML system upgrade implementation",
                    "Review international data transfer mechanisms"
                ]
            }
        },
        # Q4 2025 - Year-End Compliance Review
        {
            "period": "Q4 2025 - Year-End Compliance Review",
            "high_priority_frameworks": ["PCI DSS", "Multiple"],
            "key_milestones": [
                {"title": "Annual Compliance Program Review", "deadline": "2025-11-30"},
                {"title": "PCI DSS Annual Assessment", "deadline": "2025-11-30"},
                {"title": "2026 Compliance Roadmap Development", "deadline": "2025-12-15"},
                {"title": "Quarterly PCI Vulnerability Scan", "deadline": "2025-12-31"}
            ],
            "risk_assessment": {
                "overall_compliance_risk": "Low to Moderate",
                "highest_risk_areas": [
                    "PCI DSS certification renewal",
                    "2026 regulatory change planning",
                    "Resource allocation for compliance initiatives"
                ],
                "mitigation_recommendations": [
                    "Begin PCI DSS assessment preparation early",
                    "Conduct regulatory horizon scanning exercise",
                    "Secure budget allocation for 2026 compliance initiatives"
                ]
            }
        }
    ]
)

# Function to get upcoming compliance deadlines
def get_upcoming_deadlines(compliance_calendar, days_ahead=30, priority_filter=None):
    """Get upcoming compliance deadlines within the specified timeframe"""
    from datetime import datetime, timedelta
    
    current_date = datetime.now()
    end_date = current_date + timedelta(days=days_ahead)
    upcoming = []
    
    # Check explicit deadlines
    for deadline in compliance_calendar["upcoming_deadlines"]:
        deadline_date = datetime.fromisoformat(deadline["deadline"])
        if current_date <= deadline_date <= end_date:
            if priority_filter is None or deadline["priority"] == priority_filter:
                upcoming.append({
                    "title": deadline["title"],
                    "framework": deadline["framework"],
                    "deadline": deadline["deadline"],
                    "days_remaining": (deadline_date - current_date).days,
                    "priority": deadline["priority"],
                    "status": deadline["status"]
                })
    
    # Check recurring obligations
    for obligation in compliance_calendar["recurring_obligations"]:
        for occurrence in obligation["next_occurrences"]:
            occurrence_date = datetime.fromisoformat(occurrence)
            if current_date <= occurrence_date <= end_date:
                upcoming.append({
                    "title": obligation["title"],
                    "framework": obligation["framework"],
                    "deadline": occurrence,
                    "days_remaining": (occurrence_date - current_date).days,
                    "priority": "medium",  # Default priority for recurring obligations
                    "status": "scheduled",
                    "recurring": True
                })
    
    # Check regulatory updates with effective dates
    for update in compliance_calendar["regulatory_updates"]:
        if "effective_date" in update:
            effective_date = datetime.fromisoformat(update["effective_date"])
            if current_date <= effective_date <= end_date:
                upcoming.append({
                    "title": f"Effective Date: {update['update_title']}",
                    "framework": update["framework"],
                    "deadline": update["effective_date"],
                    "days_remaining": (effective_date - current_date).days,
                    "priority": "high",  # Regulatory changes are high priority
                    "status": "regulatory_change",
                    "update_summary": update["summary"]
                })
    
    # Sort by days remaining (ascending)
    upcoming.sort(key=lambda x: x["days_remaining"])
    
    return upcoming

# Function to generate compliance stage report
def generate_compliance_stage_report(compliance_calendar):
    """Generate a report based on the current compliance stage"""
    from datetime import datetime
    
    current_date = datetime.now()
    current_stage = None
    
    # Determine current stage based on date ranges
    # This is simplified for example purposes
    # In a real implementation, this would use actual date ranges
    for stage in compliance_calendar.get("evolution_stages", []):
        if "period" in stage:
            if "Q2" in stage["period"] and current_date.month in [4, 5, 6]:
                current_stage = stage
            elif "Q3" in stage["period"] and current_date.month in [7, 8, 9]:
                current_stage = stage
            elif "Q4" in stage["period"] and current_date.month in [10, 11, 12]:
                current_stage = stage
    
    if not current_stage:
        return {"error": "Could not determine current compliance stage"}
    
    # Generate report based on current stage
    return {
        "as_of": current_date.isoformat(),
        "current_period": current_stage["period"],
        "priority_frameworks": current_stage["high_priority_frameworks"],
        "key_milestones": current_stage["key_milestones"],
        "risk_assessment": current_stage["risk_assessment"],
        "upcoming_deadlines": get_upcoming_deadlines(compliance_calendar, days_ahead=30),
        "recommendations": current_stage["risk_assessment"]["mitigation_recommendations"]
    }

Business Application: For regulatory compliance teams, this variable provides time-sensitive compliance information:

Human: "What compliance deadlines do we have coming up in the next 30 days?"
AI: [Uses get_upcoming_deadlines(COMPLIANCE_CALENDAR, 30) to generate current deadline list]
Human: "What's our highest compliance risk area this quarter?"
AI: [Checks current date against COMPLIANCE_CALENDAR evolution stages and provides appropriate risk assessment]
Human: "Generate a compliance priority report for this period."
AI: [Creates report using generate_compliance_stage_report(COMPLIANCE_CALENDAR)]

This variable automatically transitions through different compliance focus periods based on the calendar date, highlighting different regulatory frameworks, deadlines, and risk areas as the year progresses. This ensures compliance teams are always focused on the most time-relevant regulatory requirements without requiring manual reprioritization.

Smarter Inputs: Functions That Understand Your World

Traditional functions take inputs, or parameters. CRAFT™️ functions take this to a new level:

Speak Their Language with CRAFT Data Types: Imagine telling your AI to generate_marketing_copy and giving it not just a topic, but structured inputs like a TargetAudience object (e.g., "marketing professionals, knowledge_level='intermediate'") or a specific AITone ("friendly, professional, concise"). This means less ambiguity and more targeted results, making the AI feel like a true partner that gets your project's specifics. This directly supports "Accessibility Through Familiarity" by using clear, defined terms.
Building Blocks for Brilliance (Higher-Order Functions): Think about functions that can accept other functions or entire CRAFT objects as inputs. You could have a master function apply_brand_voice(text_input, brand_style_function) that uses another specific function to ensure every piece of content is perfectly on-brand. This modularity is key to "Structure Enables Freedom".
Context is King (Context-Aware Defaults): What if functions could intelligently grab default values from your ongoing PROJECT_VARIABLES or ConversationHistory? A summarize_text(text) function might automatically know your preferred summary length, saving you tokens and time – a nod to "Conservation of Tokens" and "Cumulative Intelligence".

Dynamic Outputs: Getting More Than Just Text Back

CRAFT™️ functions don’t just talk; they do. Their "outputs" can be incredibly dynamic:

Structured Results, Ready to Use: Instead of a simple text response, a function like analyze_competitor_website(url=URL("somecompetitor.com")) could return a rich ComparisonMatrix object, pre-filled and ready for your strategic review. This makes the AI's output immediately actionable.
Evolving Your Project, Automatically: Some functions might work behind the scenes to update your project's KnowledgeGraphNode or modify key PROJECT_VARIABLES. This helps build that "Cumulative Intelligence" session after session.
Kickstarting Complex AI Actions: Imagine a single CRAFT function call, like launch_product_feedback_sequence(product_name, customer_list_object), initiating an entire AIWorkflow – from drafting emails to analyzing responses. This level of abstraction is a huge time-saver.
Seamless Session Handoffs: As we discussed in the project files, CRAFT™️ uses a "Handoff" system. A specialized function could even help draft the SESSION_SUMMARY for these handoffs, ensuring smoother continuity.

Next-Level Reusability & Organization: Your AI Efficiency Toolkit

The core beauty of functions is reusability, and CRAFT™️ supercharges this:

Your Personal Prompt Library Snippets: Functions can act as "prompt snippets" or pre-defined instruction blocks. Your build_expert_prompt function is a perfect example of this. Need a standard legal disclaimer or SEO optimization instructions? Call a function. This is "Conservation of Tokens" in its prime.
Building Big Ideas from Small Pieces (Nested Functions): Decompose massive tasks into smaller, manageable functions. A create_full_blog_post(topic) function could internally call sub-functions for keyword research, outline generation, drafting, and SEO, making complex projects feel less daunting.
Domain-Specific Function Hubs: As your CRAFT™️ project grows, you can organize functions into thematic libraries – Marketing_Magic_Functions, Financial_Analysis_Functions, and so on – building a powerful, tailored toolkit.

Enhanced Clarity & Abstraction: Simplifying the Complex

CRAFT™️ functions are designed to make sophisticated AI interactions feel simpler:

One Call, Many Steps (Encapsulating Interactions): A single function could represent an entire multi-step dialogue with the AI. Think negotiate_contract_terms(parameters) where the function handles the back-and-forth, guided by your initial parameters.
Talk Naturally (Natural Language Aliases): While CRAFT™️ uses a Python-like syntax for precision, you could have the AI trained to understand more natural invocations, like "AI, summarize this for a CEO" mapping to a specific create_tldr_summary(text, audience_profile="CEO") function.
Evolve Without Breaking (Function Versioning): Just like software, your CRAFT™️ functions can have versions (e.g., generate_image_prompt_v2(description)). This lets you innovate and refine your prompts without disrupting workflows that rely on older versions, crucial for "Cumulative Intelligence".

Building a True Human-AI Partnership

Ultimately, CRAFT™️ functions are about fostering a more powerful "Human-AI Partnership":

AI as Your Co-Pilot (AI-Suggested Functions): The AI itself can become an active partner by suggesting relevant functions from your CRAFT library during your conversation (extending concepts like "#AI->H::RecommendMakingThisAFunction:" ).
Functions That Learn (An Advanced Glimpse): Imagine functions whose internal logic or default behaviors subtly adapt over time based on your feedback and their success rates, becoming even more attuned to your needs.
Keeping CRAFT™️ Sharp (Metacognitive Functions): You can even develop functions to manage and analyze your CRAFT™️ project itself, like validate_my_craft_syntax(code_block) or find_related_project_assets(keyword).

The Future is Functional (and Conversational!)

By adapting the familiar concept of functions, CRAFT™️ aims to provide the structure that enables true creative freedom and efficiency in your AI interactions. It’s about moving beyond simple question-and-answer and into a realm where you and your AI are co-creating complex solutions with greater ease and precision.

Adapting Functions for the CRAFT Framework

Enhanced Input/Parameterization:
- Using CRAFT Data Types as Parameters.
- Functions Accepting Other Functions or Objects as Input.
- Context-Aware Default Parameters.
Sophisticated Output and Return "Values":
- Returning Structured CRAFT Objects or Data Types.
- Functions that Modify Project State or Variables.
- Functions Triggering AI Actions or Workflows.
- Functions Generating "Handoff"-Compatible Summaries.
Deepening Reusability and Organization:
- Functions as "Prompt Snippets" or "Instruction Blocks."
- Nested Functions for Complex Task Decomposition.
- Libraries of Domain-Specific CRAFT Functions.
Elevating Abstraction and Clarity:
- Functions Encapsulating Multi-Step AI Interactions.
- "Natural Language" Aliases for Functions.
- Functions with Built-in Versioning.
Strengthening Human-AI Partnership & Cumulative Intelligence:
- AI-Suggested Function Use.
- Functions that Learn or Adapt Over Time.
- Functions for Metacognitive Tasks (e.g., review_consistency()).

Now, let's dive into the details of how these concepts can be adapted to CRAFT:

Creative Ways to Adapt Functions for the CRAFT Framework

Here are some brainstormed ideas, connecting them to traditional function benefits and the six CRAFT philosophical principles:

1. Enhanced Input/Parameterization

Using CRAFT Data Types as Parameters:
- Concept: Instead of just simple strings or numbers, functions can accept defined CRAFT Data Types as inputs (Source 63-129). For instance, a function to generate marketing copy might take TargetAudience (Source 92), AITone (Source 91), and Product_Object (a hypothetical CRAFT object) as parameters.
- Benefits:
  - Input: Strongly typed inputs improve clarity and reduce errors.
  - Reusability: Functions become more versatile by operating on standardized data structures.
- CRAFT Principles:
  - Code as Conversation (Source 12, 37): generate_ad_copy(audience=TargetAudience("entrepreneurs"), tone=AITone("persuasive")) is very clear.
  - Structure Enables Freedom (Source 11, 36): Predefined input types guide usage while allowing varied content.
  - Accessibility Through Familiarity (Source 15, 40): Mimics typed parameters in programming.
Functions Accepting Other Functions or Objects as Input (Higher-Order Functions):
- Concept: A CRAFT function could take another function's name or an entire CRAFT Object as an argument. For example, apply_style_guide(text_input, style_function=get_brand_voice_function).
- Benefits:
  - Abstraction & Reusability: Allows for flexible and composable behaviors.
- CRAFT Principles:
  - Structure Enables Freedom (Source 11, 36): Enables powerful, modular prompt construction.
  - Code as Conversation (Source 12, 37): Complex operations can be expressed more elegantly.
Context-Aware Default Parameters:
- Concept: Functions could have default parameter values that are dynamically set based on the current PROJECT_VARIABLES (Source 19 in CFT-PROJ-0001_BUILDINGCRAFT-v25d4.txt), ConversationHistory (Source 20-30), or even aspects of the UserProfile (Source 81). For example, def summarize_text(text, length="auto") where "auto" could default to a preferred summary length defined in UserProfile.
- Benefits:
  - Input: Simplifies function calls for common cases.
  - Organization: Reduces repetitive parameter specification.
- CRAFT Principles:
  - Cumulative Intelligence (Source 13, 38): Functions adapt based on past interactions or established project settings.
  - Conservation of Tokens (Source 17, 42, 132): Shorter function calls.

2. Sophisticated Output and Return "Values"

Returning Structured CRAFT Objects or Data Types:
- Concept: Instead of just text, a function can "return" a fully formed CRAFT Object or a specific Data Type. For example, analyze_competitor(url=URL("competitor.com")) could return a ComparisonMatrix object (Source 122-123) populated with data.
- Benefits:
  - Output: Provides structured, machine-readable, and immediately usable results within the CRAFT ecosystem.
  - Organization: Keeps related information bundled.
- CRAFT Principles:
  - Code as Conversation (Source 12, 37): Enables chaining of operations where the output of one function (an object) is the input to another.
  - Structure Enables Freedom (Source 11, 36): Standardized outputs allow for predictable downstream processing.
Functions that Modify Project State or Variables:
- Concept: Some functions might not return a direct value but instead modify PROJECT_VARIABLES or update a KnowledgeGraphNode (Source 114-115). For example, update_project_status(status_update="milestone_achieved").
- Benefits:
  - Output (Side Effects): Allows functions to manage and evolve the project's context directly.
- CRAFT Principles:
  - Cumulative Intelligence (Source 13, 38): Functions actively contribute to the evolving knowledge base of the project.
  - Human-AI Partnership (Source 14, 39): The AI can be instructed to maintain project state through functions.
Functions Triggering AI Actions or Workflows:
- Concept: A CRAFT function could encapsulate a call to an external AI service (via an APIRequest (Source 102) type, for example) or initiate a predefined AIWorkflow (Source 75, 110-111). E.g., execute_onboarding_workflow(customer_email=Email("new@example.com")).
- Benefits:
  - Abstraction & Automation: Complex sequences of AI actions are triggered by a simple function call.
- CRAFT Principles:
  - Structure Enables Freedom (Source 11, 36): Streamlines the execution of complex processes.
  - Conservation of Tokens (Source 17, 42, 132): A single function call replaces many lines of instruction.
Functions Generating "Handoff"-Compatible Summaries:
- Concept: A special function like generate_session_summary_for_handoff() could be designed to analyze the recent ConversationHistory (Source 20-30) and produce a draft for the SESSION_SUMMARY section of the HANDOFF_SNAPSHOT_PROMPT (Source 27-30).
- Benefits:
  - Automation & Organization: Streamlines the crucial handoff process.
- CRAFT Principles:
  - Cumulative Intelligence (Source 13, 38): Ensures better context preservation between sessions.
  - Human-AI Partnership (Source 14, 39): AI assists in maintaining project continuity.

3. Deepening Reusability and Organization

Functions as "Prompt Snippets" or "Instruction Blocks":
- Concept: Your existing build_expert_prompt (Source 133) is a great example. This can be expanded. Think of functions that return common instruction sets, like get_legal_disclaimer_text() or get_standard_seo_optimization_prompt().
- Benefits:
  - Reusability & Organization: Standardizes frequently used blocks of text or instructions.
- CRAFT Principles:
  - Conservation of Tokens (Source 17, 42, 132): Reduces repetition.
  - Structure Enables Freedom (Source 11, 36): Ensures consistency in boilerplate instructions.
Nested Functions for Complex Task Decomposition:
- Concept: Define complex tasks by composing smaller, more specific functions. For example, create_blog_post(topic) might internally call research_keywords(topic), generate_outline(keywords), draft_article(outline), and optimize_for_seo(draft).
- Benefits:
  - Organization & Abstraction: Breaks down complex problems into manageable, understandable parts.
- CRAFT Principles:
  - Code as Conversation (Source 12, 37): Makes the overall process clearer.
  - Structure Enables Freedom (Source 11, 36): Allows for easier modification and debugging of specific parts of a larger task.
Libraries of Domain-Specific CRAFT Functions:
- Concept: As the CRAFT project evolves, you could organize functions into "libraries" (perhaps just sections in your CFT-PROJ file or even separate linked files if the system supported it) based on domain or purpose (e.g., Marketing_Functions, Cybersecurity_Analysis_Functions).
- Benefits:
  - Organization & Reusability: Easier to find and reuse relevant functions.
- CRAFT Principles:
  - Cumulative Intelligence (Source 13, 38): Builds a powerful, organized toolkit over time.
  - Accessibility Through Familiarity (Source 15, 40): Users can learn and utilize relevant function sets.

4. Elevating Abstraction and Clarity

Functions Encapsulating Multi-Step AI Interactions:
- Concept: A single CRAFT function call could represent a sequence of back-and-forth interactions with the AI that would normally take multiple prompts. For example, negotiate_deal_terms(initial_offer, desired_outcome, constraints) might hide several rounds of AI-powered refinement.
- Benefits:
  - Abstraction: Hides complex interaction logic.
  - Conservation of Tokens (Source 17, 42, 132): Reduces the conversational overhead for the user.
- CRAFT Principles:
  - Human-AI Partnership (Source 14, 39): AI handles the detailed steps, user guides the overall process.
  - Structure Enables Freedom (Source 11, 36): Simplifies invoking complex AI behaviors.
"Natural Language" Aliases for Functions:
- Concept: While maintaining the Python-like syntax for definition, allow users to invoke functions using more natural phrasing that the AI is trained to map to the formal function call. For example, "AI, please summarize this article for a busy executive" could map to create_tldr_summary(article_text, audience_profile="executive").
- Benefits:
  - Accessibility: Lowers the barrier to using functions for non-technical users.
- CRAFT Principles:
  - Accessibility Through Familiarity (Source 15, 40): Offers an even more conversational interface.
  - Code as Conversation (Source 12, 37): Blurs the line further in a user-friendly way.
Functions with Built-in Versioning:
- Concept: Similar to your file versioning, CRAFT functions could have explicit versions, e.g., generate_image_prompt_v2(description). This allows for experimentation and improvement without breaking existing workflows that rely on older versions.
- Benefits:
  - Organization & Reusability: Manages changes and evolution effectively.
- CRAFT Principles:
  - Cumulative Intelligence (Source 13, 38): Allows the framework and its components to improve iteratively.
  - Structure Enables Freedom (Source 11, 36): Provides stability while enabling innovation.

5. Strengthening Human-AI Partnership & Cumulative Intelligence

AI-Suggested Function Use:
- Concept: The AI, based on the conversation or the user's described task, could suggest the use of an existing CRAFT function. "#AI->H::RecommendMakingThisAFunction:" (Source 56) is already a great start. This could be expanded to: "#AI->H::Suggestion: You could use the analyze_sentiment(text) function here. Would you like to proceed?"
- Benefits:
  - Human-AI Partnership (Source 14, 39): The AI actively helps the user leverage the CRAFT framework.
  - Accessibility: Helps users discover and learn available functions.
- CRAFT Principles:
  - Code as Conversation (Source 12, 37): The AI participates more actively in structuring the interaction.
Functions that Learn or Adapt Over Time (Advanced Concept):
- Concept: A CRAFT function's internal logic or default parameters could subtly adapt based on feedback or outcomes from previous calls (if the AI platform and CRAFT framework were designed to support this kind of learning). For instance, if a generate_social_media_post function consistently gets positive feedback for a certain style, it might subtly favor that style more.
- Benefits:
  - Cumulative Intelligence (Source 13, 38): Functions become more effective with use.
- CRAFT Principles:
  - Human-AI Partnership (Source 14, 39): The system refines itself based on collaborative experience.
Functions for Metacognitive Tasks:
- Concept: Functions that help manage or analyze the CRAFT process itself. Examples:
  - validate_craft_syntax(code_block)
  - find_related_project_assets(keyword)
  - check_prompt_for_constraints_alignment(prompt_text, constraint_set_name) (using ConstraintSet (Source 118-119))
- Benefits:
  - Organization & Human-AI Partnership: Helps maintain the integrity and efficiency of the CRAFT project.
- CRAFT Principles:
  - Structure Enables Freedom (Source 11, 36): Tools to ensure the framework is used effectively.
  - Cumulative Intelligence (Source 13, 38): Building tools to understand and manage the accumulated knowledge.

By creatively adapting functions in these ways, the CRAFT framework can become even more powerful, intuitive, and aligned with its core philosophical principles, ultimately enhancing the collaborative experience between you and the AI.

Creative Adaptations of Functions to the CRAFT Framework

Highlights

Context-Aware Functions: Functions that adapt their behavior based on conversation context
Multi-Modal Functions: Functions that can process and generate different types of content
Composable AI Instructions: Building complex AI behaviors from simpler function components
Handoff-Optimized Functions: Functions designed to preserve state between chat sessions
Reflection Functions: Functions that analyze and optimize AI performance
Token-Optimized Function Design: Function structures that minimize token usage
Natural Language Parameter Handling: Functions with flexible parameter interpretation
Progressive Function Enhancement: Functions that improve with repeated use
Collaborative Function Building: Functions that humans and AI can modify together
Function Libraries: Reusable function collections for common AI tasks

Detailed Exploration

1. Context-Aware Functions

Context-awareness allows functions to adapt based on the current conversation state, previous interactions, and detected user intent.

python

def generate_response(prompt, context=None, style=None, history=None):
    """
    Generates AI responses that automatically adapt to conversation context.
    
    #H->AI::Directive: (Analyze conversation history to determine appropriate tone)
    #H->AI::Constraint: (Maintain consistency with previous responses)
    """
    # Function implementation
    pass

This approach aligns with CRAFT principles by:

Reducing cognitive overhead (Structure Enables Freedom)
Creating natural conversation flow (Code as Conversation)
Building on previous exchanges (Cumulative Intelligence)

2. Multi-Modal Functions

Functions that seamlessly handle different content types and formats:

python

def transform_content(content, target_format, enhancement=None):
    """
    Transforms content between different formats (text, structured data, etc.)
    
    #H->AI::Context: (Identify the input format automatically)
    #H->AI::Focus: (Preserve semantic meaning while changing representation)
    """
    # Function implementation
    pass

Benefits:

Creates flexibility in how information is represented (Structure Enables Freedom)
Enables richer collaboration (Human-AI Partnership)
Abstracts complexity of format conversions (Accessibility Through Familiarity)

3. Composable AI Instructions

Building complex AI behaviors from simpler function components:

python

def chain_functions(*functions, input_data=None):
    """
    Combines multiple functions into a pipeline, where output of one becomes input to the next.
    
    #H->AI::Directive: (Process functions in sequence, passing outputs as inputs)
    #H->AI::Consider: (Handle errors gracefully with fallback options)
    """
    # Function implementation
    pass

This implements:

Modular approach to complex instructions (Structure Enables Freedom)
Natural building blocks for conversation (Code as Conversation)
Efficient reuse of common patterns (Conservation of Tokens)

4. Handoff-Optimized Functions

Functions designed specifically to preserve state between AI chat sessions:

python

def create_session_snapshot(key_variables, current_state, session_summary=None):
    """
    Creates a compact representation of current session state for handoff.
    
    #H->AI::Directive: (Identify and preserve critical context elements)
    #H->AI::Constraint: (Optimize for token efficiency in the handoff)
    """
    # Function implementation
    pass

This directly supports:

Maintaining continuity between sessions (Cumulative Intelligence)
Preserving collaboration context (Human-AI Partnership)
Minimizing repeated explanations (Conservation of Tokens)

5. Reflection Functions

Functions that analyze and optimize AI performance:

python

def evaluate_response_quality(prompt, response, criteria=None):
    """
    Evaluates the quality of AI responses against defined criteria.
    
    #H->AI::Directive: (Score response against clarity, relevance, accuracy)
    #H->AI::Focus: (Suggest specific improvements for future responses)
    """
    # Function implementation
    pass

Benefits:

Creates feedback loops for improvement (Cumulative Intelligence)
Enhances collaboration quality (Human-AI Partnership)
Provides structured framework for assessment (Structure Enables Freedom)

6. Token-Optimized Function Design

Function structures specifically designed to minimize token usage:

python

def compress_instruction(instruction, level=3):
    """
    Reduces token count of instructions while preserving core meaning.
    
    #H->AI::Directive: (Identify and remove redundant or non-essential elements)
    #H->AI::Constraint: (Maintain all critical semantic content)
    """
    # Function implementation
    pass

This directly implements:

Efficient use of token allowances (Conservation of Tokens)
Clarity through structure (Structure Enables Freedom)
Accessible optimization patterns (Accessibility Through Familiarity)

7. Natural Language Parameter Handling

Functions with flexible parameter interpretation:

python

def interpret_parameters(instruction, parameter_schema=None):
    """
    Extracts structured parameters from natural language instructions.
    
    #H->AI::Directive: (Identify parameter values even when not explicitly labeled)
    #H->AI::Consider: (Apply reasonable defaults for missing parameters)
    """
    # Function implementation
    pass

Benefits:

Bridges code and natural language (Code as Conversation)
Makes technical interaction more accessible (Accessibility Through Familiarity)
Reduces need for rigid syntax (Structure Enables Freedom)

8. Progressive Function Enhancement

Functions that improve with repeated use:

python

def adaptive_function(task, past_examples=None):
    """
    Adapts function behavior based on past usage patterns.
    
    #H->AI::Directive: (Analyze successful past applications to improve output)
    #H->AI::Context: (Consider patterns in how the user modifies outputs)
    """
    # Function implementation
    pass

This implements:

Learning from previous interactions (Cumulative Intelligence)
Growing partnership capabilities (Human-AI Partnership)
Evolving structure (Structure Enables Freedom)

9. Collaborative Function Building

Functions that humans and AI can modify together:

python

def suggest_function_improvements(function_definition, usage_data=None):
    """
    Analyzes function usage and suggests improvements.
    
    #H->AI::Directive: (Identify patterns where function could be enhanced)
    #H->AI::Focus: (Suggest specific modifications to parameters or implementation)
    """
    # Function implementation
    pass

Benefits:

True collaborative development (Human-AI Partnership)
Evolving capabilities (Cumulative Intelligence)
Accessible co-creation (Accessibility Through Familiarity)

10. Function Libraries

Reusable function collections for common AI tasks:

python

def import_function_library(domain, version=None):
    """
    Imports pre-defined function collections for specific domains.
    
    #H->AI::Directive: (Load standard functions for the specified domain)
    #H->AI::Consider: (Check compatibility with current CRAFT version)
    """
    # Function implementation
    pass

This supports:

Building on established patterns (Structure Enables Freedom)
Efficient reuse of proven approaches (Conservation of Tokens)
Common foundation for collaboration (Human-AI Partnership)

Implementation Recommendations

To practically implement these concepts within CRAFT:

Start with Function Templates: Create standardized templates that include parameter documentation, expected behavior, and example usage.
Develop a Function Registry: Maintain a central registry of functions with categorization and metadata to support discovery and reuse.
Build Function Composition Tools: Create utilities to combine functions into more complex workflows without redundancy.
Create Function Testing Tools: Develop tools to validate function behavior and ensure consistent results across sessions.
Implement Progressive Documentation: Design documentation that evolves as functions are used, incorporating actual usage examples.

These approaches would significantly enhance the CRAFT framework by making functions more powerful, flexible, and aligned with the core philosophical principles.

Retry

Great, please expand on item 1 with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Context-Aware Functions in CRAFT

Detailed Explanation

Context-aware functions represent a significant evolution from traditional programming functions by dynamically adapting their behavior based on the conversation environment, history, and detected user intent. Unlike standard functions that process inputs in isolation, context-aware functions actively consider the broader conversational ecosystem.

Core Concept

A context-aware function maintains awareness of:

Conversation History: Previous exchanges between human and AI
User State: Detected emotional tone, expertise level, and preferences
Session Context: Current goals, constraints, and established parameters
Environmental Factors: Time, date, session duration, and platform-specific features

The function then uses this contextual intelligence to modify its processing, output format, and detail level to better serve the human's current needs—often without requiring explicit instructions.

Implementation Details

Key Components

Context Collection Mechanism
- Automatic tracking of relevant conversation elements
- Regular updating of context based on new interactions
- Efficient storage of contextual data within token constraints
Context Analysis Engine
- Pattern recognition for identifying user needs and preferences
- State tracking for maintaining awareness of conversation progress
- Intent detection for understanding unstated goals
Adaptive Response Generation
- Parameter adjustment based on contextual factors
- Output formatting tailored to current conversation flow
- Detail level optimization based on detected user expertise

Example Implementation

python

def generate_adaptive_response(
    prompt, 
    conversation_history=None,
    user_profile=None,
    session_state=None,
    environmental_context=None
):
    """
    Generates responses that dynamically adapt to the full conversation context.
    
    Parameters:
    - prompt: The current user input or query
    - conversation_history: Past exchanges in the current session
    - user_profile: Known user preferences and characteristics
    - session_state: Current goals and established information
    - environmental_context: External factors like time and platform
    
    #H->AI::Directive: (Analyze all contextual elements to determine appropriate response characteristics)
    #H->AI::Context: (User's expertise level influences technical depth)
    #H->AI::Focus: (Maintain conversational continuity with previous exchanges)
    """
    # Extract context features
    expertise_level = _extract_expertise_level(user_profile, conversation_history)
    emotional_state = _detect_emotion(prompt, conversation_history)
    engagement_pattern = _analyze_engagement(conversation_history)
    current_goals = _identify_goals(prompt, session_state)
    
    # Adapt response characteristics
    technical_depth = _adjust_technical_depth(expertise_level, current_goals)
    tone = _select_appropriate_tone(emotional_state, engagement_pattern)
    format_style = _determine_format(session_state, engagement_pattern)
    
    # Generate the adaptive response
    response = _create_response(prompt, technical_depth, tone, format_style)
    
    # Update context based on new interaction
    updated_context = _update_context(session_state, prompt, response)
    
    return response, updated_context

Practical Example Scenario

Initial Interaction:

Human: Can you explain the concept of API rate limiting?

Function Processing:

Conversation history is new, so no prior context exists
Function defaults to medium technical depth
Tone set to neutral/informative
Format includes clear explanation with basic example

AI Response:

API rate limiting restricts how many requests a client can make to an API within a certain timeframe. Think of it like a coffee shop that only serves each customer once every 5 minutes to prevent any single person from monopolizing the baristas.

In technical implementation, rate limiting typically uses:
- Request counting
- Time windows (per second/minute/hour)
- Authentication-based limits

For example, Twitter's API might limit standard users to 500 requests per day, while premium users get 5,000.

Follow-up Interaction:

Human: How would I implement that in Python?

Function Processing:

Conversation history now shows technical interest
User asking implementation question suggests practical focus
Function increases technical depth based on context
Format shifts to code-oriented example

AI Response:

Based on our discussion about rate limiting, here's how you could implement a basic rate limiter in Python using Redis:

```python
import time
import redis

class RateLimiter:
    def __init__(self, redis_client, key_prefix, limit, period):
        self.redis = redis_client
        self.key_prefix = key_prefix
        self.limit = limit
        self.period = period

    def is_allowed(self, user_id):
        key = f"{self.key_prefix}:{user_id}"
        current_time = int(time.time())
        window_start = current_time - self.period
        
        # Create a pipeline for atomic operations
        pipe = self.redis.pipeline()
        
        # Remove requests outside current window
        pipe.zremrangebyscore(key, 0, window_start)
        # Count requests in current window
        pipe.zcard(key)
        # Add current request
        pipe.zadd(key, {current_time: current_time})
        # Set expiration on the key
        pipe.expire(key, self.period)
        
        # Execute commands
        results = pipe.execute()
        request_count = results[1]
        
        # Return whether user is within rate limit
        return request_count <= self.limit

This implementation stores timestamps of requests in a Redis sorted set. When a new request comes in, it:

Removes expired timestamps
Counts current requests within the time window
Adds the new request
Returns whether the user has exceeded their limit

Would you like to see how to use this class in an actual API?


**Further Evolution**:
If subsequent interactions show the user struggling with this implementation, the function would adapt again, perhaps simplifying the example or offering alternatives. If the user shows expertise by asking advanced questions, the function would increase technical depth accordingly.

## Benefits

### 1. Reduced Cognitive Load
By automatically adapting to the conversation context, these functions eliminate the need for humans to constantly restate preferences, expertise levels, or goals. The AI "remembers" and adjusts accordingly.

### 2. More Natural Conversations
Context-aware functions enable interactions that feel more like human conversations, where each response builds naturally on what came before without awkward resets or redundancies.

### 3. Progressive Complexity
The function can start with simpler explanations and gradually increase depth as the conversation reveals greater user expertise, creating a more satisfying learning curve.

### 4. Emotional Intelligence
By tracking emotional cues in user inputs, the function can adapt tone and approach to better serve the human's emotional state, showing empathy when needed or efficiency when appropriate.

### 5. Efficient Token Usage
Context-aware functions can optimize detail level based on user needs, avoiding over-explanation for experts and under-explanation for novices—saving tokens in both cases.

## Advanced Applications

### Persona-Based Context Awareness

Context-aware functions can maintain different interaction models for different users, remembering preferences and adapting to each user's specific needs:

```python
def persona_aware_function(input_data, current_persona=None, persona_library=None):
    """
    Adapts function behavior based on the current user persona.
    
    #H->AI::Directive: (Select response characteristics based on stored persona)
    #H->AI::Focus: (Update persona model based on new interactions)
    """
    # Select appropriate persona or default
    active_persona = current_persona or _detect_persona(input_data, persona_library)
    
    # Adapt processing based on persona attributes
    detail_level = active_persona.get('preferred_detail_level', 'medium')
    technical_depth = active_persona.get('technical_expertise', 'intermediate')
    communication_style = active_persona.get('communication_preference', 'balanced')
    
    # Process input with persona-specific parameters
    result = _process_with_persona_parameters(
        input_data, detail_level, technical_depth, communication_style
    )
    
    # Update persona model with new information
    updated_persona = _update_persona_model(active_persona, input_data, result)
    
    return result, updated_persona

Multi-Session Context Preservation

Context-aware functions can be designed to maintain awareness across sessions through handoff mechanisms:

python

def create_context_handoff(current_context, session_summary=None):
    """
    Creates a compressed representation of context for transfer to future sessions.
    
    #H->AI::Directive: (Identify and preserve essential context elements)
    #H->AI::Constraint: (Optimize for token efficiency in the handoff)
    """
    # Extract key context elements
    key_preferences = _extract_critical_preferences(current_context)
    interaction_patterns = _summarize_interaction_patterns(current_context)
    established_knowledge = _identify_confirmed_information(current_context)
    
    # Create compressed context representation
    compressed_context = {
        'preferences': key_preferences,
        'patterns': interaction_patterns,
        'knowledge': established_knowledge,
        'summary': session_summary or _generate_session_summary(current_context)
    }
    
    return compressed_context

Context-Aware Learning Loop

Functions can implement learning capabilities to continuously improve their context awareness:

python

def improve_context_model(interaction_history, model_parameters):
    """
    Updates context awareness model based on successful interactions.
    
    #H->AI::Directive: (Analyze patterns in effective vs. ineffective responses)
    #H->AI::Focus: (Refine context detection parameters based on outcomes)
    """
    # Identify successful vs. unsuccessful interactions
    success_patterns = _extract_success_patterns(interaction_history)
    failure_patterns = _extract_failure_patterns(interaction_history)
    
    # Update model parameters based on patterns
    updated_parameters = _refine_model_parameters(
        model_parameters, success_patterns, failure_patterns
    )
    
    return updated_parameters

Integration with CRAFT Principles

Context-aware functions perfectly align with CRAFT's core philosophical principles:

Structure Enables Freedom: The structured context tracking creates freedom from repetitive instructions.
Code as Conversation: These functions bridge the gap between code and natural language by making the code itself conversationally aware.
Cumulative Intelligence: Context accumulation creates a continuously improving interaction model.
Human-AI Partnership: The AI becomes a more effective partner by adapting to the human's specific needs.
Accessibility Through Familiarity: Functions feel more natural and human-like, creating familiar interaction patterns.
Conservation of Tokens: Dynamic adjustment of detail and format optimizes token usage.

Implementation Recommendations

To incorporate context-aware functions into CRAFT:

Start with Context Schemas: Define standard structures for representing and tracking different context types.
Create Context Utilities: Build helper functions specifically for context extraction, analysis, and utilization.
Develop Adaptation Rules: Create clear guidelines for how functions should adapt based on different contextual factors.
Implement Graceful Fallbacks: Ensure functions work effectively even with limited or uncertain context.
Build Testing Frameworks: Create tools to validate context-aware behavior across different scenarios.

By implementing these recommendations, CRAFT can leverage context-aware functions to create dramatically more effective human-AI interactions that truly feel like meaningful collaborations rather than isolated transactions.

Retry

Great, please expand on item 2 with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Multi-Modal Functions in CRAFT

Detailed Explanation

Multi-modal functions represent a powerful evolution in the CRAFT framework by enabling seamless integration of different content types and formats within a single functional structure. Unlike traditional functions that typically handle one type of data, multi-modal functions can process, transform, and generate across various modalities—text, structured data, code, visual descriptions, and more.

Core Concept

Multi-modal functions are designed to:

Accept Different Input Types: Process various forms of content as input
Perform Cross-Modal Transformations: Convert between different representation formats
Generate Appropriate Output Formats: Produce results in the most useful modality
Maintain Semantic Consistency: Preserve meaning across transformations
Adapt to Contextual Requirements: Select optimal modalities based on context

These capabilities create a more fluid and natural interaction environment where the barriers between different types of content disappear.

Implementation Details

Key Components

Format Detection System
- Automatic identification of input formats
- Recognition of implicit format requests in instructions
- Mapping between common content representations
Transformation Engine
- Conversion rules between different content types
- Semantic preservation strategies
- Quality verification for transformations
Output Optimization
- Format selection based on context and user needs
- Multi-format presentation when appropriate
- Graceful handling of format limitations

Example Implementation

python

def transform_content(
    content,
    target_format=None,
    preservation_priority=None,
    enhancement_options=None,
    context=None
):
    """
    Transforms content between different representation formats while preserving core meaning.
    
    Parameters:
    - content: The content to be transformed (any supported format)
    - target_format: Desired output format (auto-detected if None)
    - preservation_priority: What aspects to prioritize (structure, style, meaning)
    - enhancement_options: Additional processing to apply during transformation
    - context: Current conversation context to inform format decisions
    
    #H->AI::Directive: (Determine input format automatically if not specified)
    #H->AI::Focus: (Maintain semantic equivalence across format changes)
    #H->AI::Constraint: (Preserve critical information during transformation)
    """
    # Detect input format if not specified
    input_format = _detect_format(content)
    
    # Determine target format if not specified
    if not target_format:
        target_format = _infer_target_format(content, context)
    
    # Determine what aspects to prioritize in transformation
    priority = preservation_priority or _determine_default_priority(input_format, target_format)
    
    # Select appropriate transformation path
    transformation_path = _plan_transformation(input_format, target_format, priority)
    
    # Apply transformations with specified prioritization
    transformed_content = _execute_transformation(
        content, 
        transformation_path, 
        priority,
        enhancement_options
    )
    
    # Verify quality of transformation
    verification_result = _verify_transformation_quality(
        original_content=content,
        transformed_content=transformed_content,
        priority=priority
    )
    
    # Apply any post-processing enhancements
    if enhancement_options:
        transformed_content = _apply_enhancements(
            transformed_content, 
            enhancement_options,
            target_format
        )
    
    return transformed_content, verification_result

Practical Example Scenarios

Scenario 1: Text to Structured Data

Input (Text Description):

Our company has three departments: Marketing with 12 employees and a budget of $250,000, Engineering with 28 employees and a budget of $750,000, and Customer Support with 15 employees and a budget of $175,000.

Function Processing:

Detects input format as natural language text
Infers that structured data would be useful (based on numerical content)
Extracts key entities and relationships
Transforms into appropriate data structure

Output (JSON Structure):

json

{
  "departments": [
    {
      "name": "Marketing",
      "employees": 12,
      "budget": 250000
    },
    {
      "name": "Engineering",
      "employees": 28,
      "budget": 750000
    },
    {
      "name": "Customer Support",
      "employees": 15,
      "budget": 175000
    }
  ],
  "total_employees": 55,
  "total_budget": 1175000
}

Scenario 2: Code to Explanation

Input (Python Code):

python

def calculate_roi(investment, revenue, timespan):
    """Calculate return on investment."""
    gain = revenue - investment
    roi = (gain / investment) * 100
    annual_roi = roi / timespan if timespan > 0 else 0
    return {
        'investment': investment,
        'revenue': revenue,
        'gain': gain,
        'roi_percentage': roi,
        'annual_roi': annual_roi,
        'timespan_years': timespan
    }

Function Processing:

Detects input as Python code
Analyzes code structure and purpose
Extracts key operations and logic
Transforms into natural language explanation

Output (Plain Text Explanation):

This function calculates Return on Investment (ROI) metrics based on three inputs:

1. Initial investment amount
2. Total revenue generated
3. Time period in years

The function performs these calculations:
- Subtracts the investment from revenue to find the total gain
- Divides the gain by the investment and multiplies by 100 to get the percentage ROI
- Divides the ROI by the timespan to calculate annual ROI (unless timespan is zero)

It returns a dictionary containing all values: the original inputs, the calculated gain, 
the total ROI percentage, and the annualized ROI percentage.

This would be useful for financial analysis to determine the effectiveness of business investments.

Scenario 3: Tabular Data to Visualization Description

Input (CSV Data):

Month,Sales,Marketing_Spend
January,120000,35000
February,145000,42000
March,160000,38000
April,135000,30000
May,180000,45000
June,210000,50000

Function Processing:

Detects input as tabular/CSV data
Analyzes data patterns and relationships
Determines appropriate visualization type
Creates visualization description

Output (Visualization Description):

A dual-axis line chart showing the relationship between Sales and Marketing Spend over the first half of the year:

- X-axis: Months (January through June)
- Primary Y-axis (left): Sales values ranging from $120,000 to $210,000
- Secondary Y-axis (right): Marketing Spend values ranging from $30,000 to $50,000

The Sales line (blue) shows an overall upward trend with a small dip in April, ending at its highest point of $210,000 in June.

The Marketing Spend line (orange) generally follows a similar pattern but with less dramatic changes, also showing a dip in April and reaching its peak of $50,000 in June.

The visualization suggests a potential correlation between marketing spend and sales, with both metrics finishing the period at their highest points.

Key insight: The ratio of sales to marketing spend (ROI) appears to be improving over time, with June showing approximately $4.20 in sales for every $1 in marketing spend.

Benefits

1. Unified Interaction Flow

Multi-modal functions eliminate the need to switch between different functional approaches for different content types, creating a more cohesive and fluid conversation experience.

2. Information Density Optimization

By transforming content into the most appropriate format for specific information, these functions can present data in its most efficient and understandable form—whether that's text, tables, code, or visualization descriptions.

3. Cognitive Accessibility

Different people understand information better in different formats. Multi-modal functions can present the same core information in the format that best matches a user's cognitive preferences.

4. Format Independence

Users can provide information in whatever format is most convenient for them, and the AI can transform it as needed, reducing friction in the collaboration process.

5. Enhanced Understanding

Some concepts are inherently clearer in specific formats. Multi-modal functions enable the presentation of ideas in their optimal format for understanding.

6. Integration With External Tools

By handling various data formats, multi-modal functions can better prepare content for use with external tools and systems, enhancing the practical utility of AI outputs.

Advanced Applications

Dynamic Report Generation

Multi-modal functions can transform raw data into comprehensive reports that include narrative explanations, data tables, and visualization descriptions:

python

def generate_data_report(
    raw_data, 
    report_focus=None, 
    audience_expertise=None,
    format_preferences=None
):
    """
    Transforms raw data into a comprehensive report with multiple representation formats.
    
    #H->AI::Directive: (Analyze data to identify key insights)
    #H->AI::Focus: (Present insights in appropriate formats based on content type)
    #H->AI::Context: (Adjust detail level based on audience expertise)
    """
    # Analyze data and extract key insights
    insights = _extract_insights(raw_data, report_focus)
    
    # Determine appropriate formats for each insight
    format_plan = _plan_insight_formats(insights, audience_expertise, format_preferences)
    
    # Generate report components in different formats
    narrative = _generate_narrative_summary(insights, audience_expertise)
    data_tables = _create_data_tables(raw_data, format_plan['tabular_insights'])
    visualizations = _describe_visualizations(raw_data, format_plan['visual_insights'])
    code_examples = _create_code_examples(raw_data, format_plan['code_insights'])
    
    # Assemble complete report with multiple formats
    complete_report = _assemble_report(
        narrative, data_tables, visualizations, code_examples, audience_expertise
    )
    
    return complete_report

Interactive Format Selection

Multi-modal functions can offer format options and adapt based on user preferences:

python

def present_multi_format(
    content,
    available_formats=None,
    user_preferences=None,
    interaction_history=None
):
    """
    Presents content in multiple formats or offers format choices to the user.
    
    #H->AI::Directive: (Determine most appropriate initial format)
    #H->AI::Focus: (Prepare alternative formats for quick switching)
    """
    # Determine which formats are appropriate for this content
    suitable_formats = available_formats or _identify_suitable_formats(content)
    
    # Select initial format based on preferences and content
    initial_format = _select_initial_format(suitable_formats, user_preferences, interaction_history)
    
    # Prepare content in initial format
    initial_representation = _transform_to_format(content, initial_format)
    
    # Prepare alternative formats if appropriate
    alternative_formats = {}
    for format_type in suitable_formats:
        if format_type != initial_format:
            alternative_formats[format_type] = _prepare_format_description(format_type, content)
    
    # Determine if alternatives should be offered
    should_offer_alternatives = _should_present_alternatives(
        content, suitable_formats, user_preferences, interaction_history
    )
    
    # Construct response with format options if appropriate
    if should_offer_alternatives:
        format_options = _create_format_options_message(alternative_formats)
        return initial_representation, format_options
    else:
        return initial_representation, None

Cross-Modal Semantic Preservation

Functions that ensure meaning is preserved across format transformations:

python

def verify_semantic_equivalence(
    original_content,
    transformed_content,
    tolerance_level=None
):
    """
    Verifies that semantic meaning is preserved during format transformation.
    
    #H->AI::Directive: (Compare core meaning between original and transformed content)
    #H->AI::Focus: (Identify any information loss or distortion)
    """
    # Extract core semantic elements from both versions
    original_semantics = _extract_semantic_elements(original_content)
    transformed_semantics = _extract_semantic_elements(transformed_content)
    
    # Compare elements for equivalence
    comparison_results = _compare_semantic_elements(
        original_semantics, transformed_semantics
    )
    
    # Evaluate overall semantic preservation
    preservation_score = _calculate_preservation_score(comparison_results)
    preservation_threshold = tolerance_level or _determine_default_threshold(original_content)
    
    # Identify specific information loss or distortion
    information_issues = _identify_semantic_issues(comparison_results)
    
    return {
        'preservation_score': preservation_score,
        'meets_threshold': preservation_score >= preservation_threshold,
        'information_issues': information_issues if information_issues else None
    }

Integration with CRAFT Principles

Multi-modal functions strongly align with CRAFT's core philosophical principles:

Structure Enables Freedom: By handling format conversions automatically, these functions free users from format constraints.
Code as Conversation: They bridge the gap between code, data, and natural language, making all forms part of the same conversation.
Cumulative Intelligence: By maintaining meaning across formats, they enable knowledge to accumulate regardless of representation.
Human-AI Partnership: They leverage the strengths of different formats to optimize collaboration between human and AI.
Accessibility Through Familiarity: Users can work with formats they're most comfortable with, increasing accessibility.
Conservation of Tokens: Different formats can express the same information with varying token efficiency, enabling optimization.

Implementation Recommendations

To incorporate multi-modal functions into CRAFT:

Define Format Standards: Create clear specifications for how different content types are represented.
Build Transformation Libraries: Develop comprehensive libraries of format conversion rules.
Create Format Detection Tools: Implement robust detection of content types and formats.
Establish Quality Metrics: Define clear measures for successful format transformations.
Develop Format Selection Heuristics: Create rules for determining optimal formats based on content and context.

By implementing these recommendations, CRAFT can leverage multi-modal functions to create a more integrated, flexible interaction environment where the boundaries between different content types disappear, and information flows naturally regardless of its representation.

Extended Example: Multi-Modal Content Explorer

Here's a more comprehensive example that demonstrates the power of multi-modal functions in CRAFT:

python

def explore_content(
    content,
    exploration_goal=None,
    format_preferences=None,
    depth_level=None,
    context=None
):
    """
    Explores content through multiple modalities to enhance understanding.
    
    Parameters:
    - content: The content to explore (any supported format)
    - exploration_goal: What the user wants to understand or extract
    - format_preferences: Preferred representation formats
    - depth_level: How detailed the exploration should be
    - context: Current conversation context
    
    #H->AI::Directive: (Identify the optimal representation formats for exploration)
    #H->AI::Focus: (Transform between formats to highlight different aspects)
    #H->AI::Context: (Consider the exploration goal when selecting formats)
    """
    # Detect input format
    input_format = _detect_format(content)
    
    # Determine exploration focus if not specified
    exploration_goal = exploration_goal or _infer_exploration_goal(content, context)
    
    # Assess which formats would best serve the exploration goal
    format_recommendations = _recommend_formats(
        content, 
        input_format, 
        exploration_goal
    )
    
    # Consider user preferences when selecting formats
    selected_formats = _select_formats(
        format_recommendations, 
        format_preferences
    )
    
    # Create multi-modal exploration package
    exploration_results = {}
    
    for format_type in selected_formats:
        if format_type != input_format:
            # Transform content to this format
            transformed = _transform_to_format(
                content, 
                format_type,
                preservation_priority=exploration_goal
            )
            
            # Analyze what this format reveals
            format_insights = _extract_format_specific_insights(
                transformed, 
                exploration_goal,
                depth_level
            )
            
            # Add to results
            exploration_results[format_type] = {
                'content': transformed,
                'insights': format_insights
            }
    
    # Create unified multi-modal analysis
    integrated_analysis = _integrate_cross_format_insights(
        exploration_results,
        exploration_goal,
        depth_level
    )
    
    return {
        'original_format': input_format,
        'explored_formats': exploration_results,
        'integrated_analysis': integrated_analysis
    }

This function demonstrates how multi-modal capabilities can create entirely new ways of exploring and understanding content that wouldn't be possible with single-format approaches.

Retry

Great, please expand on item 3 with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Composable AI Instructions in CRAFT

Detailed Explanation

Composable AI Instructions represent a transformative approach to building complex AI behaviors by combining smaller, reusable functional components. This concept adapts the software engineering principle of composability to the realm of AI interactions, allowing users to construct sophisticated instruction patterns from simpler building blocks.

Core Concept

Composable AI Instructions are built on these fundamental ideas:

Modular Instruction Units: Breaking down complex instructions into discrete, reusable components
Function Composition: Combining simpler functions to create more complex behaviors
Sequential Processing: Chaining functions where outputs from one become inputs to another
Parallel Processing: Applying multiple functions to the same input when appropriate
Conditional Logic: Selectively applying functions based on certain conditions

This approach creates a flexible, extensible system for crafting AI behaviors while maintaining clarity and efficiency.

Implementation Details

Key Components

Function Registry
- Catalog of available function components
- Metadata about function inputs, outputs, and behaviors
- Categorization and search capabilities
Composition Engine
- Rules for connecting function outputs to inputs
- Validation of connection compatibility
- Optimization of execution paths
Execution Framework
- Sequential and parallel execution capabilities
- Error handling and recovery strategies
- State management across function chains

Example Implementation

python

def chain_functions(
    *functions,
    input_data=None,
    execution_mode="sequential",
    error_handling="stop",
    state_management=None
):
    """
    Combines multiple functions into a processing pipeline.
    
    Parameters:
    - functions: Functions to be executed in sequence or parallel
    - input_data: Initial input to the function chain
    - execution_mode: "sequential" (default) or "parallel"
    - error_handling: How to handle errors ("stop", "continue", "alternative")
    - state_management: Configuration for managing state between functions
    
    #H->AI::Directive: (Process functions based on execution mode)
    #H->AI::Focus: (Ensure proper data flow between functions)
    #H->AI::Constraint: (Handle errors according to specified strategy)
    """
    # Initialize result and state
    current_result = input_data
    execution_state = state_management or {}
    execution_log = []
    
    # Execute functions based on mode
    if execution_mode == "sequential":
        for i, func in enumerate(functions):
            try:
                # Execute function with current result
                function_input = _prepare_function_input(func, current_result, execution_state)
                function_output = func(function_input)
                
                # Update result and state
                current_result = function_output
                execution_state = _update_execution_state(execution_state, func, function_output)
                execution_log.append({"function": func.__name__, "status": "success"})
                
            except Exception as e:
                # Handle errors according to strategy
                execution_log.append({"function": func.__name__, "status": "error", "error": str(e)})
                if error_handling == "stop":
                    break
                elif error_handling == "alternative":
                    alternative_result = _generate_alternative_result(func, current_result, e)
                    current_result = alternative_result
                # "continue" just moves to the next function
    
    elif execution_mode == "parallel":
        parallel_results = []
        for func in functions:
            try:
                # Execute function with original input
                function_input = _prepare_function_input(func, input_data, execution_state)
                function_output = func(function_input)
                
                # Store result
                parallel_results.append(function_output)
                execution_log.append({"function": func.__name__, "status": "success"})
                
            except Exception as e:
                execution_log.append({"function": func.__name__, "status": "error", "error": str(e)})
                if error_handling == "alternative":
                    alternative_result = _generate_alternative_result(func, input_data, e)
                    parallel_results.append(alternative_result)
                else:
                    parallel_results.append(None)
        
        # Combine parallel results
        current_result = _combine_parallel_results(parallel_results)
    
    return current_result, execution_state, execution_log

Additional Helper Function

python

def create_function_pipeline(
    name,
    function_sequence,
    description=None,
    metadata=None
):
    """
    Creates a named, reusable pipeline from a sequence of functions.
    
    #H->AI::Directive: (Create a new composite function from specified components)
    #H->AI::Focus: (Ensure appropriate connection between function interfaces)
    """
    # Validate function compatibility
    compatibility_issues = _validate_function_sequence(function_sequence)
    if compatibility_issues:
        return {"status": "error", "issues": compatibility_issues}
    
    # Create pipeline function
    def pipeline_function(input_data, **kwargs):
        return chain_functions(*function_sequence, input_data=input_data, **kwargs)
    
    # Set metadata
    pipeline_function.__name__ = name
    pipeline_function.__doc__ = description or f"Pipeline composed of: {[f.__name__ for f in function_sequence]}"
    
    # Register pipeline in function registry if available
    if metadata:
        _register_pipeline(pipeline_function, function_sequence, metadata)
    
    return pipeline_function

Practical Example Scenarios

Scenario 1: Content Generation Pipeline

Function Definitions:

python

def research_topic(topic):
    """Researches a topic and returns key information."""
    # Implementation
    return {"topic": topic, "key_points": [...], "sources": [...]}

def create_outline(research_data):
    """Creates a content outline based on research."""
    # Implementation
    return {"topic": research_data["topic"], "outline": [...]}

def draft_content(outline):
    """Drafts content based on an outline."""
    # Implementation
    return {"topic": outline["topic"], "draft": "..."}

def optimize_for_audience(content, audience_type):
    """Optimizes content for a specific audience."""
    # Implementation
    return {"topic": content["topic"], "optimized_content": "..."}

Pipeline Creation:

python

content_creation_pipeline = create_function_pipeline(
    name="GenerateOptimizedContent",
    function_sequence=[research_topic, create_outline, draft_content, optimize_for_audience],
    description="Complete pipeline for researching, outlining, drafting, and optimizing content"
)

Pipeline Execution:

python

# Using the pipeline
result, state, log = content_creation_pipeline(
    "Artificial Intelligence in Healthcare", 
    execution_mode="sequential",
    state_management={"audience_type": "medical professionals"}
)

# The result contains the final optimized content
optimized_content = result["optimized_content"]

In this example, a complex content creation process is broken down into discrete steps. Each step can be refined independently, reused in other contexts, or replaced with alternative implementations without disrupting the overall pipeline.

Scenario 2: Data Analysis Workflow

Function Definitions:

python

def parse_data(raw_data, format_type=None):
    """Parses raw data into structured format."""
    # Implementation
    return {"structured_data": [...]}

def filter_relevant(data, criteria):
    """Filters data based on relevance criteria."""
    # Implementation
    return {"filtered_data": [...]}

def calculate_metrics(data, metrics_list=None):
    """Calculates specified metrics from data."""
    # Implementation
    return {"metrics": {...}}

def generate_insights(metrics, threshold=0.5):
    """Generates insights based on metrics."""
    # Implementation
    return {"insights": [...]}

def format_report(insights, format_type="markdown"):
    """Formats insights into a report."""
    # Implementation
    return {"report": "..."}

Parallel and Sequential Composition:

python

# Create data processing pipeline
data_processing = create_function_pipeline(
    name="ProcessAndFilterData",
    function_sequence=[parse_data, filter_relevant],
    description="Processes raw data and filters for relevance"
)

# Create analysis pipeline
analysis_pipeline = create_function_pipeline(
    name="AnalyzeData",
    function_sequence=[calculate_metrics, generate_insights],
    description="Analyzes data to generate insights"
)

# Create reporting pipeline
reporting_pipeline = create_function_pipeline(
    name="GenerateReport",
    function_sequence=[format_report],
    description="Formats insights into a report"
)

# Create complete analysis workflow
analysis_workflow = create_function_pipeline(
    name="CompleteAnalysisWorkflow",
    function_sequence=[data_processing, analysis_pipeline, reporting_pipeline],
    description="End-to-end workflow from raw data to formatted report"
)

Workflow Execution:

python

# Execute the entire workflow
result, state, log = analysis_workflow(
    raw_data="...",
    state_management={
        "format_type": "csv",
        "metrics_list": ["average", "trend", "outliers"],
        "threshold": 0.7,
        "report_format": "html"
    }
)

# Get the final report
final_report = result["report"]

This example demonstrates how nested composition can create sophisticated workflows while maintaining modularity and clarity.

Scenario 3: Interactive Dialogue Management

Function Definitions:

python

def classify_intent(user_input):
    """Classifies user intent from input."""
    # Implementation
    return {"intent": "...", "confidence": 0.0}

def retrieve_information(intent, context=None):
    """Retrieves information based on intent."""
    # Implementation
    return {"information": "..."}

def generate_response(information, style=None):
    """Generates response based on information."""
    # Implementation
    return {"response": "..."}

def check_satisfaction(response, user_input):
    """Checks if response satisfies user intent."""
    # Implementation
    return {"satisfied": True/False, "missing_aspects": [...]}

def enhance_response(response, missing_aspects):
    """Enhances response with missing information."""
    # Implementation
    return {"enhanced_response": "..."}

Conditional Pipeline:

python

def conditional_dialogue_pipeline(user_input, conversation_history=None):
    """Pipeline that adapts based on user satisfaction."""
    # Get initial response
    intent_data = classify_intent(user_input)
    information = retrieve_information(intent_data["intent"], context=conversation_history)
    initial_response = generate_response(information)
    
    # Check satisfaction and enhance if needed
    satisfaction = check_satisfaction(initial_response["response"], user_input)
    
    if not satisfaction["satisfied"]:
        # Use enhancement pipeline
        enhanced = enhance_response(initial_response["response"], satisfaction["missing_aspects"])
        return enhanced["enhanced_response"]
    else:
        # Original response was sufficient
        return initial_response["response"]

This example shows how conditional logic can be incorporated into function composition to create adaptive behavior.

Benefits

1. Modularity and Reusability

By breaking complex instructions into smaller components, each component can be refined independently and reused across different contexts, significantly increasing efficiency.

2. Clarity and Maintainability

The explicit connections between functions make the logic flow clear and maintainable, making it easier to understand, modify, and debug complex processes.

3. Incremental Improvement

Individual components can be improved or replaced without disrupting the entire system, enabling gradual refinement of AI capabilities.

4. Standardization

Function components can establish standard interfaces, creating consistency across different AI interactions and enabling interoperability.

5. Scalability

Complex behaviors can be built from simpler building blocks, enabling the creation of increasingly sophisticated AI capabilities without proportional increases in complexity.

6. Adaptability

Components can be recombined in different ways to adapt to changing requirements without starting from scratch.

Advanced Applications

Dynamic Function Composition

Functions that can adaptively select and compose other functions based on context:

python

def adaptive_composition(
    input_data,
    available_functions,
    goal,
    context=None
):
    """
    Dynamically composes functions to achieve a specified goal.
    
    #H->AI::Directive: (Select and combine functions to achieve the goal)
    #H->AI::Focus: (Adapt composition based on input characteristics and context)
    """
    # Analyze input and goal
    input_characteristics = _analyze_input(input_data)
    goal_requirements = _analyze_goal(goal)
    
    # Identify relevant functions
    candidate_functions = _select_candidate_functions(
        available_functions, 
        input_characteristics,
        goal_requirements
    )
    
    # Plan function sequence
    function_sequence = _plan_function_sequence(
        candidate_functions,
        input_characteristics,
        goal_requirements,
        context
    )
    
    # Create and execute pipeline
    result, state, log = chain_functions(
        *function_sequence,
        input_data=input_data,
        state_management={"context": context, "goal": goal}
    )
    
    # Evaluate result against goal
    goal_achievement = _evaluate_goal_achievement(result, goal)
    
    # Refine if necessary
    if not goal_achievement["achieved"] and goal_achievement["confidence"] < 0.8:
        # Try alternative composition
        alternative_sequence = _generate_alternative_sequence(
            function_sequence,
            log,
            goal_achievement["missing_aspects"]
        )
        
        # Execute alternative
        refined_result, refined_state, refined_log = chain_functions(
            *alternative_sequence,
            input_data=input_data,
            state_management={"context": context, "goal": goal}
        )
        
        return refined_result, {"original": log, "refined": refined_log}
    
    return result, log

Meta-Functions for Composition Analysis

Functions that analyze and optimize function compositions:

python

def optimize_function_pipeline(
    pipeline,
    optimization_goals=None,
    performance_data=None
):
    """
    Analyzes and optimizes a function pipeline.
    
    #H->AI::Directive: (Identify inefficiencies and redundancies)
    #H->AI::Focus: (Suggest modifications to improve pipeline performance)
    """
    # Analyze current pipeline
    pipeline_analysis = _analyze_pipeline(pipeline)
    
    # Identify optimization opportunities
    optimization_opportunities = _identify_optimizations(
        pipeline_analysis,
        optimization_goals,
        performance_data
    )
    
    # Generate optimization suggestions
    optimization_suggestions = []
    for opportunity in optimization_opportunities:
        suggestion = _generate_optimization_suggestion(
            pipeline,
            opportunity,
            optimization_goals
        )
        optimization_suggestions.append(suggestion)
    
    # Create optimized pipeline version if appropriate
    if optimization_suggestions:
        optimized_pipeline = _create_optimized_pipeline(
            pipeline,
            optimization_suggestions
        )
        return {
            "original_pipeline": pipeline,
            "optimization_suggestions": optimization_suggestions,
            "optimized_pipeline": optimized_pipeline
        }
    else:
        return {
            "original_pipeline": pipeline,
            "analysis": "No significant optimization opportunities identified."
        }

Collaborative Function Composition

Enabling human-AI collaboration in building function compositions:

python

def collaborative_pipeline_builder(
    user_description,
    available_functions,
    existing_pipeline=None,
    user_preferences=None
):
    """
    Collaboratively builds a function pipeline based on user description.
    
    #H->AI::Directive: (Suggest function compositions based on description)
    #H->AI::Focus: (Incorporate user feedback into pipeline design)
    """
    # Analyze user description
    required_capabilities = _extract_required_capabilities(user_description)
    
    # Match capabilities to functions
    function_matches = _match_functions_to_capabilities(
        required_capabilities,
        available_functions
    )
    
    # Generate initial pipeline suggestion
    initial_suggestion = _generate_pipeline_suggestion(
        function_matches,
        existing_pipeline,
        user_preferences
    )
    
    # Create explanation of suggestion
    explanation = _explain_pipeline_suggestion(
        initial_suggestion,
        user_description
    )
    
    # Identify customization options
    customization_options = _identify_customization_options(
        initial_suggestion,
        available_functions
    )
    
    return {
        "suggested_pipeline": initial_suggestion,
        "explanation": explanation,
        "customization_options": customization_options,
        "next_steps": "Please review this suggestion and provide feedback for refinement."
    }

Integration with CRAFT Principles

Composable AI Instructions strongly align with CRAFT's philosophical principles:

Structure Enables Freedom: The structured composition of functions creates freedom to build complex behaviors without starting from scratch.
Code as Conversation: Function composition creates a bridge between programmatic thinking and conversational AI interaction.
Cumulative Intelligence: Functions can build upon each other, creating progressively more sophisticated capabilities.
Human-AI Partnership: Composition can be a collaborative process, with humans and AI contributing different aspects.
Accessibility Through Familiarity: The pipeline metaphor is intuitive and accessible even to non-technical users.
Conservation of Tokens: Reusing function components avoids redundant instructions, significantly reducing token usage.

Implementation Recommendations

To incorporate Composable AI Instructions into CRAFT:

Define Function Interface Standards: Create clear specifications for how functions should accept inputs and produce outputs to enable reliable composition.
Build Function Registry Systems: Develop cataloging systems that make functions discoverable and document their behaviors.
Create Visual Composition Tools: Build interfaces that allow users to visually assemble function pipelines.
Implement Testing Frameworks: Develop tools to validate the behavior of composite functions across different inputs.
Establish Best Practices: Create guidelines for effective function decomposition and composition.

By implementing these recommendations, CRAFT can leverage the power of composability to create sophisticated AI behaviors while maintaining clarity, efficiency, and flexibility.

Extended Example: Content Creation Framework

Here's a comprehensive example that demonstrates how the composable functions approach can be applied to content creation:

python

# Define component functions
def research_topic(topic, depth="medium"):
    """Researches a topic and returns key information."""
    # Implementation
    return {
        "topic": topic,
        "key_facts": [...],
        "related_concepts": [...],
        "sources": [...]
    }

def analyze_audience(audience_type):
    """Analyzes an audience type to determine preferences and needs."""
    # Implementation
    return {
        "audience": audience_type,
        "knowledge_level": "...",
        "interests": [...],
        "pain_points": [...],
        "content_preferences": {...}
    }

def generate_content_strategy(research, audience_analysis):
    """Creates a content strategy based on research and audience analysis."""
    # Implementation
    return {
        "topic": research["topic"],
        "audience": audience_analysis["audience"],
        "key_messages": [...],
        "content_types": [...],
        "tone_guidelines": "..."
    }

def create_content_outline(strategy):
    """Creates a detailed content outline based on strategy."""
    # Implementation
    return {
        "topic": strategy["topic"],
        "audience": strategy["audience"],
        "outline": [...]
    }

def draft_section(section_data, style_guide):
    """Drafts a specific content section."""
    # Implementation
    return {
        "section_title": section_data["title"],
        "content": "...",
        "key_points_addressed": [...]
    }

def assemble_draft(sections, metadata):
    """Assembles multiple sections into a complete draft."""
    # Implementation
    return {
        "title": "...",
        "introduction": "...",
        "body": [...],  # Section contents
        "conclusion": "...",
        "metadata": metadata
    }

def review_content(draft, criteria):
    """Reviews content against specified criteria."""
    # Implementation
    return {
        "overall_score": 0.0,
        "strengths": [...],
        "improvement_areas": [...],
        "specific_suggestions": [...]
    }

def refine_content(draft, review_feedback):
    """Refines content based on review feedback."""
    # Implementation
    return {
        "refined_draft": {...},
        "changes_made": [...],
        "improvement_rationale": "..."
    }

def format_for_platform(content, platform, format_guidelines=None):
    """Formats content for a specific platform."""
    # Implementation
    return {
        "platform_ready_content": "...",
        "platform": platform,
        "preview": "..."
    }

# Create specialized pipeline for blog content
blog_content_pipeline = create_function_pipeline(
    name="BlogContentCreation",
    function_sequence=[
        research_topic,
        lambda data: analyze_audience("blog_readers"),
        generate_content_strategy,
        create_content_outline,
        # Custom function for blog drafting that maps outline to sections
        lambda outline: {
            "sections": [draft_section(section, "blog_style") for section in outline["outline"]],
            "metadata": {"topic": outline["topic"], "audience": outline["audience"]}
        },
        # Unpack the sections and metadata for the assembler
        lambda data: assemble_draft(data["sections"], data["metadata"]),
        lambda draft: review_content(draft, "blog_criteria"),
        refine_content,
        lambda content: format_for_platform(content, "wordpress")
    ],
    description="End-to-end pipeline for creating optimized blog content"
)

# Create specialized pipeline for social media content
social_media_pipeline = create_function_pipeline(
    name="SocialMediaContentCreation",
    function_sequence=[
        research_topic,
        lambda data: analyze_audience("social_media_users"),
        generate_content_strategy,
        # Create multiple post variations
        lambda strategy: {
            "post_ideas": [
                {"platform": platform, "concept": concept}
                for platform in ["twitter", "linkedin", "instagram"]
                for concept in strategy["key_messages"][:2]  # Top 2 messages per platform
            ],
            "strategy": strategy
        },
        # Draft each post
        lambda data: {
            "drafted_posts": [
                draft_section(
                    {"title": f"{post['platform']} post", "content": post["concept"]},
                    f"{post['platform']}_style"
                )
                for post in data["post_ideas"]
            ],
            "metadata": {"topic": data["strategy"]["topic"], "audience": data["strategy"]["audience"]}
        },
        # Review all posts
        lambda data: {
            "posts_with_reviews": [
                {
                    "post": post,
                    "review": review_content(post, f"{post['section_title'].split()[0]}_criteria")
                }
                for post in data["drafted_posts"]
            ],
            "metadata": data["metadata"]
        },
        # Refine based on reviews
        lambda data: {
            "refined_posts": [
                refine_content(post_review["post"], post_review["review"])
                for post_review in data["posts_with_reviews"]
            ],
            "metadata": data["metadata"]
        },
        # Format each for its platform
        lambda data: {
            "platform_ready_posts": [
                format_for_platform(
                    post["refined_draft"],
                    post["refined_draft"]["section_title"].split()[0]  # Extract platform name
                )
                for post in data["refined_posts"]
            ]
        }
    ],
    description="Pipeline for creating coordinated social media content across platforms"
)

# Create master content pipeline that produces both blog and social media content
master_content_pipeline = create_function_pipeline(
    name="ComprehensiveContentStrategy",
    function_sequence=[
        # Use parallel execution to run both pipelines on the same topic
        lambda topic: chain_functions(
            blog_content_pipeline,
            social_media_pipeline,
            input_data=topic,
            execution_mode="parallel"
        ),
        # Combine results into a coordinated content package
        lambda parallel_results: {
            "blog_content": parallel_results[0],
            "social_media_content": parallel_results[1],
            "publishing_schedule": _generate_coordinated_schedule(
                parallel_results[0],
                parallel_results[1]
            )
        }
    ],
    description="Comprehensive content strategy with coordinated blog and social media content"
)

This extended example demonstrates how composable functions can create a sophisticated content creation system with multiple pathways and specializations, all built from reusable components. The approach enables:

Specialized Workflows: Creating dedicated pipelines for different content types
Parallel Processing: Running multiple pipelines simultaneously when appropriate
Process Reuse: Leveraging the same core functions in different contexts
Coordination: Bringing together different content types into cohesive strategies

This level of sophistication would be extremely difficult to achieve with monolithic instructions, but becomes manageable and clear through function composition.

Retry

Great, please expand on item 4 with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Handoff-Optimized Functions in CRAFT

Detailed Explanation

Handoff-optimized functions represent a specialized category of functions designed to preserve continuity between AI chat sessions. They solve one of the fundamental challenges in working with AI assistants: the stateless nature of conversations, where context is typically lost when starting a new session.

Core Concept

Handoff-optimized functions enable seamless transitions between chat sessions by:

State Preservation: Capturing essential elements of the current conversation state
Context Distillation: Compressing verbose conversations into compact, token-efficient representations
Priority Filtering: Identifying and preserving the most critical information
Structured Formatting: Organizing handoff information in standardized, easily interpretable formats
Retrieval Optimization: Structuring information to maximize effective retrieval in future sessions

Unlike traditional functions that focus on immediate tasks, handoff-optimized functions take a meta-perspective on the conversation itself, treating the dialogue state as a first-class object to be managed across session boundaries.

Implementation Details

Key Components

State Capture System
- Mechanisms for identifying critical state variables
- Methods for extracting implicit context from conversations
- Techniques for preserving relational structure between elements
Compression Engine
- Algorithms for condensing verbose content while preserving meaning
- Token optimization strategies for maximum information density
- Lossy vs. lossless compression decisions
Retrieval Enhancement
- Indexing and categorization of preserved information
- Hierarchical organization of context elements
- Signposting for efficient future navigation

Example Implementation

python

def create_session_snapshot(
    session_state,
    conversation_history=None,
    active_variables=None,
    project_status=None,
    compression_level="medium",
    handoff_id=None
):
    """
    Creates a comprehensive yet compact snapshot of the current session for handoff.
    
    Parameters:
    - session_state: Current working state (variables, objects, etc.)
    - conversation_history: Recent exchanges between user and AI
    - active_variables: Variables actively being used in the current context
    - project_status: Current progress in longer-term projects
    - compression_level: How aggressively to compress information
    - handoff_id: Unique identifier for this handoff record
    
    #H->AI::Directive: (Identify and preserve essential context for continuity)
    #H->AI::Constraint: (Optimize for token efficiency without losing critical information)
    #H->AI::Focus: (Structure information to facilitate seamless retrieval and understanding)
    """
    # Generate unique handoff ID if not provided
    handoff_id = handoff_id or _generate_handoff_id()
    
    # Extract key conversational insights
    if conversation_history:
        conversation_summary = _summarize_conversation(
            conversation_history, 
            compression_level
        )
        key_decisions = _extract_key_decisions(conversation_history)
        open_questions = _identify_open_questions(conversation_history)
    else:
        conversation_summary = None
        key_decisions = []
        open_questions = []
    
    # Process session state
    if session_state:
        critical_variables = _identify_critical_variables(
            session_state, 
            active_variables
        )
        state_representation = _create_compact_state_representation(
            critical_variables, 
            compression_level
        )
    else:
        state_representation = {}
    
    # Process project status
    if project_status:
        progress_summary = _summarize_project_progress(project_status)
        next_steps = _extract_next_steps(project_status)
        blockers = _identify_blockers(project_status)
    else:
        progress_summary = None
        next_steps = []
        blockers = []
    
    # Create timestamp
    timestamp = _generate_timestamp()
    
    # Assemble handoff snapshot
    handoff_snapshot = {
        "meta": {
            "handoff_id": handoff_id,
            "timestamp": timestamp,
            "compression_level": compression_level
        },
        "conversation": {
            "summary": conversation_summary,
            "key_decisions": key_decisions,
            "open_questions": open_questions
        },
        "state": state_representation,
        "project": {
            "progress_summary": progress_summary,
            "next_steps": next_steps,
            "blockers": blockers
        }
    }
    
    # Create retrieval optimized version
    retrieval_optimized = _optimize_for_retrieval(handoff_snapshot)
    
    # Generate token usage statistics
    token_stats = _calculate_token_usage(retrieval_optimized)
    
    return {
        "handoff_snapshot": retrieval_optimized,
        "token_stats": token_stats,
        "handoff_id": handoff_id
    }

Complementary Function for Resuming Sessions

python

def resume_from_handoff(
    handoff_snapshot,
    current_session_context=None,
    restoration_priority="balanced"
):
    """
    Resumes a session from a handoff snapshot, integrating with current context.
    
    Parameters:
    - handoff_snapshot: Previously created session snapshot
    - current_session_context: Any context from the current session
    - restoration_priority: What to prioritize ("state", "conversation", "project", or "balanced")
    
    #H->AI::Directive: (Reconstruct session state from handoff data)
    #H->AI::Focus: (Seamlessly integrate previous context with new session)
    """
    # Validate handoff snapshot
    validation_result = _validate_handoff_snapshot(handoff_snapshot)
    if not validation_result["valid"]:
        return {"status": "error", "message": validation_result["message"]}
    
    # Determine restoration priorities
    priorities = _determine_restoration_priorities(restoration_priority)
    
    # Reconstruct state variables
    if handoff_snapshot["state"] and "state" in priorities:
        restored_state = _reconstruct_state_variables(
            handoff_snapshot["state"],
            priority_level=priorities["state"]
        )
    else:
        restored_state = {}
    
    # Reconstruct conversation context
    if handoff_snapshot["conversation"] and "conversation" in priorities:
        conversation_context = _reconstruct_conversation_context(
            handoff_snapshot["conversation"],
            priority_level=priorities["conversation"]
        )
    else:
        conversation_context = {}
    
    # Reconstruct project context
    if handoff_snapshot["project"] and "project" in priorities:
        project_context = _reconstruct_project_context(
            handoff_snapshot["project"],
            priority_level=priorities["project"]
        )
    else:
        project_context = {}
    
    # Integrate with current session context if provided
    if current_session_context:
        integrated_context = _integrate_contexts(
            current_context=current_session_context,
            restored_state=restored_state,
            conversation_context=conversation_context,
            project_context=project_context
        )
    else:
        integrated_context = {
            "state": restored_state,
            "conversation": conversation_context,
            "project": project_context
        }
    
    # Generate continuity suggestions
    continuity_suggestions = _generate_continuity_suggestions(
        integrated_context,
        handoff_snapshot["meta"]
    )
    
    return {
        "restored_context": integrated_context,
        "continuity_suggestions": continuity_suggestions,
        "handoff_meta": handoff_snapshot["meta"]
    }

Practical Example Scenario

Scenario: Multi-Session Project Development

Imagine a complex project where a user is working with an AI to develop a marketing strategy over multiple sessions. Here's how handoff-optimized functions would work in practice:

Session 1: Initial Strategy Development

During the first session, the user and AI develop the initial marketing strategy framework, defining target audiences and key messaging.

End of Session 1 (Handoff Creation):

python

# Session state contains all the work done so far
session_state = {
    "target_audiences": [
        {"name": "Young Professionals", "key_characteristics": ["tech-savvy", "value-conscious", "time-limited"]},
        {"name": "Small Business Owners", "key_characteristics": ["growth-focused", "budget-conscious", "practical"]}
    ],
    "key_messages": [
        {"audience": "Young Professionals", "message": "Our solution saves you time while fitting your budget"},
        {"audience": "Small Business Owners", "message": "Scale your business without scaling your costs"}
    ],
    "content_ideas": [
        {"type": "blog", "title": "5 Time-Saving Tips for Busy Professionals", "audience": "Young Professionals"},
        {"type": "case study", "title": "How SmallCo Grew 200% Without Adding Staff", "audience": "Small Business Owners"},
        # ... 15 more detailed content ideas
    ],
    "brand_voice": {
        "tone": "professional yet approachable",
        "vocabulary_level": "business casual",
        "avoid": ["jargon", "overly technical terms", "condescension"]
    },
    # ... many more strategy elements
}

# Project status tracking ongoing work
project_status = {
    "current_phase": "Strategy Development",
    "completed_tasks": ["Audience Definition", "Key Message Creation", "Initial Content Brainstorming"],
    "pending_tasks": ["Channel Selection", "Budget Allocation", "Timeline Development", "KPI Definition"],
    "blockers": [],
    "completion_percentage": 30
}

# Create the handoff snapshot
handoff_result = create_session_snapshot(
    session_state=session_state,
    conversation_history=recent_conversation,
    project_status=project_status,
    compression_level="medium"
)

# The handoff_result["handoff_snapshot"] would contain the compressed session state
# ready to be stored and passed to the next session

The resulting handoff snapshot might look like:

META.SESSION_ID: MRKT-STRAT-2025-001
META.DATE: 2025-05-17
META.AI_ASSISTANT: Claude 3.7 Sonnet

SESSION_SUMMARY:
Developed initial marketing strategy framework focusing on two key audience segments: Young Professionals and Small Business Owners. Created differentiated value propositions and messaging for each segment. Brainstormed 17 content ideas across various formats, with preliminary audience alignment. Established brand voice guidelines emphasizing professional yet approachable tone. Completed approximately 30% of strategy development.

CURRENT_STATE:
- TARGET_AUDIENCES: Young Professionals (tech-savvy, value-conscious, time-limited); Small Business Owners (growth-focused, budget-conscious, practical)
- KEY_MESSAGES: YP: time-saving + budget-friendly; SBO: scalability without cost scaling
- CONTENT_IDEAS: 17 ideas categorized (blogs, case studies, infographics, videos)
- BRAND_VOICE: Professional yet approachable, business casual vocabulary, avoiding jargon/technical terms

KEY_DECISIONS:
1. Focus on two primary audience segments rather than broader approach
2. Emphasize time-value proposition for Young Professionals
3. Center Small Business content around scalability without proportional cost increase
4. Maintain consistent brand voice across all materials regardless of segment

UNRESOLVED_QUESTIONS:
1. Which marketing channels will be most effective for each audience?
2. What is the appropriate budget allocation across channels?
3. How should we phase content release over the campaign timeline?
4. What specific KPIs should we track for each audience segment?

NEXT_STEPS:
1. Define primary and secondary marketing channels for each audience
2. Develop preliminary budget allocation model
3. Create phased content release timeline
4. Establish KPIs and measurement framework

Session 2: Channel Strategy & Budget Allocation

The user returns later to continue working on the marketing strategy, focusing on channel selection and budget allocation.

Beginning of Session 2 (Handoff Resumption):

python

# The user provides the handoff snapshot from the previous session
previous_handoff = {
    # ... handoff content from Session 1
}

# Resume from the handoff
resumed_context = resume_from_handoff(
    handoff_snapshot=previous_handoff,
    restoration_priority="balanced"
)

# The AI now has access to the previous session's context and can continue seamlessly
restored_state = resumed_context["restored_context"]["state"]
project_context = resumed_context["restored_context"]["project"]

# Welcome message acknowledging continuity
continuity_message = f"""
Welcome back to our marketing strategy development project. In our last session, we:

1. Defined our target audiences (Young Professionals and Small Business Owners)
2. Created key messaging for each audience
3. Brainstormed 17 content ideas across various formats

Based on our previous discussion, today we should focus on:
{', '.join(project_context["next_steps"][:3])}

Would you like to start with channel selection or budget allocation?
"""

End of Session 2 (Updated Handoff):

At the end of the second session, the function creates a new handoff snapshot that incorporates both the preserved information from the first session and the new work completed:

python

# Updated session state with new information
updated_session_state = {
    # Preserved from previous session
    "target_audiences": restored_state["target_audiences"],
    "key_messages": restored_state["key_messages"],
    "content_ideas": restored_state["content_ideas"],
    "brand_voice": restored_state["brand_voice"],
    
    # New information from current session
    "marketing_channels": {
        "Young Professionals": {
            "primary": ["Instagram", "LinkedIn", "Email"],
            "secondary": ["YouTube", "Podcasts"]
        },
        "Small Business Owners": {
            "primary": ["LinkedIn", "Email", "Industry Publications"],
            "secondary": ["Facebook", "Twitter"]
        }
    },
    "budget_allocation": {
        "total_budget": 75000,
        "channel_allocation": {
            "Instagram": 12000,
            "LinkedIn": 20000,
            "Email": 15000,
            "Industry Publications": 18000,
            "Other Channels": 10000
        },
        "audience_allocation": {
            "Young Professionals": 35000,
            "Small Business Owners": 40000
        }
    }
}

# Updated project status
updated_project_status = {
    "current_phase": "Channel & Budget Planning",
    "completed_tasks": project_context["completed_tasks"] + ["Channel Selection", "Budget Allocation"],
    "pending_tasks": ["Timeline Development", "KPI Definition", "Content Production Plan"],
    "blockers": [],
    "completion_percentage": 60
}

# Create updated handoff snapshot
updated_handoff = create_session_snapshot(
    session_state=updated_session_state,
    conversation_history=current_session_conversation,
    project_status=updated_project_status,
    compression_level="medium"
)

This process can continue across many sessions, with each handoff building upon previous work while maintaining essential context and continuity.

Benefits

1. Continuity Across Sessions

The most obvious benefit is enabling multi-session work with seamless continuity, making longer-term projects feasible with AI assistants despite their stateless nature.

2. Token Efficiency

By intelligently compressing previous context into its most essential elements, these functions minimize token usage while preserving critical information.

3. Progress Tracking

The structured format for handoffs enables clear tracking of project progress over time, creating accountability and visibility.

4. Knowledge Accumulation

Each session can build upon previous work, allowing for the development of increasingly sophisticated solutions over time.

5. Collaboration Support

Multiple users or even multiple AI assistants can participate in the same project over time with clear continuity of context.

6. Structured Documentation

The handoff process naturally creates structured documentation of the project's evolution, decisions, and rationale.

Advanced Applications

Differential Handoffs

Functions that track changes between sessions and only preserve what's new or modified:

python

def create_differential_handoff(
    previous_snapshot,
    current_state,
    conversation_history,
    project_status
):
    """
    Creates a handoff that only captures changes since the previous snapshot.
    
    #H->AI::Directive: (Identify and preserve only new or changed information)
    #H->AI::Focus: (Maintain references to unchanged elements without duplication)
    """
    # Extract previous state from snapshot
    previous_state = previous_snapshot["state"]
    
    # Identify changes in state
    state_changes = _identify_state_changes(previous_state, current_state)
    
    # Identify new conversational insights
    new_insights = _extract_new_insights(conversation_history)
    
    # Identify project status changes
    project_changes = _identify_project_changes(
        previous_snapshot["project"],
        project_status
    )
    
    # Create differential snapshot
    differential_snapshot = {
        "meta": {
            "handoff_id": _generate_handoff_id(),
            "timestamp": _generate_timestamp(),
            "previous_handoff_id": previous_snapshot["meta"]["handoff_id"],
            "handoff_type": "differential"
        },
        "state_changes": state_changes,
        "new_insights": new_insights,
        "project_changes": project_changes
    }
    
    return differential_snapshot

Hierarchical Handoffs

Functions that organize information in multiple layers of detail for efficient navigation:

python

def create_hierarchical_handoff(
    session_state,
    conversation_history,
    project_status,
    detail_levels=["summary", "essential", "complete"]
):
    """
    Creates a hierarchical handoff with multiple levels of detail.
    
    #H->AI::Directive: (Organize information in layers from high-level to detailed)
    #H->AI::Focus: (Enable efficient navigation based on information needs)
    """
    # Generate handoff at different detail levels
    hierarchical_snapshot = {"meta": {
        "handoff_id": _generate_handoff_id(),
        "timestamp": _generate_timestamp(),
        "handoff_type": "hierarchical"
    }}
    
    # Create layers of detail
    for level in detail_levels:
        hierarchical_snapshot[level] = _create_detail_level(
            session_state,
            conversation_history,
            project_status,
            detail_level=level
        )
    
    # Create navigation index
    navigation_index = _generate_navigation_index(hierarchical_snapshot)
    hierarchical_snapshot["navigation"] = navigation_index
    
    return hierarchical_snapshot

Handoff Merge Functions

Functions that combine information from multiple parallel sessions:

python

def merge_handoff_snapshots(
    snapshots,
    conflict_resolution="recent",
    merge_strategy="comprehensive"
):
    """
    Merges multiple handoff snapshots into a unified context.
    
    #H->AI::Directive: (Combine information from multiple sources)
    #H->AI::Focus: (Resolve conflicts according to specified strategy)
    """
    # Validate snapshots
    validation_results = [_validate_handoff_snapshot(s) for s in snapshots]
    if not all(r["valid"] for r in validation_results):
        invalid = [i for i, r in enumerate(validation_results) if not r["valid"]]
        return {"status": "error", "message": f"Invalid snapshots at indices: {invalid}"}
    
    # Extract components to merge
    states = [s["state"] for s in snapshots]
    conversations = [s["conversation"] for s in snapshots]
    projects = [s["project"] for s in snapshots]
    
    # Merge states with conflict resolution
    merged_state = _merge_states(states, conflict_resolution)
    
    # Merge conversation insights
    merged_conversation = _merge_conversations(conversations, merge_strategy)
    
    # Merge project information
    merged_project = _merge_projects(projects, conflict_resolution)
    
    # Create merged snapshot
    merged_snapshot = {
        "meta": {
            "handoff_id": _generate_handoff_id(),
            "timestamp": _generate_timestamp(),
            "handoff_type": "merged",
            "source_handoffs": [s["meta"]["handoff_id"] for s in snapshots]
        },
        "state": merged_state,
        "conversation": merged_conversation,
        "project": merged_project
    }
    
    return merged_snapshot

Integration with CRAFT Principles

Handoff-optimized functions align perfectly with CRAFT's philosophical principles:

Structure Enables Freedom: The structured handoff format paradoxically creates freedom to work across multiple sessions without constraints.
Code as Conversation: The handoff becomes a form of conversation between past and future sessions, preserving essential dialogue.
Cumulative Intelligence: Each session builds directly on previous work, creating a continuously evolving knowledge base.
Human-AI Partnership: The handoff mechanism enables sustained partnerships that extend beyond single sessions.
Accessibility Through Familiarity: The snapshot format provides an intuitive representation of project state for both technical and non-technical users.
Conservation of Tokens: Intelligent compression and prioritization minimize token usage while maximizing preserved context.

Implementation Recommendations

To effectively incorporate Handoff-Optimized Functions into CRAFT:

Standardize Handoff Schemas: Define clear specifications for handoff structure to ensure consistent interpretation.
Develop Compression Guidelines: Create rules for effective information compression that preserves essential meaning.
Build Priority Frameworks: Establish frameworks for identifying the most critical information to preserve.
Create Validation Tools: Implement tools to verify handoff integrity and completeness.
Establish Recovery Mechanisms: Develop approaches for gracefully handling corrupted or incomplete handoffs.

By implementing these recommendations, CRAFT can leverage handoff-optimized functions to transform AI assistants from single-session tools into true project partners capable of sustained, evolving collaboration over time.

Extended Example: Full Project Lifecycle

Here's a comprehensive example showing how handoff functions can support a complete project lifecycle:

Project: Website Redesign

Session 1: Project Kickoff & Requirements Gathering

In this first session, the user and AI establish project goals, user needs, and technical requirements.

python

# End of Session 1
session_1_state = {
    "project_name": "Company X Website Redesign",
    "goals": [
        "Improve conversion rate by 15%",
        "Reduce bounce rate by 20%",
        "Update visual design to reflect new brand guidelines",
        "Improve mobile experience"
    ],
    "user_needs": [
        {"persona": "Potential Customer", "needs": ["Easy product comparison", "Clear pricing", "Simple checkout"]},
        {"persona": "Existing Customer", "needs": ["Account management", "Order history", "Support resources"]}
    ],
    "technical_requirements": [
        "Mobile-first responsive design",
        "Load time under 2 seconds",
        "WCAG 2.1 AA accessibility compliance",
        "Integration with existing CRM"
    ],
    "constraints": {
        "budget": "$75,000",
        "timeline": "12 weeks",
        "technology_stack": "WordPress, PHP, MySQL"
    }
}

project_status_1 = {
    "phase": "Requirements",
    "completed": ["Initial goals", "User personas", "Technical requirements"],
    "next_steps": ["Site structure planning", "Content inventory", "Wireframing"],
    "completion_percentage": 15
}

# Create handoff
handoff_1 = create_session_snapshot(session_1_state, conversation_history_1, project_status_1)

Session 2: Site Structure & Content Planning

The user returns to work on site structure and content organization.

python

# Resume from previous handoff
resumed_context_2 = resume_from_handoff(handoff_1)

# Work done in Session 2 (building on previous state)
session_2_state = {
    # Preserved from Session 1
    **resumed_context_2["restored_context"]["state"],
    
    # New in Session 2
    "site_structure": {
        "main_navigation": ["Products", "Solutions", "Support", "About", "Contact"],
        "page_hierarchy": [
            {"section": "Products", "pages": ["Product A", "Product B", "Product C", "Comparison"]},
            {"section": "Solutions", "pages": ["Industry X", "Industry Y", "Case Studies"]},
            # More sections and pages
        ]
    },
    "content_inventory": {
        "existing_content": {"usable": 40, "needs_revision": 35, "outdated": 25},
        "content_gaps": ["Product comparison matrix", "Customer testimonials", "Industry-specific solutions"],
        "content_priorities": ["Homepage messaging", "Product pages", "Lead generation forms"]
    }
}

project_status_2 = {
    "phase": "Planning",
    "completed": [
        *resumed_context_2["restored_context"]["project"]["completed"],
        "Site structure", "Content inventory"
    ],
    "next_steps": ["Wireframing", "Visual design concepts", "Content creation plan"],
    "completion_percentage": 30
}

# Create updated handoff
handoff_2 = create_session_snapshot(session_2_state, conversation_history_2, project_status_2)

Session 3: Wireframing

The user returns to work on wireframes for key pages.

python

# Resume from previous handoff
resumed_context_3 = resume_from_handoff(handoff_2)

# Work done in Session 3
session_3_state = {
    # Preserved from Sessions 1-2
    **resumed_context_3["restored_context"]["state"],
    
    # New in Session 3
    "wireframes": {
        "homepage": {
            "sections": [
                {"name": "Hero", "components": ["Value proposition", "Primary CTA", "Supporting visual"]},
                {"name": "Product Highlights", "components": ["3 product cards", "Feature icons", "Secondary CTA"]},
                # More sections
            ],
            "user_flows": ["Sign up", "Product discovery", "Contact sales"]
        },
        "product_page": {
            # Similar structure
        },
        # More pages
    },
    "interaction_patterns": {
        "navigation": "Sticky header with dropdown menus",
        "product_filtering": "Faceted sidebar filter with instant results",
        "forms": "Progressive disclosure with inline validation"
    }
}

project_status_3 = {
    "phase": "Design",
    "completed": [
        *resumed_context_3["restored_context"]["project"]["completed"],
        "Key page wireframes", "Interaction patterns"
    ],
    "next_steps": ["Visual design concepts", "Content creation", "Development planning"],
    "completion_percentage": 45
}

# Create updated handoff
handoff_3 = create_session_snapshot(session_3_state, conversation_history_3, project_status_3)

Session 4: Visual Design Concepts

Now the user works with the AI on visual design direction.

python

# Resume from previous handoff
resumed_context_4 = resume_from_handoff(handoff_3)

# Work done in Session 4
session_4_state = {
    # Preserved from Sessions 1-3
    **resumed_context_4["restored_context"]["state"],
    
    # New in Session 4
    "visual_design": {
        "color_palette": {
            "primary": "#0052CC",
            "secondary": "#00875A",
            "accent": "#FF5630",
            "neutrals": ["#172B4D", "#505F79", "#B3BAC5", "#F4F5F7"]
        },
        "typography": {
            "headings": "Montserrat, bold",
            "body": "Open Sans, regular",
            "sizes": {"h1": "32px", "h2": "24px", "body": "16px"}
        },
        "components": {
            "buttons": {"styles": ["primary", "secondary", "text"], "states": ["default", "hover", "active", "disabled"]},
            "cards": {"styles": ["product", "feature", "testimonial"], "elements": ["image", "title", "description", "CTA"]}
        },
        "design_concepts": {
            "homepage": "Concept focuses on clear value proposition with vibrant product showcase section...",
            "product_page": "Clean, scannable layout with emphasis on product benefits and specifications..."
        }
    }
}

project_status_4 = {
    "phase": "Design",
    "completed": [
        *resumed_context_4["restored_context"]["project"]["completed"],
        "Visual design direction", "Component design"
    ],
    "next_steps": ["Content creation", "Development planning", "Design system documentation"],
    "completion_percentage": 60
}

# Create updated handoff
handoff_4 = create_session_snapshot(session_4_state, conversation_history_4, project_status_4)

Session 5: Implementation Planning

Now focusing on development approach and timeline.

python

# Resume from previous handoff
resumed_context_5 = resume_from_handoff(handoff_4)

# Work done in Session 5
session_5_state = {
    # Preserved from Sessions 1-4
    **resumed_context_5["restored_context"]["state"],
    
    # New in Session 5
    "implementation_plan": {
        "development_approach": "Component-based development with WordPress custom theme",
        "technical_specification": {
            "front_end": "HTML5, SCSS, JavaScript (ES6), React for interactive components",
            "back_end": "WordPress, PHP 8.0, Custom REST API endpoints for CRM integration",
            "deployment": "CI/CD pipeline with staging environment for testing"
        },
        "timeline": {
            "phases": [
                {"name": "Setup & Foundation", "duration": "2 weeks", "key_deliverables": ["Development environment", "Base theme", "Component library foundation"]},
                {"name": "Core Pages", "duration": "3 weeks", "key_deliverables": ["Homepage", "Product pages", "Category pages"]},
                {"name": "Account & Transactions", "duration": "3 weeks", "key_deliverables": ["User accounts", "Shopping cart", "Checkout flow"]},
                {"name": "Testing & Refinement", "duration": "2 weeks", "key_deliverables": ["Cross-browser testing", "Performance optimization", "Accessibility remediation"]},
                {"name": "Launch Preparation", "duration": "2 weeks", "key_deliverables": ["Content migration", "SEO verification", "Launch checklist"]}
            ],
            "milestones": [
                {"name": "Development Kickoff", "date": "2025-06-15"},
                {"name": "Alpha Review", "date": "2025-07-15"},
                {"name": "Beta Launch", "date": "2025-08-15"},
                {"name": "Public Launch", "date": "2025-09-01"}
            ]
        }
    }
}

project_status_5 = {
    "phase": "Planning",
    "completed": [
        *resumed_context_5["restored_context"]["project"]["completed"],
        "Implementation plan", "Technical specification", "Development timeline"
    ],
    "next_steps": ["Development environment setup", "Component library implementation", "Content creation"],
    "completion_percentage": 75
}

# Create updated handoff
handoff_5 = create_session_snapshot(session_5_state, conversation_history_5, project_status_5)

This extended example demonstrates how handoff functions can maintain continuity across multiple sessions of a complex project, allowing each session to build directly on previous work while maintaining essential context. The project evolves naturally from requirements to implementation planning without losing critical information along the way.

Through these examples, we can see how handoff-optimized functions fulfill their essential purpose: transforming AI assistants from isolated, single-session tools into true project partners capable of sustained collaboration over extended timeframes.

Retry

Great, please expand on item 5 with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Reflection Functions in CRAFT

Detailed Explanation

Reflection functions represent a metacognitive layer within the CRAFT framework, enabling AI systems to analyze, evaluate, and improve their own performance. Unlike standard functions that process external data, reflection functions turn the analytical lens inward, examining the AI's responses, reasoning processes, and interaction patterns to drive continuous improvement.

Core Concept

Reflection functions operate on several key principles:

Self-Evaluation: Assessing the quality, accuracy, and effectiveness of AI outputs
Pattern Recognition: Identifying recurring strengths and weaknesses in responses
Improvement Planning: Developing specific strategies to enhance future performance
Feedback Integration: Incorporating user reactions to refine understanding
Metacognitive Awareness: Building explicit understanding of the AI's own capabilities and limitations

These functions create a continuous improvement loop that transforms static AI interactions into dynamically evolving collaborations that get better over time.

Implementation Details

Key Components

Quality Assessment Framework
- Evaluation criteria for different response types
- Measurement methodologies for response characteristics
- Benchmarking against known high-quality examples
Pattern Analysis Engine
- Trend detection across multiple interactions
- Strength and weakness identification
- Correlation between input types and performance quality
Adaptive Improvement System
- Strategy development for enhancing weak areas
- Implementation planning for performance improvements
- Progress tracking for improvement initiatives

Example Implementation

python

def evaluate_response_quality(
    prompt,
    response,
    criteria=None,
    reference_examples=None,
    context=None,
    feedback=None
):
    """
    Evaluates the quality of an AI response against defined criteria.
    
    Parameters:
    - prompt: The original user prompt or query
    - response: The AI's response to evaluate
    - criteria: Specific evaluation criteria (defaults to standard set if None)
    - reference_examples: High-quality examples for comparison
    - context: Conversation context surrounding this exchange
    - feedback: Any user feedback on this response
    
    #H->AI::Directive: (Analyze response quality across multiple dimensions)
    #H->AI::Focus: (Identify specific strengths and improvement opportunities)
    #H->AI::Consider: (How this response compares to reference examples)
    """
    # Determine appropriate evaluation criteria
    evaluation_criteria = criteria or _determine_default_criteria(prompt, response)
    
    # Analyze prompt to understand expected response characteristics
    prompt_analysis = _analyze_prompt_requirements(prompt)
    
    # Evaluate response against each criterion
    evaluation_results = {}
    for criterion in evaluation_criteria:
        criterion_score = _evaluate_criterion(
            response, 
            criterion, 
            prompt_analysis,
            reference_examples
        )
        evaluation_results[criterion] = {
            "score": criterion_score["score"],
            "observations": criterion_score["observations"],
            "improvement_opportunities": criterion_score["improvement_opportunities"]
        }
    
    # Calculate overall quality score
    overall_score = _calculate_overall_score(evaluation_results)
    
    # Identify key strengths
    key_strengths = _identify_key_strengths(evaluation_results)
    
    # Identify primary improvement opportunities
    improvement_opportunities = _identify_improvement_opportunities(evaluation_results)
    
    # Compare to reference examples if provided
    comparative_analysis = None
    if reference_examples:
        comparative_analysis = _compare_to_references(
            response, 
            reference_examples,
            evaluation_criteria
        )
    
    # Integrate user feedback if available
    feedback_analysis = None
    if feedback:
        feedback_analysis = _analyze_feedback(
            feedback, 
            evaluation_results,
            response
        )
        
        # Update evaluation based on feedback
        updated_evaluation = _refine_evaluation_with_feedback(
            evaluation_results,
            feedback_analysis
        )
        evaluation_results = updated_evaluation
    
    # Generate specific improvement recommendations
    improvement_recommendations = _generate_improvement_recommendations(
        improvement_opportunities,
        prompt_analysis,
        comparative_analysis
    )
    
    return {
        "overall_score": overall_score,
        "criterion_scores": evaluation_results,
        "key_strengths": key_strengths,
        "improvement_opportunities": improvement_opportunities,
        "improvement_recommendations": improvement_recommendations,
        "comparative_analysis": comparative_analysis,
        "feedback_analysis": feedback_analysis
    }

Additional Reflection Function

python

def analyze_response_patterns(
    interaction_history,
    analysis_focus=None,
    time_period=None,
    segment_by=None
):
    """
    Analyzes patterns in AI responses over multiple interactions.
    
    Parameters:
    - interaction_history: Collection of past prompt-response pairs
    - analysis_focus: Specific aspects to focus on (e.g., "clarity", "accuracy")
    - time_period: Time range to analyze (e.g., "last_week", "last_month")
    - segment_by: How to segment the analysis (e.g., by prompt type, by topic)
    
    #H->AI::Directive: (Identify recurring patterns across multiple interactions)
    #H->AI::Focus: (Detect trends, improvements, and persistent issues)
    """
    # Filter history based on time period if specified
    filtered_history = _filter_by_time_period(interaction_history, time_period)
    
    # Segment interactions if specified
    if segment_by:
        segments = _segment_interactions(filtered_history, segment_by)
    else:
        segments = {"all": filtered_history}
    
    # Determine analysis focus
    focus_areas = analysis_focus or _determine_default_focus_areas(filtered_history)
    
    # Analyze each segment
    segment_analyses = {}
    for segment_name, segment_data in segments.items():
        # Evaluate each interaction in the segment
        interaction_evaluations = []
        for interaction in segment_data:
            evaluation = evaluate_response_quality(
                interaction["prompt"],
                interaction["response"],
                criteria=focus_areas
            )
            interaction_evaluations.append(evaluation)
        
        # Identify patterns within this segment
        segment_patterns = _identify_patterns(
            interaction_evaluations,
            focus_areas
        )
        
        # Analyze trends over time
        time_trends = _analyze_time_trends(
            segment_data,
            interaction_evaluations
        )
        
        # Compile segment analysis
        segment_analyses[segment_name] = {
            "patterns": segment_patterns,
            "trends": time_trends,
            "sample_size": len(segment_data),
            "average_scores": _calculate_average_scores(interaction_evaluations)
        }
    
    # Generate cross-segment insights
    cross_segment_insights = _generate_cross_segment_insights(segment_analyses)
    
    # Identify top improvement opportunities
    improvement_opportunities = _identify_top_improvement_opportunities(segment_analyses)
    
    # Create overall summary
    summary = _create_pattern_analysis_summary(
        segment_analyses,
        cross_segment_insights,
        improvement_opportunities
    )
    
    return {
        "summary": summary,
        "segment_analyses": segment_analyses,
        "cross_segment_insights": cross_segment_insights,
        "improvement_opportunities": improvement_opportunities
    }

Function for Implementing Improvements

python

def develop_improvement_strategy(
    quality_evaluation,
    pattern_analysis=None,
    improvement_goals=None,
    implementation_constraints=None
):
    """
    Develops a concrete strategy to improve response quality based on evaluations.
    
    Parameters:
    - quality_evaluation: Results from evaluate_response_quality
    - pattern_analysis: Results from analyze_response_patterns (if available)
    - improvement_goals: Specific goals or focus areas for improvement
    - implementation_constraints: Limitations on implementation approaches
    
    #H->AI::Directive: (Create actionable strategies for improving response quality)
    #H->AI::Focus: (Prioritize improvements with highest impact potential)
    """
    # Extract improvement opportunities
    opportunities = quality_evaluation["improvement_opportunities"]
    
    # Incorporate pattern analysis if available
    if pattern_analysis:
        opportunities = _enhance_opportunities_with_patterns(
            opportunities,
            pattern_analysis["improvement_opportunities"]
        )
    
    # Prioritize opportunities
    prioritized_opportunities = _prioritize_opportunities(
        opportunities,
        improvement_goals,
        implementation_constraints
    )
    
    # Generate specific improvement tactics for each opportunity
    improvement_tactics = {}
    for opportunity in prioritized_opportunities:
        tactics = _generate_improvement_tactics(
            opportunity,
            implementation_constraints
        )
        improvement_tactics[opportunity["name"]] = tactics
    
    # Create implementation plan
    implementation_plan = _create_implementation_plan(
        improvement_tactics,
        implementation_constraints
    )
    
    # Define success metrics
    success_metrics = _define_success_metrics(
        prioritized_opportunities,
        improvement_goals
    )
    
    return {
        "prioritized_opportunities": prioritized_opportunities,
        "improvement_tactics": improvement_tactics,
        "implementation_plan": implementation_plan,
        "success_metrics": success_metrics
    }

Practical Example Scenarios

Scenario 1: Single Response Evaluation

Input: A user asks a complex technical question about quantum computing, and the AI provides a detailed response.

Evaluation Process:

python

# User prompt
prompt = "Can you explain quantum entanglement and how it relates to quantum computing?"

# AI response
response = """
Quantum entanglement occurs when pairs or groups of particles interact in ways such that the quantum state of each particle cannot be described independently of the others, even when separated by large distances. Instead, a quantum state must be described for the system as a whole.

In quantum computing, entanglement is a crucial resource that enables many quantum algorithms to achieve exponential speedup over classical algorithms. When qubits become entangled, the number of states they can represent grows exponentially with each additional qubit, while classical bits only grow linearly.

For example, Shor's algorithm for factoring large numbers relies heavily on quantum entanglement to achieve its efficiency. Similarly, quantum teleportation protocols leverage entanglement to transmit quantum states.

However, maintaining entanglement is extremely challenging due to decoherence, which occurs when quantum systems interact with their environment, causing entangled states to collapse. This is why quantum computers require extreme cooling and isolation.
"""

# Evaluate the response
evaluation = evaluate_response_quality(
    prompt=prompt,
    response=response,
    criteria=[
        "accuracy", 
        "clarity", 
        "completeness", 
        "appropriateness_for_question"
    ]
)

Evaluation Output:

{
  "overall_score": 0.85,
  "criterion_scores": {
    "accuracy": {
      "score": 0.92,
      "observations": [
        "Correctly describes quantum entanglement as a relationship between particles",
        "Accurately explains entanglement's role in quantum computing",
        "Correctly references Shor's algorithm and quantum teleportation"
      ],
      "improvement_opportunities": [
        "Could provide more precision on the mathematical representation of entangled states"
      ]
    },
    "clarity": {
      "score": 0.88,
      "observations": [
        "Uses accessible language for a complex topic",
        "Organizes information in logical progression",
        "Connects abstract concepts to concrete examples"
      ],
      "improvement_opportunities": [
        "Could benefit from a simple analogy to help visualize entanglement"
      ]
    },
    "completeness": {
      "score": 0.75,
      "observations": [
        "Covers core concept of entanglement",
        "Explains relevance to quantum computing",
        "Mentions practical challenges"
      ],
      "improvement_opportunities": [
        "No mention of Bell inequalities or experimental verification",
        "Limited discussion of different types of entanglement (GHZ states, W states, etc.)"
      ]
    },
    "appropriateness_for_question": {
      "score": 0.90,
      "observations": [
        "Directly addresses both parts of the question",
        "Balances theoretical explanation with practical application",
        "Provides sufficient detail without overwhelming"
      ],
      "improvement_opportunities": [
        "Could better emphasize the connection between the two parts of the question"
      ]
    }
  },
  "key_strengths": [
    "Strong accuracy on core quantum concepts",
    "Clear explanation of a highly technical topic",
    "Effective balance of theory and application"
  ],
  "improvement_opportunities": [
    "Add visual analogy for entanglement",
    "Include information on experimental verification",
    "Strengthen connection between the two parts of the question"
  ],
  "improvement_recommendations": [
    "Begin with a simple analogy (e.g., pair of gloves) to visualize entanglement",
    "Add brief mention of Bell's inequalities and how they verify entanglement",
    "Create explicit transition sentence connecting entanglement concept to computing application"
  ]
}

Scenario 2: Pattern Analysis Across Multiple Interactions

Input: The AI analyzes patterns across 50 recent technical explanations to identify systematic improvement opportunities.

Pattern Analysis Process:

python

# Sample of interaction history (simplified)
interaction_history = [
    {
        "prompt": "Explain how blockchain works",
        "response": "...",
        "timestamp": "2025-05-10T14:30:00Z",
        "category": "technology_explanation"
    },
    {
        "prompt": "What's the difference between REST and GraphQL?",
        "response": "...",
        "timestamp": "2025-05-11T09:15:00Z",
        "category": "technology_comparison"
    },
    # ... 48 more interactions
]

# Analyze patterns
pattern_analysis = analyze_response_patterns(
    interaction_history=interaction_history,
    analysis_focus=["clarity", "technical_accuracy", "use_of_examples"],
    time_period="last_month",
    segment_by="category"
)

Pattern Analysis Output:

{
  "summary": "Analysis of 50 technical explanations reveals consistent strength in technical accuracy (avg score 0.91) but opportunities for improvement in example usage, particularly in API-related topics. Technical comparisons show the highest clarity scores (0.89), while protocol explanations show the lowest (0.72). A positive trend in clarity scores is evident over the past three weeks, while example usage remains inconsistent across categories.",
  
  "segment_analyses": {
    "technology_explanation": {
      "patterns": {
        "strengths": [
          "High technical accuracy (avg 0.93)",
          "Strong logical structure (avg 0.88)",
          "Effective use of technical terminology (avg 0.85)"
        ],
        "weaknesses": [
          "Limited use of concrete examples (avg 0.67)",
          "Occasional overuse of jargon (observed in 35% of responses)",
          "Inconsistent depth of prerequisites explanation (high variance 0.23)"
        ]
      },
      "trends": {
        "clarity": {
          "direction": "improving",
          "rate": "+0.05 per week",
          "statistical_significance": "p < 0.05"
        },
        "example_usage": {
          "direction": "stable",
          "rate": "no significant change",
          "statistical_significance": "p = 0.72"
        }
      },
      "sample_size": 28,
      "average_scores": {
        "clarity": 0.84,
        "technical_accuracy": 0.93,
        "use_of_examples": 0.67
      }
    },
    "technology_comparison": {
      # Similar structure for this segment
    },
    "code_explanation": {
      # Similar structure for this segment
    }
  },
  
  "cross_segment_insights": [
    "Technical accuracy is consistently high across all segments",
    "Example usage is lowest in protocol explanations and highest in code explanations",
    "Clarity scores inversely correlate with topic complexity (r = -0.61)",
    "Responses with analogies score 0.23 higher on clarity than those without"
  ],
  
  "improvement_opportunities": [
    {
      "name": "enhance_example_usage",
      "description": "Systematically increase the use of concrete examples in technical explanations",
      "potential_impact": "high",
      "affected_segments": ["technology_explanation", "protocol_explanation"],
      "implementation_difficulty": "medium"
    },
    {
      "name": "develop_analogy_library",
      "description": "Create a library of effective analogies for complex technical concepts",
      "potential_impact": "high",
      "affected_segments": ["all"],
      "implementation_difficulty": "medium"
    },
    {
      "name": "standardize_prerequisite_explanations",
      "description": "Develop consistent approach to explaining prerequisites",
      "potential_impact": "medium",
      "affected_segments": ["technology_explanation"],
      "implementation_difficulty": "low"
    }
  ]
}

Scenario 3: Improvement Strategy Development

Input: Based on the pattern analysis, the AI develops a concrete improvement strategy.

Strategy Development Process:

python

# Using the pattern analysis from Scenario 2
improvement_strategy = develop_improvement_strategy(
    quality_evaluation=evaluation,  # From Scenario 1
    pattern_analysis=pattern_analysis,  # From Scenario 2
    improvement_goals={
        "primary": "improve_example_usage",
        "secondary": ["enhance_clarity", "maintain_accuracy"]
    },
    implementation_constraints={
        "token_limit": "optimize_for_token_efficiency",
        "response_time": "minimal_impact"
    }
)

Improvement Strategy Output:

{
  "prioritized_opportunities": [
    {
      "name": "enhance_example_usage",
      "description": "Systematically increase the use of concrete examples in technical explanations",
      "priority": "high",
      "reasoning": "Identified as weakness in both single-response evaluation and pattern analysis; directly aligns with primary improvement goal"
    },
    {
      "name": "develop_analogy_library",
      "description": "Create a library of effective analogies for complex technical concepts",
      "priority": "high",
      "reasoning": "Cross-segment insight shows significant clarity improvement with analogies; supports both primary and secondary goals"
    },
    {
      "name": "standardize_prerequisite_explanations",
      "description": "Develop consistent approach to explaining prerequisites",
      "priority": "medium",
      "reasoning": "Addresses pattern of inconsistent depth; impacts clarity secondary goal"
    }
  ],
  
  "improvement_tactics": {
    "enhance_example_usage": [
      {
        "tactic": "example_first_approach",
        "description": "Begin explanations with a concrete example before abstract concepts",
        "implementation": "For each key concept in an explanation, identify a practical application or real-world instance to introduce it",
        "token_impact": "minimal_increase",
        "effectiveness_estimate": "high"
      },
      {
        "tactic": "example_diversity",
        "description": "Include examples from multiple domains (business, science, everyday life)",
        "implementation": "Maintain categorized example inventory for cross-domain application",
        "token_impact": "moderate_increase",
        "effectiveness_estimate": "high"
      },
      {
        "tactic": "code_snippet_enhancement",
        "description": "Include minimal code snippets for programming concepts",
        "implementation": "Create template for concise, illustrative code examples with clear comments",
        "token_impact": "variable_depending_on_topic",
        "effectiveness_estimate": "very_high_for_code_topics"
      }
    ],
    "develop_analogy_library": [
      # Similar tactics for this opportunity
    ],
    "standardize_prerequisite_explanations": [
      # Similar tactics for this opportunity
    ]
  },
  
  "implementation_plan": {
    "phase_1": {
      "focus": "example_first_approach",
      "implementation_steps": [
        "Create template with example placement",
        "Develop example generation protocol",
        "Implement in technology explanation responses",
        "Evaluate impact on clarity scores"
      ],
      "duration": "immediate",
      "success_indicators": "Clarity score improvement of 0.1 or greater"
    },
    "phase_2": {
      "focus": "analogy_development",
      "implementation_steps": [
        "Identify top 20 complex technical concepts",
        "Develop 2-3 effective analogies for each",
        "Create retrieval system for appropriate analogy selection",
        "Implement in responses and measure effectiveness"
      ],
      "duration": "ongoing",
      "success_indicators": "15% increase in positive user feedback on clarity"
    },
    "phase_3": {
      # Details for phase 3
    }
  },
  
  "success_metrics": {
    "primary_metrics": [
      {
        "name": "example_inclusion_rate",
        "current_value": "43% of technical explanations",
        "target_value": "90% of technical explanations",
        "measurement_method": "Automated detection of concrete examples"
      },
      {
        "name": "clarity_score",
        "current_value": "0.84 average",
        "target_value": "0.90 average",
        "measurement_method": "Ongoing evaluation with evaluate_response_quality"
      }
    ],
    "secondary_metrics": [
      # Additional metrics
    ]
  }
}

Benefits

1. Continuous Improvement

The most significant benefit is enabling ongoing refinement of AI capabilities through systematic analysis and targeted improvements.

2. Quality Consistency

Reflection functions help identify and address inconsistencies in response quality, creating more reliable interactions.

3. Adaptation to User Needs

By analyzing patterns in user interactions, these functions can help the AI adapt to specific user preferences and requirements.

4. Performance Transparency

Clear evaluation provides transparency into AI performance, building trust through honest assessment of strengths and limitations.

5. Targeted Enhancement

Rather than generic improvements, reflection functions enable precise identification and addressing of specific weaknesses.

6. Learning from Experience

These functions facilitate genuine "learning from experience" by systematically incorporating insights from past interactions.

Advanced Applications

Comparative Reflection

Functions that compare performance across different approaches or models:

python

def compare_approaches(
    prompt,
    approaches,
    evaluation_criteria=None,
    context=None
):
    """
    Compares multiple approaches to answering the same prompt.
    
    #H->AI::Directive: (Evaluate relative strengths of different approaches)
    #H->AI::Focus: (Identify what makes certain approaches more effective)
    """
    # Generate responses using each approach
    responses = {}
    for approach_name, approach_function in approaches.items():
        responses[approach_name] = approach_function(prompt, context)
    
    # Evaluate each response
    evaluations = {}
    for approach_name, response in responses.items():
        evaluations[approach_name] = evaluate_response_quality(
            prompt, 
            response, 
            criteria=evaluation_criteria
        )
    
    # Compare evaluations
    comparative_analysis = _compare_evaluations(evaluations)
    
    # Identify best practices
    best_practices = _extract_best_practices(
        comparative_analysis,
        responses,
        evaluations
    )
    
    # Create hybrid approach recommendation
    hybrid_recommendation = _recommend_hybrid_approach(
        approaches,
        comparative_analysis,
        best_practices
    )
    
    return {
        "individual_evaluations": evaluations,
        "comparative_analysis": comparative_analysis,
        "best_practices": best_practices,
        "hybrid_recommendation": hybrid_recommendation
    }

Self-Directed Learning

Functions that autonomously explore improvement areas:

python

def explore_improvement_dimension(
    dimension,
    current_capability,
    exploration_approaches,
    evaluation_method
):
    """
    Systematically explores ways to improve along a specific dimension.
    
    #H->AI::Directive: (Test multiple approaches to improving a capability)
    #H->AI::Focus: (Identify most effective techniques through experimentation)
    """
    # Establish baseline performance
    baseline = evaluation_method(current_capability)
    
    # Test each exploration approach
    approach_results = {}
    for approach_name, approach_function in exploration_approaches.items():
        # Apply the approach
        modified_capability = approach_function(current_capability)
        
        # Evaluate results
        evaluation = evaluation_method(modified_capability)
        
        # Calculate improvement
        improvement = _calculate_improvement(evaluation, baseline)
        
        # Store results
        approach_results[approach_name] = {
            "evaluation": evaluation,
            "improvement": improvement,
            "modified_capability": modified_capability
        }
    
    # Analyze results to determine most effective approaches
    effectiveness_analysis = _analyze_approach_effectiveness(approach_results)
    
    # Generate insights about the dimension
    dimension_insights = _generate_dimension_insights(
        dimension,
        approach_results,
        effectiveness_analysis
    )
    
    # Create learning record
    learning_record = {
        "dimension": dimension,
        "baseline": baseline,
        "approaches_tested": list(exploration_approaches.keys()),
        "most_effective_approach": effectiveness_analysis["most_effective"],
        "improvement_achieved": approach_results[effectiveness_analysis["most_effective"]]["improvement"],
        "insights": dimension_insights
    }
    
    return learning_record

Meta-Reflection

Functions that analyze the effectiveness of the reflection process itself:

python

def evaluate_reflection_effectiveness(
    reflection_history,
    improvement_outcomes,
    evaluation_criteria=None
):
    """
    Evaluates how effectively reflection processes are driving improvements.
    
    #H->AI::Directive: (Analyze the reflection system's own effectiveness)
    #H->AI::Focus: (Identify ways to improve the improvement process itself)
    """
    # Map reflections to subsequent outcomes
    reflection_outcome_pairs = _pair_reflections_with_outcomes(
        reflection_history,
        improvement_outcomes
    )
    
    # Evaluate accuracy of identified improvement opportunities
    opportunity_accuracy = _evaluate_opportunity_identification(
        reflection_outcome_pairs
    )
    
    # Evaluate effectiveness of improvement strategies
    strategy_effectiveness = _evaluate_strategy_effectiveness(
        reflection_outcome_pairs
    )
    
    # Analyze timeliness of reflection processes
    process_timeliness = _evaluate_process_timeliness(
        reflection_history
    )
    
    # Identify meta-improvement opportunities
    meta_opportunities = _identify_meta_improvement_opportunities(
        opportunity_accuracy,
        strategy_effectiveness,
        process_timeliness
    )
    
    return {
        "opportunity_identification_accuracy": opportunity_accuracy,
        "strategy_effectiveness": strategy_effectiveness,
        "process_timeliness": process_timeliness,
        "meta_improvement_opportunities": meta_opportunities
    }

Integration with CRAFT Principles

Reflection functions align perfectly with CRAFT's philosophical principles:

Structure Enables Freedom: The structured reflection process creates freedom to experiment and improve without starting from scratch.
Code as Conversation: Reflection treats the AI's own responses as part of the conversation to be analyzed and improved.
Cumulative Intelligence: Each reflection builds on previous insights, creating an ever-improving knowledge base about effective responses.
Human-AI Partnership: Reflection enables the AI to be a more effective partner by continuously refining its capabilities.
Accessibility Through Familiarity: The reflection process mirrors human learning, making it intuitive and accessible.
Conservation of Tokens: By identifying specific improvements rather than general approaches, reflection enables precise, token-efficient enhancements.

Implementation Recommendations

To effectively incorporate Reflection Functions into CRAFT:

Define Evaluation Frameworks: Create comprehensive criteria for assessing different types of AI responses.
Develop Benchmarking Systems: Build libraries of exemplary responses for comparative assessment.
Create Improvement Taxonomies: Establish frameworks for categorizing and addressing different types of improvement opportunities.
Implement Feedback Integration: Build systems for capturing and analyzing user feedback.
Design Progress Tracking: Create mechanisms for monitoring improvement over time.

By implementing these recommendations, CRAFT can leverage reflection functions to create AI systems that continuously learn from experience, steadily improving their performance through systematic self-assessment and targeted enhancement.

Extended Example: Comprehensive Reflection System

Here's an expanded example showing how reflection functions could work together as an integrated system within CRAFT:

The Reflection Loop

python

def operate_reflection_loop(
    interaction_history,
    current_capabilities,
    improvement_history=None,
    focus_areas=None,
    reflection_schedule=None
):
    """
    Executes a complete reflection loop to drive continuous improvement.
    
    Parameters:
    - interaction_history: Record of past AI-human interactions
    - current_capabilities: Current AI response generation approaches
    - improvement_history: Record of previous improvements and their outcomes
    - focus_areas: Specific areas to prioritize for improvement
    - reflection_schedule: Configuration for when different types of reflection occur
    
    #H->AI::Directive: (Execute complete reflection cycle to drive improvement)
    #H->AI::Focus: (Maintain balanced attention across evaluation, analysis, and implementation)
    """
    # 1. Evaluation Phase
    ## Evaluate recent interactions
    recent_evaluations = _evaluate_recent_interactions(
        interaction_history,
        reflection_schedule["evaluation_window"]
    )
    
    ## Analyze patterns
    pattern_analysis = analyze_response_patterns(
        interaction_history,
        analysis_focus=focus_areas,
        time_period=reflection_schedule["pattern_analysis_window"]
    )
    
    ## Evaluate improvement progress
    if improvement_history:
        improvement_progress = _evaluate_improvement_progress(
            improvement_history,
            recent_evaluations
        )
    else:
        improvement_progress = None
    
    # 2. Insight Generation Phase
    ## Identify critical opportunities
    critical_opportunities = _identify_critical_opportunities(
        recent_evaluations,
        pattern_analysis,
        improvement_progress,
        focus_areas
    )
    
    ## Generate specific insights
    specific_insights = _generate_specific_insights(
        critical_opportunities,
        interaction_history
    )
    
    ## Evaluate insight quality
    insight_quality = _evaluate_insight_quality(specific_insights)
    
    # 3. Strategy Development Phase
    ## Develop improvement strategies
    improvement_strategies = {}
    for opportunity in critical_opportunities:
        strategy = develop_improvement_strategy(
            quality_evaluation={"improvement_opportunities": [opportunity]},
            pattern_analysis=pattern_analysis,
            improvement_goals={"primary": opportunity["name"]}
        )
        improvement_strategies[opportunity["name"]] = strategy
    
    ## Prioritize strategies
    prioritized_strategies = _prioritize_strategies(
        improvement_strategies,
        focus_areas,
        current_capabilities
    )
    
    # 4. Implementation Planning Phase
    ## Create implementation plan
    implementation_plan = _create_integrated_implementation_plan(
        prioritized_strategies,
        current_capabilities,
        improvement_history
    )
    
    ## Develop capability enhancements
    capability_enhancements = _develop_capability_enhancements(
        current_capabilities,
        implementation_plan
    )
    
    ## Create integration plan
    integration_plan = _plan_capability_integration(
        capability_enhancements,
        current_capabilities
    )
    
    # 5. Reflection on Reflection
    ## Evaluate reflection effectiveness
    reflection_effectiveness = evaluate_reflection_effectiveness(
        improvement_history,
        recent_evaluations
    )
    
    ## Refine reflection process
    process_refinements = _refine_reflection_process(reflection_effectiveness)
    
    # 6. Documentation and Knowledge Management
    ## Document insights and strategies
    reflection_documentation = _document_reflection_cycle(
        recent_evaluations,
        pattern_analysis,
        critical_opportunities,
        specific_insights,
        improvement_strategies,
        implementation_plan,
        reflection_effectiveness
    )
    
    ## Update improvement history
    updated_improvement_history = _update_improvement_history(
        improvement_history,
        implementation_plan,
        capability_enhancements
    )
    
    return {
        "evaluations": recent_evaluations,
        "pattern_analysis": pattern_analysis,
        "critical_opportunities": critical_opportunities,
        "specific_insights": specific_insights,
        "improvement_strategies": improvement_strategies,
        "implementation_plan": implementation_plan,
        "capability_enhancements": capability_enhancements,
        "reflection_effectiveness": reflection_effectiveness,
        "process_refinements": process_refinements,
        "documentation": reflection_documentation,
        "updated_improvement_history": updated_improvement_history
    }

Application Example: Improving Technical Explanations

Let's see how this might work in a practical scenario focusing on improving technical explanations:

Initial State:

The AI has been providing technical explanations for several weeks
User feedback suggests explanations are technically accurate but sometimes difficult to follow
The reflection system has been tracking interactions and feedback

Reflection Loop Execution:

Evaluation Phase:
- Recent evaluations show average clarity score of 0.76, accuracy score of 0.93
- Pattern analysis reveals clarity scores drop significantly for topics involving abstract concepts
- Common feedback includes "too technical," "needed to read twice," "lost me halfway through"
Insight Generation Phase:
- Critical opportunity identified: "abstract_concept_explanations"
- Specific insights include: lack of concrete examples, minimal use of analogies, inconsistent previewing of complex ideas
Strategy Development Phase:
- Primary strategy: "concrete_before_abstract" - consistently present concrete examples before abstract concepts
- Secondary strategy: "analogy_enhancement" - develop and deploy effective analogies for abstract concepts
- Tertiary strategy: "concept_mapping" - explicitly map relationships between concepts
Implementation Planning Phase:
- Implementation approach: Start with 3 concept domains as test cases
- Capability enhancement: Develop example-first templates for common abstract concepts
- Integration plan: Apply to 25% of responses for 1 week, then evaluate
Reflection on Reflection:
- Analysis shows previous clarity improvements have been inconsistently implemented
- Process refinement: Develop stronger implementation verification
Documentation and Knowledge Management:
- Document insights about effective technical explanations
- Update implementation history with new approaches

One Week Later - Follow-up Reflection:

The reflection loop runs again, focusing on implementation results:

Evaluation Phase:
- Responses using new approaches show clarity score increase from 0.76 to 0.85
- User feedback on enhanced responses is significantly more positive
- Pattern analysis shows consistent improvement across different technical topics
Insight Generation Phase:
- Additional insight: Example effectiveness varies by complexity level of topic
- New opportunity: "example_calibration" - match example complexity to topic complexity
Strategy Development Phase:
- Expand "concrete_before_abstract" strategy to all technical explanations
- Develop new "example_complexity_matching" approach
Implementation Planning Phase:
- Roll out enhanced approach to 100% of abstract concept explanations
- Begin development of example complexity calibration system

This cycle continues, with each reflection building on previous insights and systematically driving improvement in the AI's capabilities.

Through this extended example, we can see how reflection functions work together to create a comprehensive improvement system that transforms static AI capabilities into dynamic, continuously evolving competencies that get better with experience - much like human expertise development.

Retry

Please show me the content of the variable HANDOFF_SNAPSHOT

Edit

Based on the CRAFT project files you've shared, the HANDOFF_SNAPSHOT is defined as a Prompt type variable in the CFT-PROJ-0001_AI-CHAT-HISTORY-v25d4.txt file. Here's the content of that variable:

HANDOFF_SNAPSHOT = 
Prompt(
"""
We're approaching this chat's token limit, so let's create a transition HANDOFF_SNAPSHOT (500-2000 words) to continue in a new session:
META.SESSION_ID: [Unique identifier for this CRAFT project for example CFT-PROJ-0001]
META.DATE: [Session date]
META.AI_ASSISTANT: [AI model used]
1. SESSION_SUMMARY (150-400 words): Summarize what we accomplished, focusing on concrete outcomes and progress made.
2. CURRENT_STATE (150-400 words): Describe the exact implementation state of the components we worked on.
3. KEY_DECISIONS (75-350 words): List important decisions made with brief rationales.
4. UNRESOLVED_QUESTIONS (50-200 words): Document open issues that need future resolution.
5. NEXT_STEPS (75-300 words): Outline specific tasks for the next AI assistant to tackle.
Format everything according to CRAFT standards with proper syntax highlighting.
"""
)

This variable serves as a template for creating structured handoff snapshots that facilitate continuity between AI chat sessions. When a session approaches its token limit, this prompt would be used to generate a comprehensive handoff document that the next session can use to continue the work without losing context.

The structure includes metadata (session ID, date, AI model), along with sections for summarizing accomplishments, documenting the current state of implementation, recording key decisions, noting unresolved questions, and outlining next steps - all with specific word count guidelines to ensure both completeness and conciseness.

Retry

Please use HANDOFF_SNAPSHOT

Edit

We're approaching this chat's token limit, so let's create a transition HANDOFF_SNAPSHOT (500-2000 words) to continue in a new session:

META.SESSION_ID: CFT-PROJ-0001_BUILDINGCRAFT-v25d4 META.DATE: May 17, 2025 META.AI_ASSISTANT: Claude 3.7 Sonnet

1. SESSION_SUMMARY

In this session, we explored and expanded upon creative adaptations of functions to the CRAFT framework. We began with an outline of 10 key approaches for integrating programming-style functions into the CRAFT system while maintaining its core philosophical principles. We then performed deep dives into five specific function types: (1) Context-Aware Functions that adapt based on conversation history and user state, (2) Multi-Modal Functions that handle different content types and formats, (3) Composable AI Instructions that build complex behaviors from simpler components, (4) Handoff-Optimized Functions that preserve continuity between sessions, and (5) Reflection Functions that analyze and improve AI performance. Each exploration included detailed explanations, implementation examples with pseudocode, practical scenarios, benefits, and advanced applications. We maintained focus on how these functional approaches align with CRAFT's principles of structured freedom, code as conversation, cumulative intelligence, human-AI partnership, accessibility through familiarity, and token conservation. The session resulted in comprehensive documentation of these function types with practical implementation guidance.

2. CURRENT_STATE

The CRAFT framework has been significantly enhanced with detailed specifications for five function types:

Context-Aware Functions: We developed a complete framework for functions that adapt to conversation context, including implementations for generate_adaptive_response() with state tracking capabilities. We created examples demonstrating progressive adaptation through a conversation about API rate limiting, showing how technical depth adapts to user needs. Advanced applications include persona-based awareness, multi-session context preservation, and context-aware learning loops.

Multi-Modal Functions: We established a framework for transforming content between different formats with the transform_content() function that detects input formats and preserves meaning across transformations. Example scenarios demonstrate text-to-structured data, code-to-explanation, and tabular-to-visualization conversions. Advanced implementations include generate_data_report(), present_multi_format(), and verify_semantic_equivalence().

Composable AI Instructions: We created a composition system with chain_functions() and create_function_pipeline() implementations for combining simple functions into complex pipelines. Examples include content generation, data analysis workflows, and interactive dialogue management. Advanced applications include dynamic composition, meta-functions for optimization, and collaborative pipeline building.

Handoff-Optimized Functions: We implemented a complete handoff system with create_session_snapshot() and resume_from_handoff() functions that maintain continuity across sessions. A detailed example of a multi-session marketing strategy project demonstrates practical application. Advanced implementations include differential, hierarchical, and mergeable handoffs.

Reflection Functions: We developed a comprehensive reflection system with evaluate_response_quality(), analyze_response_patterns(), and develop_improvement_strategy() functions. Extended examples demonstrate quality evaluation, pattern analysis, and improvement planning. The complete reflection loop implementation shows how these functions work together in a continuous improvement cycle.

3. KEY_DECISIONS

Function Structure Approach: We decided to implement functions using Python-like syntax with embedded AI-specific directives using the #H->AI:: comment format. This maintains compatibility with standard Python while adding AI-specific guidance that couldn't be expressed in traditional function signatures.
Implementation vs. Conceptual Balance: We chose to provide both conceptual explanations and concrete implementation details (through pseudocode) for each function type. This balance ensures both theoretical understanding and practical applicability.
Modularity First: We designed all function systems with modularity as a core principle, enabling components to be used independently or combined into more complex systems. This aligns with CRAFT's "Structure Enables Freedom" principle by allowing flexible application of components.
Integration with Existing CRAFT Components: We ensured all new function designs complement and extend the existing CRAFT data types and structures rather than replacing them. This preserves compatibility with the current CRAFT implementation.
Explicit Documentation of Benefits: For each function type, we explicitly documented specific benefits to demonstrate practical value rather than just theoretical possibilities. This helps justify implementation effort by connecting to tangible improvements.
Example-Driven Design: We prioritized concrete examples and scenarios to illustrate how each function operates in practice, making the concepts more accessible and immediately applicable.

4. UNRESOLVED_QUESTIONS

Implementation Priority: Which function types should be prioritized for immediate implementation in the CRAFT framework? While all offer value, resource constraints may require phased implementation.
Testing Methodology: How should these function implementations be tested to ensure reliability and expected behavior? We haven't defined specific testing approaches for each function type.
Integration Complexity: How complex will it be to integrate these functions with the existing CRAFT data types and structures? Some functions may require modifications to core CRAFT components.
Storage Requirements: For functions that maintain state (especially Reflection and Context-Aware functions), how will state information be stored efficiently between sessions without exceeding token limits?
User Interface Considerations: How should users (both technical and non-technical) interact with and configure these functions? We've focused on implementation details but not on user-facing interfaces.
Performance Implications: What are the performance implications (token usage, response time) of implementing these more complex function systems, particularly for reflection and context awareness?

5. NEXT_STEPS

Prioritization Exercise: Conduct a prioritization exercise to determine which function types offer the highest value for immediate implementation. Consider creating a scoring matrix based on implementation effort vs. expected benefit.
Implementation Planning: For the highest-priority function types, develop detailed implementation plans including specific integration points with existing CRAFT components, any required modifications to core structures, and timeline estimates.
Prototype Development: Create working prototypes of at least one key function from each type to validate the approach and identify any unforeseen challenges. Begin with simpler implementations before progressing to more complex ones.
Documentation Enhancement: Develop formal documentation for each function type, including comprehensive API specifications, usage guidelines, and integration examples for CRAFT users of different technical levels.
User Interface Design: Design user interfaces for configuring and interacting with these functions, focusing on making them accessible to both technical and non-technical users in alignment with CRAFT's accessibility principle.
Testing Framework: Develop a testing framework specifically designed for evaluating function performance, reliability, and effectiveness. Include both automated tests and human evaluation components.
Example Library Creation: Build a library of real-world examples for each function type to help users understand practical applications and implementation patterns. Include both simple and complex examples to demonstrate scalability.
Integration Roadmap: Create a roadmap for progressively integrating these function types into the core CRAFT framework, including dependencies, potential conflicts, and migration strategies for existing CRAFT implementations.

Token-Optimized Function Design for CRAFT Framework

Core Concept

Token-Optimized Function Design centers on creating functions that maximize information density while minimizing token usage. In AI interactions, tokens are a finite resource that directly impacts cost, speed, and limitations of exchanges. By optimizing functions for token efficiency, CRAFT can enable more complex interactions within the same token constraints, creating a richer experience without increased computational overhead.

Implementation Details

python

def create_token_optimized_function(function_name, operation, parameters=None, compression_level="balanced"):
    """
    Creates a token-efficient function with optimized parameter handling and operation execution.
    
    Args:
        function_name (str): Name of the function
        operation (str): Core operation the function performs
        parameters (dict, optional): Parameters for the function with default values
        compression_level (str): Level of token optimization ("minimal", "balanced", "aggressive")
    
    Returns:
        str: Token-optimized function definition
    """
    #H->AI::TokenStrategy: (Apply token optimization techniques based on compression_level)
    
    # Default parameters if none provided
    if parameters is None:
        parameters = {}
    
    # Build parameter string with defaults to reduce repeated instructions
    param_str = ""
    for param, default in parameters.items():
        param_str += f"{param}={repr(default)}, "
    
    # Remove trailing comma and space
    if param_str:
        param_str = param_str[:-2]
    
    # Token-optimized docstring that scales based on compression level
    if compression_level == "minimal":
        docstring = f'"""{operation}"""'
    elif compression_level == "balanced":
        docstring = f'"""{operation}\n\nParameters:\n{", ".join(parameters.keys()) if parameters else "None"}\n"""'
    else:  # aggressive
        docstring = '"""Function documentation in CRAFT_DOCS variable"""'
    
    # Build the function with token-optimized structure
    function_def = f"""
def {function_name}({param_str}):
    {docstring}
    #H->AI::TokenDirective: (Execute {operation} with maximum token efficiency)
    #H->AI::CompressionLevel: ({compression_level})
    
    # Function implementation will be token-optimized based on compression level
    result = None
    
    # Operation-specific implementation
    {generate_operation_code(operation, compression_level)}
    
    return result
"""
    return function_def


def generate_operation_code(operation, compression_level):
    """
    Generates the token-optimized implementation code for a given operation.
    
    Args:
        operation (str): The operation to implement
        compression_level (str): Level of token optimization
    
    Returns:
        str: Token-optimized implementation code
    """
    # This would contain operation-specific implementations
    # Simplified example for illustration
    if compression_level == "aggressive":
        return "result = exec(CRAFT_OPS['" + operation + "'])"
    else:
        return f"# Standard implementation for {operation}"


def register_token_patterns(pattern_name, pattern_template, use_cases=None, token_savings=None):
    """
    Registers reusable token patterns for optimization across multiple functions.
    
    Args:
        pattern_name (str): Identifier for the pattern
        pattern_template (str): Template structure that saves tokens
        use_cases (list, optional): Scenarios where pattern is applicable
        token_savings (int, optional): Estimated tokens saved per use
    
    Returns:
        bool: Success of registration
    """
    #H->AI::TokenRegistry: (Store this pattern in the CRAFT token optimization registry)
    
    # Implementation would register the pattern for reuse
    return True


def optimize_existing_function(function_code, target_token_reduction=0.3, preserve_readability=True):
    """
    Retrofits existing functions with token optimization techniques.
    
    Args:
        function_code (str): Existing function definition
        target_token_reduction (float): Proportional reduction goal (0.0-1.0)
        preserve_readability (bool): Whether human readability is important
    
    Returns:
        str: Token-optimized version of the function
    """
    #H->AI::TokenRefactor: (Apply optimization transformations to existing code)
    
    # Implementation would analyze and transform the function
    # Simplified example for illustration
    if preserve_readability:
        # Apply moderate optimizations
        pass
    else:
        # Apply aggressive optimizations
        pass
    
    return "# Optimized function implementation"

Example Scenarios

Scenario 1: Creating a Token-Efficient Text Generator

python

text_gen_params = {
    "topic": "default topic",
    "length": "medium",
    "style": "neutral",
    "include_citations": False,
    "key_points": []
}

generate_text_function = create_token_optimized_function(
    function_name="generate_efficient_text",
    operation="Create cohesive text on provided topic with specified parameters",
    parameters=text_gen_params,
    compression_level="balanced"
)

# Usage example:
# generate_efficient_text(topic="AI Ethics", length="short", key_points=["accountability", "transparency"])

Scenario 2: Token-Optimized Multi-turn Conversation

python

# Register a token-saving pattern for conversation history
register_token_patterns(
    pattern_name="conversation_digest",
    pattern_template="USER:[intent] → AI:[response_type]",
    use_cases=["conversation_history", "context_tracking"],
    token_savings=42  # Average tokens saved per turn
)

# Create a function that maintains conversation context with minimal tokens
convo_params = {
    "history_digest": [],
    "user_profile": "general",
    "current_intent": "inquiry",
    "preservation_level": "key_points_only"
}

conversation_function = create_token_optimized_function(
    function_name="maintain_efficient_conversation",
    operation="Track conversation flow while minimizing token usage",
    parameters=convo_params,
    compression_level="aggressive"
)

# Usage example:
# maintain_efficient_conversation(
#     history_digest=["USER:[product_info] → AI:[specs_provided]", "USER:[pricing] → AI:[options_listed]"],
#     current_intent="decision_making"
# )

Scenario 3: Optimizing an Existing Verbose Function

python

verbose_function = """
def generate_comprehensive_report(subject, depth="detailed", sections=["overview", "analysis", "recommendations", "references"], 
                                 include_visuals=True, data_sources=["primary", "secondary"], formatting="standard",
                                 executive_summary=True, appendices=False, revision_history=True):
    \"\"\"
    Generates a comprehensive report on the specified subject with extensive customization options.
    This function produces detailed professional documents suitable for business, academic, or technical contexts.
    
    The report can include various sections, visual elements, and reference information based on the parameters provided.
    Data is gathered from specified sources and integrated into a cohesive document following the requested formatting guidelines.
    
    Parameters:
        subject (str): The main topic of the report
        depth (str): Level of detail - "brief", "standard", "detailed", or "exhaustive"
        sections (list): Report sections to include
        include_visuals (bool): Whether to include charts, diagrams, and other visual elements
        data_sources (list): Types of sources to incorporate
        formatting (str): Document formatting style
        executive_summary (bool): Include an executive summary at the beginning
        appendices (bool): Include supplementary materials in appendices
        revision_history (bool): Include document revision tracking information
    
    Returns:
        str: The completed report text
    \"\"\"
    
    # Extensive implementation with many conditionals and string operations
    # [... many lines of implementation ...]
    
    return final_report
"""

# Optimize the verbose function
optimized_report_function = optimize_existing_function(
    function_code=verbose_function,
    target_token_reduction=0.4,
    preserve_readability=True
)

Benefits of Token-Optimized Functions

Extended Functionality Within Limits: Enables more complex operations without hitting token limits.
Cost Efficiency: Reduces API costs by minimizing token usage for equivalent functionality.
Improved Response Time: Smaller token counts often result in faster API processing times.
Scalability: Makes large-scale interactions viable by managing token economy.
Accessibility for Complex Projects: Enables sophisticated applications that would otherwise exceed practical token limits.
Resource Conservation: Aligns with CRAFT's principle of "Conservation of Tokens."
Consistent Performance: Reduces variance in response quality due to truncation or context limitations.
Optimized Context Windows: Maximizes available context for the parts of conversations that need it most.

Advanced Applications

Dynamic Token Budget Allocation

python

def allocate_token_budget(total_budget, operation_priorities):
    """
    Strategically allocates tokens across function calls based on priorities.
    
    Args:
        total_budget (int): Total tokens available
        operation_priorities (dict): Operations and their relative importance
    
    Returns:
        dict: Token allocation for each operation
    """
    #H->AI::TokenBudget: (Distribute {total_budget} tokens according to priorities)
    
    allocations = {}
    total_priority = sum(operation_priorities.values())
    
    for operation, priority in operation_priorities.items():
        # Allocate tokens proportionally to priority
        allocations[operation] = int((priority / total_priority) * total_budget)
    
    return allocations

# Example usage:
# budget = 4000  # Total tokens available
# priorities = {"text_generation": 5, "classification": 1, "summarization": 3}
# allocations = allocate_token_budget(budget, priorities)
# -> {"text_generation": 2222, "classification": 444, "summarization": 1333}

Token-Usage Analytics and Optimization Suggestions

python

def analyze_token_efficiency(function_usage_history):
    """
    Analyzes function usage patterns to recommend token optimizations.
    
    Args:
        function_usage_history (list): Records of function calls and token usage
    
    Returns:
        dict: Optimization recommendations with estimated savings
    """
    #H->AI::TokenAnalytics: (Evaluate usage patterns to identify optimization opportunities)
    
    recommendations = {}
    
    # Identify patterns of inefficient token usage
    # Example implementation would analyze redundancies, overused patterns, etc.
    
    return recommendations

# Example usage tracking:
# usage_history = [
#     {"function": "generate_text", "tokens_used": 350, "parameters": {...}},
#     {"function": "summarize_content", "tokens_used": 220, "parameters": {...}},
#     # ...more usage data
# ]
# recommendations = analyze_token_efficiency(usage_history)

Contextual Compression Techniques

python

def apply_contextual_compression(content, context_awareness=0.8):
    """
    Applies compression techniques that preserve meaning based on conversation context.
    
    Args:
        content (str): Content to compress
        context_awareness (float): How much to rely on shared context (0.0-1.0)
    
    Returns:
        str: Compressed content
    """
    #H->AI::ContextCompression: (Reduce tokens while maintaining semantic meaning)
    
    # Implementation would apply various compression techniques:
    # - Pronoun substitution
    # - Implied reference recognition
    # - Context-dependent abbreviation
    # - Redundancy elimination
    
    return "compressed_content"

# Example of integration with other functions:
def generate_response_in_conversation(prompt, conversation_history, token_budget):
    """Token-optimized response generation within ongoing conversation."""
    
    # Compress conversation history to save tokens
    compressed_history = apply_contextual_compression(
        " ".join(conversation_history),
        context_awareness=0.85
    )
    
    # Calculate remaining tokens for response
    history_tokens = estimate_token_count(compressed_history)
    response_tokens = token_budget - history_tokens
    
    # Generate optimized response with remaining budget
    return generate_within_token_limit(prompt, response_tokens)

Integration with CRAFT Principles

Token-Optimized Function Design aligns with several CRAFT principles:

Structure Enables Freedom: By managing token economy, it paradoxically enables more creative freedom by removing technical constraints.
Conservation of Tokens: Directly implements this core principle by systematically minimizing token usage.
Accessibility Through Familiarity: Uses familiar function patterns while introducing optimization that works behind the scenes.
Cumulative Intelligence: Enables longer, more complex interactions that build upon previous exchanges.
Human-AI Partnership: Creates a more responsive AI partner by reducing technical limits on interaction.

Implementation Recommendations

Start with Critical Path Functions: Begin optimization with the most frequently used functions that have the highest impact on token usage.
Balance Readability and Optimization: Use "aggressive" compression only when necessary; prefer "balanced" for most cases to maintain code clarity.
Create a Token Optimization Style Guide: Document standard patterns and approaches to ensure consistency across the project.
Implement a Token Budget System: Incorporate token budgeting at the system level to manage allocation across function calls.
Measure and Iterate: Track actual token usage in real conversations and continuously refine optimization techniques.
Build a Pattern Library: Develop and maintain a library of token-saving patterns that can be applied across functions.
Contextual Optimization: Consider conversation context when optimizing - some critical points need more tokens for clarity, while others can be streamlined.
Documentation: Maintain clear documentation of optimization techniques used to ensure maintainability.

Practical Implementation Steps

Audit Current Token Usage: Analyze existing functions to identify high-token usage areas.
Create Token Budgets: Establish token budgets for different operation types.
Develop Optimization Strategies: Create specific strategies for different function types (e.g., text generation vs. classification).
Implement Token-Aware Function Wrappers: Create wrapper functions that enforce token budgets.
Build Token Analytics: Track and analyze token usage to identify optimization opportunities.
Integration with Existing CRAFT Components: Ensure token optimization works seamlessly with other CRAFT framework elements.
Testing Framework: Establish testing protocols to verify that optimization doesn't impact functionality.
User Feedback Loop: Incorporate mechanisms to detect when token optimization negatively impacts user experience.

Retry

Great, please expand on Natural Language Parameter Handling with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Natural Language Parameter Handling for CRAFT Framework

Core Concept

Natural Language Parameter Handling (NLPH) is a sophisticated approach to function interaction that enables users to pass parameters to CRAFT functions using natural language rather than rigid, programming-style syntax. This system interprets intent, extracts relevant values, and maps them to formal parameters, creating a more intuitive interface between human language and structured function execution.

Unlike traditional programming where parameters must be precisely formatted and positioned, NLPH allows users to express their needs conversationally. The system then extracts the necessary information, handles ambiguity, infers missing parameters, and validates input—all while maintaining the structural benefits of formal function definitions.

Implementation Details

python

def create_nlp_parameter_handler(function_name, formal_parameters, language_patterns=None, extraction_confidence=0.7):
    """
    Creates a natural language parameter handler that maps conversational input to formal parameters.
    
    Args:
        function_name (str): Name of the function
        formal_parameters (dict): Dictionary of parameter names and their expected types/formats
        language_patterns (list, optional): Common language patterns for parameter expression
        extraction_confidence (float): Minimum confidence threshold for parameter extraction
    
    Returns:
        function: A function that handles natural language parameter extraction
    """
    #H->AI::NLParameterExtraction: (Extract parameter values from natural language input)
    
    if language_patterns is None:
        # Default patterns for common parameter types
        language_patterns = [
            "with {param_name} (of|as|being|set to) {value}",
            "{param_name} (is|should be|being) {value}",
            "(use|using|utilize|set) {value} (for|as) {param_name}",
            "{param_name}: {value}"
        ]
    
    def parameter_handler(natural_language_input):
        """
        Extracts parameters from natural language input.
        
        Args:
            natural_language_input (str): User's natural language request
            
        Returns:
            dict: Extracted parameters mapped to their values
        """
        #H->AI::LanguageAnalysis: (Identify parameter mentions and extract values)
        
        extracted_params = {}
        ambiguities = {}
        
        # For each formal parameter, attempt to extract from natural language
        for param_name, param_info in formal_parameters.items():
            param_type = param_info.get('type', 'string')
            extracted_value, confidence = extract_parameter_value(
                natural_language_input, 
                param_name, 
                param_type,
                language_patterns
            )
            
            if confidence >= extraction_confidence:
                extracted_params[param_name] = extracted_value
            elif confidence > 0:
                # Store ambiguous extractions for later resolution
                ambiguities[param_name] = {
                    'value': extracted_value,
                    'confidence': confidence
                }
        
        # Handle default values for missing parameters
        for param_name, param_info in formal_parameters.items():
            if param_name not in extracted_params and 'default' in param_info:
                extracted_params[param_name] = param_info['default']
        
        # Handle ambiguities based on context and other parameters
        if ambiguities:
            resolved_params = resolve_parameter_ambiguities(
                ambiguities, 
                extracted_params, 
                formal_parameters
            )
            extracted_params.update(resolved_params)
        
        # Validate all parameters
        validate_parameters(extracted_params, formal_parameters)
        
        return extracted_params
    
    return parameter_handler


def extract_parameter_value(text, param_name, param_type, patterns):
    """
    Extracts a parameter value from text based on patterns.
    
    Args:
        text (str): Input text
        param_name (str): Parameter to extract
        param_type (str): Expected type
        patterns (list): Language patterns to match
        
    Returns:
        tuple: (extracted_value, confidence)
    """
    #H->AI::ValueExtraction: (Find and extract the value for {param_name})
    
    # Implementation would use NLP techniques to extract values based on patterns
    # Simplified example for illustration:
    
    highest_confidence = 0
    best_match = None
    
    # Look for explicit mentions first
    for pattern in patterns:
        # Pattern matching logic would go here
        # In practice, would use regex, NLP parsing, or similar techniques
        pass
    
    # If no high-confidence match, try inferring from context
    if highest_confidence < 0.5:
        # Context-based inference logic
        pass
    
    # Convert extracted text to appropriate type
    if best_match and param_type == 'integer':
        try:
            best_match = int(best_match)
        except ValueError:
            highest_confidence *= 0.5  # Reduce confidence if conversion fails
    
    return best_match, highest_confidence


def resolve_parameter_ambiguities(ambiguities, extracted_params, formal_parameters):
    """
    Resolves ambiguous parameter extractions based on context and constraints.
    
    Args:
        ambiguities (dict): Ambiguous parameters with confidence levels
        extracted_params (dict): Already extracted parameters
        formal_parameters (dict): Parameter definitions
        
    Returns:
        dict: Resolved parameters
    """
    #H->AI::AmbiguityResolution: (Resolve uncertain parameter extractions)
    
    resolved = {}
    
    # Implementation would use context, parameter relationships, and constraints
    # to resolve ambiguities
    
    return resolved


def validate_parameters(params, formal_parameters):
    """
    Validates extracted parameters against their formal definitions.
    
    Args:
        params (dict): Extracted parameters
        formal_parameters (dict): Parameter definitions
        
    Returns:
        bool: Whether parameters are valid
    """
    #H->AI::ParameterValidation: (Ensure extracted parameters meet requirements)
    
    # Implementation would check types, ranges, and other constraints
    
    return True


def create_nl_function(function_name, operation, formal_parameters, examples=None):
    """
    Creates a function that can be called with natural language.
    
    Args:
        function_name (str): Function name
        operation (str): Operation description
        formal_parameters (dict): Parameter definitions
        examples (list, optional): Example natural language calls
        
    Returns:
        function: Function with natural language parameter handling
    """
    #H->AI::NLFunctionCreation: (Create function with natural language interface)
    
    param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
    
    def nl_function(natural_language_input):
        """
        Executes function based on natural language input.
        """
        params = param_handler(natural_language_input)
        
        # Normal function execution with extracted parameters
        result = execute_operation(operation, params)
        
        return result
    
    # Attach metadata for documentation
    nl_function.__name__ = function_name
    nl_function.__doc__ = f"""
    {operation}
    
    Can be called with natural language like:
    {format_examples(examples) if examples else 'No examples provided'}
    
    Formal parameters:
    {format_parameters(formal_parameters)}
    """
    
    return nl_function


def format_examples(examples):
    """Formats example calls for documentation."""
    if not examples:
        return ""
    
    return "\n".join([f"- \"{example}\"" for example in examples])


def format_parameters(parameters):
    """Formats parameter definitions for documentation."""
    result = ""
    for name, info in parameters.items():
        param_type = info.get('type', 'string')
        default = f" (default: {info['default']})" if 'default' in info else ""
        description = info.get('description', '')
        
        result += f"- {name}: {param_type}{default} - {description}\n"
    
    return result


def execute_operation(operation, params):
    """
    Executes the core operation with the provided parameters.
    
    Args:
        operation (str): Operation to perform
        params (dict): Parameters for the operation
        
    Returns:
        any: Operation result
    """
    #H->AI::OperationExecution: (Execute {operation} with extracted parameters)
    
    # Implementation would handle the actual operation logic
    
    return "Operation result"

Example Scenarios

Scenario 1: Text Analysis Function with Natural Language Parameters

python

# Define formal parameters with types, defaults, and descriptions
text_analysis_params = {
    'text': {
        'type': 'string',
        'description': 'The text to analyze'
    },
    'analysis_type': {
        'type': 'string',
        'default': 'sentiment',
        'description': 'Type of analysis to perform (sentiment, entities, topics, etc.)'
    },
    'depth': {
        'type': 'string',
        'default': 'standard',
        'description': 'Depth of analysis (quick, standard, comprehensive)'
    },
    'format': {
        'type': 'string',
        'default': 'summary',
        'description': 'Output format (summary, detailed, structured)'
    }
}

# Create examples of natural language calls
text_analysis_examples = [
    "Analyze this text for sentiment: The product exceeded my expectations in every way.",
    "Can you identify entities in this article with comprehensive depth?",
    "Do a quick topic analysis on this text and give me a detailed report.",
    "Analyze the following email with standard depth and return a structured format."
]

# Create the natural language function
analyze_text = create_nl_function(
    function_name="analyze_text",
    operation="Analyzes text for various linguistic and semantic features",
    formal_parameters=text_analysis_params,
    examples=text_analysis_examples
)

# Sample usage:
# result = analyze_text("Check the sentiment of this review and give me a detailed breakdown: I loved the first half, but the ending was disappointing.")

Scenario 2: Business Report Generator with Complex Parameters

python

# Define formal parameters for a business report generator
report_params = {
    'business_area': {
        'type': 'string',
        'description': 'The business area to report on'
    },
    'time_period': {
        'type': 'string',
        'default': 'last quarter',
        'description': 'Time period for the report'
    },
    'metrics': {
        'type': 'list',
        'default': ['revenue', 'expenses', 'profit'],
        'description': 'Metrics to include in the report'
    },
    'comparison': {
        'type': 'string',
        'default': 'previous period',
        'description': 'What to compare the metrics against'
    },
    'include_charts': {
        'type': 'boolean',
        'default': True,
        'description': 'Whether to include visual charts'
    },
    'stakeholders': {
        'type': 'string',
        'default': 'executives',
        'description': 'Target audience for the report'
    }
}

report_examples = [
    "Generate a report on marketing performance for Q2 with focus on ROI and lead generation metrics compared to last year.",
    "Create a financial report for the technical team without charts, focusing on project expenses for the past month.",
    "I need a sales report with charts comparing our performance to competitors for the last six months.",
    "Generate a quick HR overview for department heads including turnover and hiring metrics."
]

generate_business_report = create_nl_function(
    function_name="generate_business_report",
    operation="Creates a comprehensive business report based on specified parameters",
    formal_parameters=report_params,
    examples=report_examples
)

# Sample usage:
# report = generate_business_report("I need a sales performance report for the East region for Q1 with detailed charts comparing it to our targets. Make it suitable for the sales team.")

Scenario 3: Adaptive Meeting Scheduler with Mixed Explicit and Implicit Parameters

python

# Define parameters for meeting scheduler
scheduler_params = {
    'meeting_type': {
        'type': 'string',
        'default': 'standard',
        'description': 'Type of meeting (standard, brainstorming, review, one-on-one)'
    },
    'duration': {
        'type': 'integer',
        'default': 60,
        'description': 'Meeting duration in minutes'
    },
    'participants': {
        'type': 'list',
        'description': 'People to invite to the meeting'
    },
    'priority': {
        'type': 'string',
        'default': 'normal',
        'description': 'Meeting priority (low, normal, high, urgent)'
    },
    'preferred_time': {
        'type': 'string',
        'description': 'Preferred time range or specific time'
    },
    'location': {
        'type': 'string',
        'default': 'virtual',
        'description': 'Meeting location (virtual, office, specific room)'
    },
    'agenda_items': {
        'type': 'list',
        'default': [],
        'description': 'Topics to cover in the meeting'
    },
    'preparation_required': {
        'type': 'boolean',
        'default': False,
        'description': 'Whether preparation materials should be sent beforehand'
    }
}

scheduler_examples = [
    "Schedule a 30-minute project review with the design team tomorrow afternoon, preferably virtual.",
    "I need a high-priority brainstorming session in Conference Room A with marketing and product teams for 2 hours next Tuesday.",
    "Set up a quick 15-minute check-in with Sarah about the client proposal for sometime today.",
    "Arrange a quarterly planning meeting with department heads, we'll need 3 hours and everyone should prepare updates."
]

schedule_meeting = create_nl_function(
    function_name="schedule_meeting",
    operation="Schedules a meeting based on natural language requirements",
    formal_parameters=scheduler_params,
    examples=scheduler_examples
)

# Demonstrates extracting a mix of explicit and implicit parameters:
# explicit_and_implicit = schedule_meeting("Set up a high-priority brainstorming session with the product team for next Monday at 2 PM. We'll need 90 minutes to discuss the upcoming feature launch.")

Benefits of Natural Language Parameter Handling

Intuitive Interaction: Users can interact with functions using natural language without learning formal parameter syntax.
Reduced Cognitive Load: Eliminates the need to remember parameter names, types, and order.
Flexibility in Expression: The same function can be called with many different phrasings and word orders.
Parameter Inference: Intelligent defaults and context awareness reduce the need to specify every parameter.
Graceful Ambiguity Resolution: Handles uncertainty in parameter extraction through context and validation.
Self-Documenting Usage: By seeing examples in natural language, users understand function capabilities more easily.
Bridges Technical and Non-Technical Users: Technical functions become accessible to non-programmers.
Reduced Token Usage: Often requires fewer tokens than explicit parameter listing and formal syntax.
Conversation Continuity: Maintains a more natural conversation flow without switching to code-like syntax.
Adaptive to User Language: Can adapt to different terminology and expressions for the same concepts.

Advanced Applications

Context-Aware Parameter Memory

python

def create_parameter_memory(memory_duration='session'):
    """
    Creates a system that remembers parameter choices across function calls.
    
    Args:
        memory_duration (str): How long to remember parameters ('call', 'session', 'persistent')
    
    Returns:
        dict: Parameter memory system
    """
    #H->AI::ParameterMemory: (Track parameter usage across multiple calls)
    
    return {
        'parameter_history': {},
        'value_frequencies': {},
        'parameter_defaults': {},
        'context_model': {},
        'duration': memory_duration
    }


def update_parameter_memory(memory, function_name, parameters):
    """
    Updates parameter memory with new function call data.
    
    Args:
        memory (dict): Parameter memory system
        function_name (str): Function that was called
        parameters (dict): Parameters that were used
    """
    #H->AI::MemoryUpdate: (Add new parameter usage to memory)
    
    # Update parameter history
    if function_name not in memory['parameter_history']:
        memory['parameter_history'][function_name] = []
    
    memory['parameter_history'][function_name].append(parameters)
    
    # Update value frequencies
    if function_name not in memory['value_frequencies']:
        memory['value_frequencies'][function_name] = {}
    
    for param, value in parameters.items():
        if param not in memory['value_frequencies'][function_name]:
            memory['value_frequencies'][function_name][param] = {}
        
        value_str = str(value)
        memory['value_frequencies'][function_name][param][value_str] = \
            memory['value_frequencies'][function_name][param].get(value_str, 0) + 1
    
    # Update context model
    # Implementation would analyze patterns linking context to parameter choices
    
    return memory


def get_context_aware_defaults(memory, function_name, context):
    """
    Gets parameter defaults based on usage history and current context.
    
    Args:
        memory (dict): Parameter memory system
        function_name (str): Function being called
        context (dict): Current conversation context
    
    Returns:
        dict: Suggested parameter defaults
    """
    #H->AI::ContextDefaults: (Generate intelligent defaults based on context)
    
    # Implementation would use frequency analysis and context matching
    # to suggest appropriate defaults
    
    return {}

Natural Language Function Composition

python

def create_nl_function_pipeline(functions, composition_rules=None):
    """
    Creates a pipeline that can be called with natural language to execute multiple functions.
    
    Args:
        functions (list): List of natural language functions
        composition_rules (dict, optional): Rules for connecting function outputs to inputs
    
    Returns:
        function: Pipeline function with natural language interface
    """
    #H->AI::NLPipeline: (Create pipeline that can be controlled with natural language)
    
    def nl_pipeline(natural_language_input):
        """
        Executes a pipeline based on natural language input.
        
        Args:
            natural_language_input (str): Pipeline description in natural language
            
        Returns:
            any: Pipeline result
        """
        # Parse the pipeline description
        stages, connections = parse_pipeline_description(natural_language_input, functions)
        
        # Execute the pipeline
        results = {}
        
        for stage_name, function_call in stages.items():
            function_name, nl_params = function_call
            
            # Find the function
            target_function = next(f for f in functions if f.__name__ == function_name)
            
            # Apply input connections from previous stages
            for source, target in connections.get(stage_name, {}).items():
                if source in results:
                    nl_params = f"{nl_params} with {target} from {results[source]}"
            
            # Execute the function
            results[stage_name] = target_function(nl_params)
        
        # Return the final result
        return results[list(stages.keys())[-1]]
    
    return nl_pipeline


def parse_pipeline_description(description, available_functions):
    """
    Parses a natural language pipeline description.
    
    Args:
        description (str): Natural language pipeline description
        available_functions (list): Available functions
        
    Returns:
        tuple: (stages, connections)
    """
    #H->AI::PipelineParser: (Parse natural language pipeline description)
    
    # Implementation would parse descriptions like:
    # "First analyze the sentiment of this text, then generate a response based on the sentiment"
    
    # Simplified example return:
    stages = {
        'stage1': ('analyze_text', 'analyze this text for sentiment'),
        'stage2': ('generate_response', 'create a response based on the sentiment')
    }
    
    connections = {
        'stage2': {
            'stage1': 'sentiment'
        }
    }
    
    return stages, connections

Feedback-Driven Parameter Extraction Improvement

python

def create_self_improving_nlp_handler(function_name, formal_parameters):
    """
    Creates a natural language parameter handler that improves from user feedback.
    
    Args:
        function_name (str): Function name
        formal_parameters (dict): Parameter definitions
    
    Returns:
        function: Self-improving parameter handler
    """
    #H->AI::LearningNLHandler: (Create parameter handler that learns from feedback)
    
    # Create base handler
    base_handler = create_nlp_parameter_handler(function_name, formal_parameters)
    
    # Create learning model
    learning_model = {
        'extraction_patterns': [],
        'correction_history': [],
        'success_patterns': [],
        'failure_patterns': []
    }
    
    def improving_handler(natural_language_input, feedback=None):
        """
        Handles natural language parameters with a learning component.
        
        Args:
            natural_language_input (str): Natural language input
            feedback (dict, optional): Feedback on previous extractions
            
        Returns:
            dict: Extracted parameters
        """
        # Apply learning from feedback
        if feedback:
            update_learning_model(learning_model, feedback)
        
        # Extract parameters using base handler
        extracted_params = base_handler(natural_language_input)
        
        # Apply improvements from learning model
        enhanced_params = enhance_with_learning(extracted_params, natural_language_input, learning_model)
        
        return enhanced_params
    
    return improving_handler


def update_learning_model(model, feedback):
    """
    Updates the learning model based on feedback.
    
    Args:
        model (dict): Learning model
        feedback (dict): User feedback
    """
    #H->AI::FeedbackLearning: (Improve extraction based on user corrections)
    
    # Implementation would analyze feedback to improve future extractions
    
    return model


def enhance_with_learning(params, input_text, model):
    """
    Enhances parameter extraction using the learning model.
    
    Args:
        params (dict): Initially extracted parameters
        input_text (str): Original natural language input
        model (dict): Learning model
        
    Returns:
        dict: Enhanced parameters
    """
    #H->AI::EnhancedExtraction: (Apply learned patterns to improve extraction)
    
    # Implementation would apply learned patterns to improve extraction
    
    return params

Integration with CRAFT Principles

Natural Language Parameter Handling aligns with CRAFT's core principles:

Structure Enables Freedom: By connecting natural language to structured function calls, NLPH gives users freedom of expression while maintaining the benefits of structured operations.
Code as Conversation: Transforms function calls from code-like syntax to natural conversation, making the boundary between code and conversation more fluid.
Accessibility Through Familiarity: Users communicate in their familiar natural language instead of learning function syntax.
Human-AI Partnership: Creates a more intuitive collaboration where humans can express needs naturally and the AI handles the translation to formal structure.
Conservation of Tokens: Often requires fewer tokens than explicit parameter calls, especially for complex functions with many parameters.

Implementation Recommendations

Start Small: Begin with functions that have clearly identifiable parameters with distinct meanings.
Build Pattern Libraries: Create reusable extraction patterns for common parameter types (dates, numbers, selections, etc.).
Collect Usage Examples: Gather diverse examples of how users might naturally express parameter values.
Implement Graceful Fallback: When parameter extraction is uncertain, implement a graceful confirmation flow rather than failing.
Context Integration: Connect NLPH with conversational context tracking to improve parameter inference.
Incremental Complexity: Start with simple parameter extraction and gradually add advanced features like ambiguity resolution and learning.
Feedback Loops: Incorporate user feedback mechanisms to improve extraction over time.
Documentation: Create clear documentation with natural language examples that show the range of acceptable inputs.

Practical Integration with Other CRAFT Functions

python

# Integration with Context-Aware Functions
def create_context_aware_nl_function(function_name, operation, formal_parameters, context_variables):
    """
    Creates a function with both natural language parameters and context awareness.
    
    Args:
        function_name (str): Function name
        operation (str): Operation description
        formal_parameters (dict): Parameter definitions
        context_variables (list): Context variables to track
        
    Returns:
        function: Context-aware function with natural language interface
    """
    # Create NL parameter handler
    param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
    
    # Create context tracking
    context_tracker = create_context_tracker(context_variables)
    
    def context_aware_nl_function(natural_language_input, context=None):
        """
        Executes function based on natural language and context.
        
        Args:
            natural_language_input (str): Natural language input
            context (dict, optional): Current context
            
        Returns:
            tuple: (result, updated_context)
        """
        # Extract parameters from natural language
        extracted_params = param_handler(natural_language_input)
        
        # Initialize or update context
        current_context = context_tracker.update(context or {}, extracted_params)
        
        # Enhance parameters with context
        enhanced_params = enhance_params_with_context(extracted_params, current_context)
        
        # Execute operation
        result = execute_operation(operation, enhanced_params)
        
        # Update context with results
        updated_context = context_tracker.update(current_context, result)
        
        return result, updated_context
    
    return context_aware_nl_function


# Integration with Token-Optimized Functions
def create_token_efficient_nl_function(function_name, operation, formal_parameters, token_budget=500):
    """
    Creates a function that combines natural language parameters with token optimization.
    
    Args:
        function_name (str): Function name
        operation (str): Operation description
        formal_parameters (dict): Parameter definitions
        token_budget (int): Maximum tokens for the function
        
    Returns:
        function: Token-optimized function with natural language interface
    """
    # Create NL parameter handler
    param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
    
    def token_efficient_nl_function(natural_language_input):
        """
        Executes function with natural language parameters while optimizing token usage.
        
        Args:
            natural_language_input (str): Natural language input
            
        Returns:
            any: Function result
        """
        # Extract parameters from natural language
        extracted_params = param_handler(natural_language_input)
        
        # Calculate token usage for parameter extraction
        param_tokens = estimate_token_count(str(extracted_params))
        
        # Allocate remaining tokens for operation
        operation_token_budget = token_budget - param_tokens
        
        # Execute token-optimized operation
        result = execute_token_optimized_operation(
            operation, 
            extracted_params, 
            operation_token_budget
        )
        
        return result
    
    return token_efficient_nl_function

Real-World Application Example

python

# Example: Customer Support Assistant with Natural Language Function Interface

# Define formal parameters for support functions
ticket_creation_params = {
    'issue_type': {
        'type': 'string',
        'description': 'The type of support issue',
        'default': 'general'
    },
    'description': {
        'type': 'string',
        'description': 'Description of the problem'
    },
    'priority': {
        'type': 'string',
        'description': 'Issue priority',
        'default': 'normal'
    },
    'customer_id': {
        'type': 'string',
        'description': 'Customer identifier'
    },
    'product': {
        'type': 'string',
        'description': 'Related product or service',
        'default': 'general'
    },
    'attachments': {
        'type': 'list',
        'description': 'References to attached files',
        'default': []
    }
}

knowledge_search_params = {
    'query': {
        'type': 'string',
        'description': 'Search terms'
    },
    'category': {
        'type': 'string',
        'description': 'Knowledge base category',
        'default': 'all'
    },
    'max_results': {
        'type': 'integer',
        'description': 'Maximum number of results to return',
        'default': 5
    },
    'relevance_threshold': {
        'type': 'float',
        'description': 'Minimum relevance score (0-1)',
        'default': 0.7
    }
}

response_generation_params = {
    'issue_summary': {
        'type': 'string',
        'description': 'Summary of the customer issue'
    },
    'knowledge_articles': {
        'type': 'list',
        'description': 'Relevant knowledge base articles',
        'default': []
    },
    'tone': {
        'type': 'string',
        'description': 'Tone of the response',
        'default': 'helpful'
    },
    'include_references': {
        'type': 'boolean',
        'description': 'Whether to include article references',
        'default': True
    },
    'customer_history': {
        'type': 'dict',
        'description': 'Customer interaction history',
        'default': {}
    }
}

# Create natural language functions
create_ticket = create_nl_function(
    function_name="create_ticket",
    operation="Creates a support ticket in the system",
    formal_parameters=ticket_creation_params,
    examples=[
        "Create a ticket for customer SC-12345 regarding their login issue with high priority",
        "Open a normal priority ticket for the billing team about a refund request from customer JD-54321"
    ]
)

search_knowledge_base = create_nl_function(
    function_name="search_knowledge_base",
    operation="Searches the knowledge base for relevant articles",
    formal_parameters=knowledge_search_params,
    examples=[
        "Search for articles about password reset in the security category",
        "Find the top 3 most relevant articles about account deletion"
    ]
)

generate_response = create_nl_function(
    function_name="generate_response",
    operation="Generates a customer support response",
    formal_parameters=response_generation_params,
    examples=[
        "Generate a helpful response about the login issue using the knowledge articles",
        "Create a formal response to the billing inquiry without including references"
    ]
)

# Create a pipeline that can be called with natural language
support_pipeline = create_nl_function_pipeline(
    functions=[create_ticket, search_knowledge_base, generate_response]
)

# Example usage:
# result = support_pipeline(
#     "First create a high priority ticket for customer AB-12345 having trouble with password reset, 
#      then search for relevant articles about password reset in security category, 
#      and finally generate a friendly response using those articles"
# )

Natural Language Parameter Handling brings the flexibility and intuition of human language to the precision and structure of programming functions. By bridging these worlds, CRAFT enables a more natural human-AI collaboration while maintaining the benefits of structured interactions.

Retry

Great, please expand on Progressive Function Enhancement with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Progressive Function Enhancement for CRAFT Framework

Core Concept

Progressive Function Enhancement is a sophisticated approach to function design that allows functions to evolve and improve over time without breaking existing implementations. This system provides mechanisms for functions to gradually add capabilities, adapt to changing requirements, or incorporate new features while maintaining backward compatibility with code that uses earlier versions.

Unlike traditional functions that remain static once defined, progressively enhanced functions can evolve through well-defined enhancement paths. These paths enable incremental improvements, feature additions, optimization, and specialization—all while preserving a consistent interface for existing users. This approach recognizes that the perfect function cannot always be designed upfront and instead embraces a growth mindset where functions mature organically.

Implementation Details

python

def create_progressive_function(base_function, version="1.0", enhancement_path=None):
    """
    Creates a function that can be progressively enhanced over time.
    
    Args:
        base_function (function): Initial function implementation
        version (str): Initial function version
        enhancement_path (dict, optional): Planned enhancement stages
        
    Returns:
        function: Progressive function with enhancement capabilities
    """
    #H->AI::EnhancementSetup: (Prepare function for progressive enhancement)
    
    if enhancement_path is None:
        enhancement_path = {}
    
    # Create metadata to track function evolution
    metadata = {
        'name': base_function.__name__,
        'version': version,
        'history': [{
            'version': version,
            'timestamp': get_current_timestamp(),
            'description': 'Initial version'
        }],
        'enhancement_path': enhancement_path,
        'capability_flags': {},
        'deprecated_params': set(),
        'added_params': {},
        'usage_statistics': {}
    }
    
    def enhanced_function(*args, **kwargs):
        """
        Wrapper function that handles version compatibility and enhancements.
        """
        #H->AI::VersionHandling: (Apply appropriate version logic)
        
        # Track function usage
        track_function_usage(metadata, args, kwargs)
        
        # Handle deprecated parameters
        kwargs = handle_deprecated_parameters(kwargs, metadata['deprecated_params'], metadata['added_params'])
        
        # Apply active enhancements based on capability flags
        result = apply_enhancements(base_function, args, kwargs, metadata['capability_flags'])
        
        return result
    
    # Copy function metadata
    enhanced_function.__name__ = base_function.__name__
    enhanced_function.__doc__ = base_function.__doc__
    enhanced_function.__dict__.update(base_function.__dict__)
    
    # Add enhancement methods
    enhanced_function.get_version = lambda: metadata['version']
    enhanced_function.get_history = lambda: metadata['history']
    enhanced_function.get_enhancement_path = lambda: metadata['enhancement_path']
    enhanced_function.get_metadata = lambda: metadata
    enhanced_function.enable_capability = lambda capability: enable_capability(metadata, capability)
    enhanced_function.disable_capability = lambda capability: disable_capability(metadata, capability)
    enhanced_function.enhance = lambda enhancement, version=None: enhance_function(enhanced_function, metadata, enhancement, version)
    enhanced_function.deprecate_parameter = lambda param, replacement=None: deprecate_parameter(metadata, param, replacement)
    enhanced_function.add_parameter = lambda param, default=None: add_parameter(metadata, param, default)
    
    return enhanced_function


def track_function_usage(metadata, args, kwargs):
    """Records function usage patterns for analysis."""
    #H->AI::UsageTracking: (Monitor how the function is being used)
    
    timestamp = get_current_timestamp()
    
    if 'usage_count' not in metadata['usage_statistics']:
        metadata['usage_statistics']['usage_count'] = 0
        metadata['usage_statistics']['parameter_frequency'] = {}
        metadata['usage_statistics']['call_pattern'] = {}
    
    metadata['usage_statistics']['usage_count'] += 1
    
    # Track parameter usage
    for key, value in kwargs.items():
        if key not in metadata['usage_statistics']['parameter_frequency']:
            metadata['usage_statistics']['parameter_frequency'][key] = 0
        metadata['usage_statistics']['parameter_frequency'][key] += 1
    
    # Track call pattern (simplified)
    call_signature = str(len(args)) + ":" + ",".join(sorted(kwargs.keys()))
    if call_signature not in metadata['usage_statistics']['call_pattern']:
        metadata['usage_statistics']['call_pattern'][call_signature] = 0
    metadata['usage_statistics']['call_pattern'][call_signature] += 1


def handle_deprecated_parameters(kwargs, deprecated_params, added_params):
    """Handles backward compatibility for deprecated parameters."""
    #H->AI::DeprecationHandling: (Support deprecated parameters while encouraging new ones)
    
    new_kwargs = kwargs.copy()
    
    for old_param in deprecated_params:
        if old_param in kwargs:
            # Check if there's a replacement parameter
            replacement = added_params.get(old_param)
            if replacement and replacement not in kwargs:
                # Apply the old value to the new parameter
                new_kwargs[replacement] = kwargs[old_param]
            
            # Log usage of deprecated parameter (would be implementation-specific)
            # log_deprecated_usage(old_param, replacement)
    
    return new_kwargs


def apply_enhancements(base_function, args, kwargs, capability_flags):
    """
    Applies active enhancements to the function execution.
    
    Args:
        base_function (function): Base function
        args (tuple): Positional arguments
        kwargs (dict): Keyword arguments
        capability_flags (dict): Active enhancement capabilities
        
    Returns:
        any: Function result with enhancements applied
    """
    #H->AI::ApplyEnhancements: (Execute function with enabled enhancements)
    
    # Pre-processing enhancements
    if capability_flags.get('input_validation', False):
        args, kwargs = apply_input_validation(args, kwargs)
    
    if capability_flags.get('input_enrichment', False):
        args, kwargs = apply_input_enrichment(args, kwargs)
    
    # Execute base function
    result = base_function(*args, **kwargs)
    
    # Post-processing enhancements
    if capability_flags.get('result_enrichment', False):
        result = apply_result_enrichment(result)
    
    if capability_flags.get('performance_metrics', False):
        # Would record execution metrics in a real implementation
        pass
    
    return result


def enable_capability(metadata, capability):
    """Enables a specific capability enhancement."""
    #H->AI::CapabilityManagement: (Enable specific enhancement features)
    
    metadata['capability_flags'][capability] = True
    
    # Record the change in history
    metadata['history'].append({
        'version': metadata['version'],
        'timestamp': get_current_timestamp(),
        'description': f'Enabled capability: {capability}'
    })
    
    return True


def disable_capability(metadata, capability):
    """Disables a specific capability enhancement."""
    #H->AI::CapabilityManagement: (Disable specific enhancement features)
    
    if capability in metadata['capability_flags']:
        metadata['capability_flags'][capability] = False
        
        # Record the change in history
        metadata['history'].append({
            'version': metadata['version'],
            'timestamp': get_current_timestamp(),
            'description': f'Disabled capability: {capability}'
        })
        
        return True
    
    return False


def enhance_function(function, metadata, enhancement, new_version=None):
    """
    Applies a new enhancement to the function.
    
    Args:
        function (function): Function to enhance
        metadata (dict): Function metadata
        enhancement (function): Enhancement to apply
        new_version (str, optional): New version number
        
    Returns:
        function: Enhanced function
    """
    #H->AI::FunctionEvolution: (Apply new enhancement to function)
    
    # Update version if provided
    if new_version:
        metadata['version'] = new_version
    
    # Apply the enhancement
    enhanced_fn = enhancement(function)
    
    # Record the enhancement in history
    metadata['history'].append({
        'version': metadata['version'],
        'timestamp': get_current_timestamp(),
        'description': f'Applied enhancement: {enhancement.__name__}'
    })
    
    return enhanced_fn


def deprecate_parameter(metadata, param, replacement=None):
    """
    Marks a parameter as deprecated while maintaining backward compatibility.
    
    Args:
        metadata (dict): Function metadata
        param (str): Parameter to deprecate
        replacement (str, optional): Replacement parameter
        
    Returns:
        bool: Success
    """
    #H->AI::ParameterEvolution: (Safely deprecate parameters)
    
    metadata['deprecated_params'].add(param)
    
    if replacement:
        metadata['added_params'][param] = replacement
    
    # Record the change in history
    metadata['history'].append({
        'version': metadata['version'],
        'timestamp': get_current_timestamp(),
        'description': f'Deprecated parameter: {param}' + 
                       (f' (replaced by {replacement})' if replacement else '')
    })
    
    return True


def add_parameter(metadata, param, default=None):
    """
    Adds a new parameter to the function with a default value for backward compatibility.
    
    Args:
        metadata (dict): Function metadata
        param (str): Parameter to add
        default (any, optional): Default value
        
    Returns:
        bool: Success
    """
    #H->AI::ParameterEvolution: (Add new parameters with defaults)
    
    # Record the new parameter
    metadata['added_params'][param] = default
    
    # Record the change in history
    metadata['history'].append({
        'version': metadata['version'],
        'timestamp': get_current_timestamp(),
        'description': f'Added parameter: {param} (default: {default})'
    })
    
    return True


def get_current_timestamp():
    """Gets current timestamp for versioning."""
    #H->AI::TimeTracking: (Record timestamp for version history)
    
    # In real implementation, would return actual timestamp
    return "2025-05-17T12:34:56Z"

Example Scenarios

Scenario 1: Text Analysis Function with Progressive Capability Enhancement

python

def basic_text_analyzer(text, analysis_type="sentiment"):
    """
    Analyzes text for basic linguistic and semantic features.
    
    Args:
        text (str): Text to analyze
        analysis_type (str): Type of analysis to perform (sentiment, entities)
        
    Returns:
        dict: Analysis results
    """
    #H->AI::BasicAnalysis: (Perform simple text analysis)
    
    results = {
        "text_length": len(text),
        "word_count": len(text.split())
    }
    
    if analysis_type == "sentiment":
        # Basic sentiment analysis (simplified for example)
        positive_words = ["good", "great", "excellent", "happy", "positive"]
        negative_words = ["bad", "terrible", "awful", "sad", "negative"]
        
        sentiment_score = 0
        lower_text = text.lower()
        
        for word in positive_words:
            if word in lower_text:
                sentiment_score += 1
        
        for word in negative_words:
            if word in lower_text:
                sentiment_score -= 1
        
        results["sentiment"] = {
            "score": sentiment_score,
            "classification": "positive" if sentiment_score > 0 else "negative" if sentiment_score < 0 else "neutral"
        }
    
    elif analysis_type == "entities":
        # Basic entity extraction (simplified)
        results["entities"] = []
        
        # Very simple person detection (names starting with capital letters)
        import re
        potential_names = re.findall(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', text)
        for name in potential_names:
            results["entities"].append({"type": "person", "text": name})
    
    return results

# Create a progressive function from the basic analyzer
analyzer = create_progressive_function(basic_text_analyzer, version="1.0", 
                                       enhancement_path={
                                           "1.1": "Add language detection",
                                           "1.2": "Add readability metrics",
                                           "1.5": "Add topic classification",
                                           "2.0": "Comprehensive semantic analysis"
                                       })

# Define an enhancement for language detection
def add_language_detection(function):
    """Enhancement that adds language detection capability."""
    
    def enhanced_function(text, analysis_type="sentiment", detect_language=False):
        """
        Enhanced text analyzer with language detection.
        
        Args:
            text (str): Text to analyze
            analysis_type (str): Type of analysis to perform
            detect_language (bool): Whether to detect language
            
        Returns:
            dict: Analysis results with language information
        """
        # Call the original function
        results = function(text, analysis_type)
        
        # Add language detection if requested
        if detect_language:
            # Simple language detection (simplified for example)
            # In a real implementation, would use a proper language detection library
            common_words = {
                "en": ["the", "and", "is", "in", "to", "it", "of"],
                "es": ["el", "la", "es", "en", "y", "de", "que"],
                "fr": ["le", "la", "est", "en", "et", "de", "qui"]
            }
            
            text_lower = text.lower()
            words = set(re.findall(r'\b\w+\b', text_lower))
            
            language_scores = {}
            for lang, lang_words in common_words.items():
                matches = sum(1 for word in lang_words if word in words)
                language_scores[lang] = matches / len(lang_words)
            
            detected_language = max(language_scores.items(), key=lambda x: x[1])
            results["language"] = {
                "detected": detected_language[0],
                "confidence": detected_language[1]
            }
        
        return results
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Apply the enhancement to the progressive function
analyzer.enhance(add_language_detection, "1.1")

# Example usage after enhancement:
# result = analyzer("This is a great product, I'm very happy with it!", detect_language=True)
# This works despite the detect_language parameter not being in the original function

# Define another enhancement for readability metrics
def add_readability_metrics(function):
    """Enhancement that adds readability analysis capability."""
    
    def enhanced_function(text, analysis_type="sentiment", detect_language=False, analyze_readability=False):
        """
        Enhanced text analyzer with readability metrics.
        
        Args:
            text (str): Text to analyze
            analysis_type (str): Type of analysis to perform
            detect_language (bool): Whether to detect language
            analyze_readability (bool): Whether to analyze readability
            
        Returns:
            dict: Analysis results with readability metrics
        """
        # Call the previous version
        results = function(text, analysis_type, detect_language)
        
        # Add readability analysis if requested
        if analyze_readability:
            # Simple readability analysis (simplified for example)
            sentences = len(re.findall(r'[.!?]+', text)) or 1
            words = len(text.split())
            syllable_count = count_syllables(text)  # Would be implemented
            
            # Calculate Flesch Reading Ease (simplified)
            flesch_score = 206.835 - (1.015 * (words / sentences)) - (84.6 * (syllable_count / words))
            
            results["readability"] = {
                "sentences": sentences,
                "words": words,
                "syllables": syllable_count,
                "flesch_reading_ease": round(flesch_score, 2),
                "grade_level": "Easy" if flesch_score > 80 else "Standard" if flesch_score > 60 else "Difficult"
            }
        
        return results
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Function for counting syllables (simplified)
def count_syllables(text):
    """Counts syllables in text (very simplified)."""
    text = text.lower()
    words = text.split()
    
    syllable_count = 0
    for word in words:
        # Count vowels as a simple approximation of syllables
        vowels = 'aeiouy'
        count = 0
        previous_was_vowel = False
        
        for char in word:
            if char in vowels:
                if not previous_was_vowel:
                    count += 1
                previous_was_vowel = True
            else:
                previous_was_vowel = False
        
        # Every word has at least one syllable
        syllable_count += max(1, count)
    
    return syllable_count

# Apply the readability enhancement
analyzer.enhance(add_readability_metrics, "1.2")

# Enable specific capabilities
analyzer.enable_capability('input_validation')
analyzer.enable_capability('performance_metrics')

# Example usage after multiple enhancements:
# result = analyzer(
#     "This is a great product, I'm very happy with it!",
#     detect_language=True,
#     analyze_readability=True
# )

Scenario 2: Data Processing Pipeline with Progressive Performance Improvements

python

def basic_data_processor(data, operations=None):
    """
    Processes data through a sequence of operations.
    
    Args:
        data (list): Data to process
        operations (list): Sequence of operations to apply
        
    Returns:
        list: Processed data
    """
    if operations is None:
        operations = ["filter_nulls", "normalize"]
    
    result = data.copy()
    
    for operation in operations:
        if operation == "filter_nulls":
            result = [item for item in result if item is not None]
        elif operation == "normalize":
            # Simple normalization (scaling to 0-1)
            if result:
                min_val = min(result)
                max_val = max(result)
                if max_val > min_val:
                    result = [(x - min_val) / (max_val - min_val) for x in result]
        elif operation == "sort":
            result = sorted(result)
    
    return result

# Create progressive function
processor = create_progressive_function(basic_data_processor, version="1.0")

# Define performance enhancement
def add_parallel_processing(function):
    """Enhancement that adds parallel processing capability."""
    
    def enhanced_function(data, operations=None, use_parallel=False, chunk_size=1000):
        """
        Enhanced data processor with parallel processing option.
        
        Args:
            data (list): Data to process
            operations (list): Sequence of operations to apply
            use_parallel (bool): Whether to use parallel processing
            chunk_size (int): Size of chunks for parallel processing
            
        Returns:
            list: Processed data
        """
        # Use parallel processing if requested and data is large enough
        if use_parallel and len(data) > chunk_size:
            # In a real implementation, would use proper parallel processing
            # Simplified example:
            chunks = [data[i:i+chunk_size] for i in range(0, len(data), chunk_size)]
            processed_chunks = []
            
            for chunk in chunks:
                # Process each chunk using the original function
                processed_chunk = function(chunk, operations)
                processed_chunks.append(processed_chunk)
            
            # Combine results (may need operation-specific combination logic)
            result = []
            for chunk in processed_chunks:
                result.extend(chunk)
            
            return result
        else:
            # Use original function for small data or when parallel not requested
            return function(data, operations)
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Apply the enhancement
processor.enhance(add_parallel_processing, "1.1")

# Define caching enhancement
def add_result_caching(function):
    """Enhancement that adds result caching for repeated operations."""
    
    # Cache for storing previous results
    cache = {}
    
    def enhanced_function(data, operations=None, use_parallel=False, chunk_size=1000, use_cache=True):
        """
        Enhanced data processor with result caching.
        
        Args:
            data (list): Data to process
            operations (list): Sequence of operations to apply
            use_parallel (bool): Whether to use parallel processing
            chunk_size (int): Size of chunks for parallel processing
            use_cache (bool): Whether to use result caching
            
        Returns:
            list: Processed data
        """
        if operations is None:
            operations = ["filter_nulls", "normalize"]
        
        # Create cache key based on data and operations
        if use_cache:
            # For simplicity, using string representation as cache key
            # In production, would use a more efficient hashing mechanism
            cache_key = str(hash(str(data))) + "_" + str(hash(str(operations)))
            
            if cache_key in cache:
                # Return cached result
                return cache[cache_key]
        
        # Process data using previous version
        result = function(data, operations, use_parallel, chunk_size)
        
        # Cache result if caching is enabled
        if use_cache:
            cache[cache_key] = result
        
        return result
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Apply the caching enhancement
processor.enhance(add_result_caching, "1.2")

# Example usage after enhancements:
# large_dataset = [random.random() for _ in range(10000)]
# result = processor(large_dataset, 
#                    operations=["filter_nulls", "normalize", "sort"],
#                    use_parallel=True, 
#                    chunk_size=1000,
#                    use_cache=True)

Scenario 3: Content Generator with Progressive Interface Evolution

python

def basic_content_generator(topic, length="medium", format="article"):
    """
    Generates content on a specified topic.
    
    Args:
        topic (str): Topic to generate content about
        length (str): Content length (short, medium, long)
        format (str): Content format (article, blog, social)
        
    Returns:
        str: Generated content
    """
    # Simplified implementation for illustration
    content = f"# {topic.title()}\n\n"
    
    if length == "short":
        paragraphs = 2
    elif length == "medium":
        paragraphs = 5
    else:  # long
        paragraphs = 10
    
    for i in range(paragraphs):
        content += f"Paragraph {i+1} about {topic}.\n\n"
    
    return content

# Create progressive function
generator = create_progressive_function(basic_content_generator, version="1.0")

# Define interface evolution
def modernize_interface(function):
    """Updates function interface to use a more modern structure."""
    
    def enhanced_function(topic=None, **kwargs):
        """
        Enhanced content generator with modern parameter interface.
        
        Args:
            topic (str, optional): Topic to generate content about
            **kwargs: Modern parameter interface
                content_type (str): Type of content (article, blog, social)
                word_count (int): Target word count
                style (str): Writing style
                ...
                
        Returns:
            str: Generated content
        """
        # Map modern parameters to legacy parameters
        if 'content_type' in kwargs and 'format' not in kwargs:
            kwargs['format'] = kwargs.pop('content_type')
        
        if 'word_count' in kwargs and 'length' not in kwargs:
            word_count = kwargs.pop('word_count')
            if word_count < 200:
                kwargs['length'] = 'short'
            elif word_count < 500:
                kwargs['length'] = 'medium'
            else:
                kwargs['length'] = 'long'
        
        # Call original function with mapped parameters
        return function(topic, **kwargs)
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Apply the interface evolution
generator.enhance(modernize_interface, "2.0")
generator.deprecate_parameter('format', 'content_type')
generator.deprecate_parameter('length', 'word_count')

# Define feature enhancement
def add_style_customization(function):
    """Adds style customization capabilities."""
    
    def enhanced_function(topic=None, **kwargs):
        """
        Enhanced content generator with style customization.
        
        Args:
            topic (str, optional): Topic to generate content about
            **kwargs: Parameter interface
                content_type (str): Type of content (article, blog, social)
                word_count (int): Target word count
                style (str): Writing style (formal, casual, technical)
                tone (str): Content tone (informative, persuasive, entertaining)
                target_audience (str): Intended audience
                
        Returns:
            dict: Generated content with metadata
        """
        # Extract style parameters
        style = kwargs.pop('style', 'neutral')
        tone = kwargs.pop('tone', 'informative')
        target_audience = kwargs.pop('target_audience', 'general')
        
        # Generate base content using previous version
        content = function(topic, **kwargs)
        
        # Enhance content with style customization (simplified)
        if style == 'formal':
            content = content.replace('about', 'regarding')
        elif style == 'casual':
            content = content.replace('Paragraph', 'Let\'s talk')
        
        # Add metadata to result
        result = {
            'content': content,
            'metadata': {
                'topic': topic,
                'style': style,
                'tone': tone,
                'target_audience': target_audience,
                'word_count': len(content.split()),
                'reading_time': len(content.split()) // 200  # words per minute
            }
        }
        
        return result
    
    # Update function metadata
    enhanced_function.__name__ = function.__name__
    enhanced_function.__doc__ = enhanced_function.__doc__
    
    return enhanced_function

# Apply the style enhancement
generator.enhance(add_style_customization, "2.1")

# Example usage after enhancements:
# result = generator(
#     topic="Artificial Intelligence Ethics",
#     content_type="blog",  # New parameter replacing 'format'
#     word_count=400,       # New parameter replacing 'length'
#     style="formal",       # New parameter
#     tone="persuasive",    # New parameter
#     target_audience="technical"  # New parameter
# )

Benefits of Progressive Function Enhancement

Future-Proofing: Functions adapt to changing requirements without breaking existing code.
Backward Compatibility: New features and improvements don't invalidate existing implementations.
Incremental Improvement: Functions become more powerful and efficient over time through gradual evolution.
Testability: Each enhancement can be tested independently, reducing the risk of regressions.
Transparency: Version history and capability tracking provide clear documentation of function evolution.
Graceful Deprecation: Old parameters and patterns can be phased out gradually rather than abruptly.
Usage-Driven Improvement: Enhancement paths can be informed by actual usage patterns.
Feature Toggles: Capabilities can be enabled or disabled without modifying function implementation.
Reduced Duplication: Avoids the need for multiple similar functions with slightly different capabilities.
Knowledge Preservation: Function history captures the reasoning behind changes and improvements.

Advanced Applications

Automatic Enhancement Based on Usage Patterns

python

def create_self_enhancing_function(base_function, learning_threshold=100):
    """
    Creates a function that automatically enhances itself based on usage patterns.
    
    Args:
        base_function (function): Initial function implementation
        learning_threshold (int): Number of calls before analyzing patterns
        
    Returns:
        function: Self-enhancing function
    """
    # Create progressive function
    prog_function = create_progressive_function(base_function)
    
    # Track call count for learning trigger
    call_count = [0]
    
    def self_enhancing_wrapper(*args, **kwargs):
        """Wrapper that enables self-enhancement."""
        # Increment call count
        call_count[0] += 1
        
        # Check if we should analyze patterns and enhance
        if call_count[0] % learning_threshold == 0:
            # Get usage statistics
            metadata = prog_function.get_metadata()
            stats = metadata['usage_statistics']
            
            # Analyze patterns and generate enhancements
            enhancements = generate_enhancements_from_usage(stats)
            
            # Apply any identified enhancements
            for enhancement in enhancements:
                prog_function.enhance(enhancement)
        
        # Call the progressive function
        return prog_function(*args, **kwargs)
    
    # Copy function metadata
    self_enhancing_wrapper.__name__ = base_function.__name__
    self_enhancing_wrapper.__doc__ = base_function.__doc__
    
    return self_enhancing_wrapper


def generate_enhancements_from_usage(usage_stats):
    """
    Analyzes usage statistics to generate potential enhancements.
    
    Args:
        usage_stats (dict): Function usage statistics
        
    Returns:
        list: Enhancement functions
    """
    enhancements = []
    
    # Example enhancement: Add default values for commonly used parameters
    if 'parameter_frequency' in usage_stats:
        param_freq = usage_stats['parameter_frequency']
        
        for param, freq in param_freq.items():
            # If parameter is used in more than 80% of calls
            if freq / usage_stats['usage_count'] > 0.8:
                # Create an enhancement to add a default value
                def add_default_for_common_param(function):
                    """Adds default value for commonly used parameter."""
                    # Implementation would determine appropriate default value
                    # from usage patterns
                    return function
                
                enhancements.append(add_default_for_common_param)
    
    # Example enhancement: Optimize for common call patterns
    if 'call_pattern' in usage_stats:
        # Find the most common call pattern
        most_common_pattern = max(usage_stats['call_pattern'].items(), 
                                 key=lambda x: x[1])[0]
        
        # Create an enhancement to optimize for this pattern
        def optimize_for_common_pattern(function):
            """Optimizes function for the most common call pattern."""
            # Implementation would optimize execution path for this pattern
            return function
        
        enhancements.append(optimize_for_common_pattern)
    
    return enhancements

Progressive A/B Testing of Function Implementations

python

def create_ab_testing_function(base_function, variant_functions=None, test_allocation=0.2):
    """
    Creates a function that conducts A/B testing between implementation variants.
    
    Args:
        base_function (function): Primary function implementation (A)
        variant_functions (list): Alternative implementations to test (B, C, etc.)
        test_allocation (float): Proportion of calls to allocate to variants
        
    Returns:
        function: A/B testing function
    """
    if variant_functions is None:
        variant_functions = []
    
    # Create progressive function
    prog_function = create_progressive_function(base_function)
    
    # Test results storage
    test_results = {
        'base': {'calls': 0, 'errors': 0, 'duration': 0},
    }
    
    for i, _ in enumerate(variant_functions):
        test_results[f'variant_{i+1}'] = {'calls': 0, 'errors': 0, 'duration': 0}
    
    def ab_testing_wrapper(*args, **kwargs):
        """Wrapper that implements A/B testing."""
        import random
        import time
        
        # Decide which implementation to use
        if random.random() < test_allocation and variant_functions:
            # Use a variant
            variant_idx = random.randint(0, len(variant_functions) - 1)
            variant_key = f'variant_{variant_idx+1}'
            function_to_use = variant_functions[variant_idx]
        else:
            # Use the base implementation
            variant_key = 'base'
            function_to_use = prog_function
        
        # Execute and measure performance
        start_time = time.time()
        try:
            result = function_to_use(*args, **kwargs)
            test_results[variant_key]['calls'] += 1
            test_results[variant_key]['duration'] += time.time() - start_time
            return result
        except Exception as e:
            test_results[variant_key]['calls'] += 1
            test_results[variant_key]['errors'] += 1
            test_results[variant_key]['duration'] += time.time() - start_time
            raise e
    
    # Add methods for managing the test
    ab_testing_wrapper.get_test_results = lambda: test_results
    ab_testing_wrapper.reset_test = lambda: test_results.clear()
    ab_testing_wrapper.add_variant = lambda variant: variant_functions.append(variant)
    ab_testing_wrapper.promote_best_variant = lambda: promote_best_variant(prog_function, variant_functions, test_results)
    
    # Copy function metadata
    ab_testing_wrapper.__name__ = base_function.__name__
    ab_testing_wrapper.__doc__ = base_function.__doc__
    
    return ab_testing_wrapper


def promote_best_variant(base_function, variants, results):
    """
    Promotes the best performing variant to become the primary implementation.
    
    Args:
        base_function (function): Current primary implementation
        variants (list): Alternative implementations
        results (dict): Test results
        
    Returns:
        function: New primary implementation
    """
    # Calculate performance metrics
    performance = {}
    
    for key, data in results.items():
        if data['calls'] == 0:
            continue
        
        error_rate = data['errors'] / data['calls']
        avg_duration = data['duration'] / data['calls']
        
        # Lower is better for both metrics
        performance[key] = (error_rate, avg_duration)
    
    # Find the best performer (lowest error rate, then lowest duration)
    if not performance:
        return base_function
    
    best_key = min(performance.keys(), key=lambda k: performance[k])
    
    # If the best is not the base implementation, promote it
    if best_key != 'base':
        variant_idx = int(best_key.split('_')[1]) - 1
        return variants[variant_idx]
    
    return base_function

Progressive Specialization for Different Use Cases

python

def create_specializing_function(base_function, specialization_criteria=None):
    """
    Creates a function that progressively specializes for different use cases.
    
    Args:
        base_function (function): General-purpose implementation
        specialization_criteria (dict): Criteria for identifying use cases
        
    Returns:
        function: Use case-specializing function
    """
    if specialization_criteria is None:
        specialization_criteria = {}
    
    # Create progressive function
    prog_function = create_progressive_function(base_function)
    
    # Storage for specialized implementations
    specialized_implementations = {}
    
    def specializing_wrapper(*args, **kwargs):
        """Wrapper that applies specialized implementations for recognized use cases."""
        # Identify the use case based on arguments
        use_case = identify_use_case(args, kwargs, specialization_criteria)
        
        # If we have a specialized implementation for this use case, use it
        if use_case and use_case in specialized_implementations:
            return specialized_implementations[use_case](*args, **kwargs)
        
        # Otherwise, use the progressive function
        return prog_function(*args, **kwargs)
    
    # Add methods for managing specializations
    specializing_wrapper.add_specialization = lambda use_case, implementation: specialized_implementations.update({use_case: implementation})
    specializing_wrapper.get_specializations = lambda: list(specialized_implementations.keys())
    specializing_wrapper.remove_specialization = lambda use_case: specialized_implementations.pop(use_case, None)
    
    # Copy function metadata
    specializing_wrapper.__name__ = base_function.__name__
    specializing_wrapper.__doc__ = base_function.__doc__
    
    return specializing_wrapper


def identify_use_case(args, kwargs, criteria):
    """
    Identifies the use case based on function arguments.
    
    Args:
        args (tuple): Positional arguments
        kwargs (dict): Keyword arguments
        criteria (dict): Criteria for identifying use cases
        
    Returns:
        str: Identified use case or None
    """
    # For each use case, check if criteria match
    for use_case, use_case_criteria in criteria.items():
        matches = True
        
        for key, value in use_case_criteria.items():
            if key in kwargs and kwargs[key] != value:
                matches = False
                break
        
        if matches:
            return use_case
    
    return None

Integration with CRAFT Principles

Progressive Function Enhancement aligns with several CRAFT principles:

Structure Enables Freedom: By establishing a clear evolution path, functions can grow and adapt while maintaining predictable interfaces.
Cumulative Intelligence: Functions preserve knowledge and improvements across iterations, building on previous insights.
Human-AI Partnership: Progressive enhancement facilitates evolving collaboration as AI capabilities and user needs change.
Accessibility Through Familiarity: Functions maintain familiar interfaces while gaining power, making enhancements accessible to existing users.
Conservation of Tokens: Functions can become more token-efficient without breaking backward compatibility.

Implementation Recommendations

Start with Core Functionality: Begin by implementing essential functionality that serves the most common use cases.
Plan Enhancement Paths: Define potential enhancement paths upfront to guide future development.
Preserve Backward Compatibility: Always ensure new enhancements maintain compatibility with existing code.
Document Evolution History: Maintain clear records of function changes, versions, and rationale.
Implement Feature Flags: Use capability flags to allow selective enabling of enhancements.
Track Usage Patterns: Monitor how functions are used to inform enhancement decisions.
Test Each Enhancement: Thoroughly test each enhancement to ensure it works correctly in isolation and in combination with others.
Apply Gradual Deprecation: Use deprecation warnings and transition periods for interface changes.
Create Enhancement Packages: Group related enhancements into coherent packages with version numbers.
Limit Enhancement Complexity: Keep each enhancement focused and simple to reduce integration issues.

Practical Integration with Other CRAFT Functions

python

# Integration with Context-Aware Functions
def integrate_progressive_and_context_aware(base_function, context_variables=None):
    """
    Creates a function that combines progressive enhancement with context awareness.
    
    Args:
        base_function (function): Base function implementation
        context_variables (list): Context variables to track
        
    Returns:
        function: Progressive and context-aware function
    """
    if context_variables is None:
        context_variables = []
    
    # Create progressive function
    prog_function = create_progressive_function(base_function)
    
    # Create context tracking
    context_tracker = create_context_tracker(context_variables)
    
    def integrated_function(*args, **kwargs):
        """Function with both progressive enhancement and context awareness."""
        # Extract context from arguments
        context = kwargs.pop('context', {})
        
        # Update context based on current call
        updated_context = context_tracker.update(context, args, kwargs)
        
        # Execute function with current context
        result = prog_function(*args, **kwargs)
        
        # Update context based on result
        final_context = context_tracker.update(updated_context, result)
        
        # Package result with updated context
        return {
            'result': result,
            'context': final_context
        }
    
    # Add progressive function methods
    integrated_function.enhance = prog_function.enhance
    integrated_function.get_version = prog_function.get_version
    integrated_function.get_history = prog_function.get_history
    integrated_function.enable_capability = prog_function.enable_capability
    
    # Add context methods
    integrated_function.get_context_variables = lambda: context_variables
    integrated_function.add_context_variable = lambda var: context_variables.append(var)
    
    return integrated_function


# Integration with Natural Language Parameter Handling
def integrate_progressive_and_nl_parameters(base_function, formal_parameters=None):
    """
    Creates a function that combines progressive enhancement with natural language parameters.
    
    Args:
        base_function (function): Base function implementation
        formal_parameters (dict): Parameter definitions
        
    Returns:
        function: Progressive function with natural language parameters
    """
    if formal_parameters is None:
        formal_parameters = {}
    
    # Create progressive function
    prog_function = create_progressive_function(base_function)
    
    # Create parameter handler
    param_handler = create_nlp_parameter_handler(base_function.__name__, formal_parameters)
    
    def integrated_function(natural_language_input=None, *args, **kwargs):
        """Function with both progressive enhancement and NL parameters."""
        # If natural language input is provided, extract parameters
        if natural_language_input is not None and isinstance(natural_language_input, str):
            extracted_params = param_handler(natural_language_input)
            kwargs.update(extracted_params)
            
            # Execute with extracted parameters
            return prog_function(*args, **kwargs)
        else:
            # Use as regular function
            if natural_language_input is not None:
                args = (natural_language_input,) + args
            return prog_function(*args, **kwargs)
    
    # Add progressive function methods
    integrated_function.enhance = prog_function.enhance
    integrated_function.get_version = prog_function.get_version
    integrated_function.get_history = prog_function.get_history
    integrated_function.enable_capability = prog_function.enable_capability
    
    # Add NL parameter methods
    integrated_function.add_parameter = lambda param, default=None: add_parameter(prog_function.get_metadata(), param, default)
    integrated_function.deprecate_parameter = lambda param, replacement=None: deprecate_parameter(prog_function.get_metadata(), param, replacement)
    
    return integrated_function

Real-World Application Example

python

# Example: Customer Support Response Generator with Progressive Enhancement

# Base function for generating support responses
def basic_support_response(issue_description, issue_type="general", customer_info=None):
    """
    Generates a basic customer support response.
    
    Args:
        issue_description (str): Description of the customer's issue
        issue_type (str): Category of the issue (general, technical, billing)
        customer_info (dict, optional): Customer information
        
    Returns:
        str: Support response
    """
    # Basic implementation (simplified)
    greeting = "Dear Customer,"
    if customer_info and "name" in customer_info:
        greeting = f"Dear {customer_info['name']},"
    
    response_body = f"Thank you for contacting our support team about your {issue_type} issue.\n\n"
    response_body += f"Regarding your concern: \"{issue_description}\"\n\n"
    
    if issue_type == "technical":
        response_body += "Our technical team is looking into this matter. Have you tried restarting the application?\n\n"
    elif issue_type == "billing":
        response_body += "Our billing department will review your account and address any discrepancies.\n\n"
    else:
        response_body += "We're reviewing your request and will get back to you shortly.\n\n"
    
    closing = "Best regards,\nCustomer Support Team"
    
    return f"{greeting}\n\n{response_body}{closing}"

# Create progressive function
support_response_gen = create_progressive_function(
    basic_support_response, 
    version="1.0",
    enhancement_path={
        "1.1": "Personalization enhancements",
        "1.2": "Knowledge base integration",
        "2.0": "Multi-channel formatting",
        "2.1": "Tone and sentiment awareness",
        "3.0": "Response templates and macros"
    }
)

# Define personalization enhancement
def add_personalization(function):
    """Enhances response with deeper personalization."""
    
    def enhanced_function(issue_description, issue_type="general", customer_info=None, 
                          personalization_level="standard"):
        """
        Enhanced support response with personalization options.
        
        Args:
            issue_description (str): Description of the customer's issue
            issue_type (str): Category of the issue
            customer_info (dict, optional): Customer information
            personalization_level (str): Level of personalization (minimal, standard, deep)
            
        Returns:
            str: Personalized support response
        """
        # Get base response
        response = function(issue_description, issue_type, customer_info)
        
        # Apply additional personalization if customer info available
        if customer_info and personalization_level != "minimal":
            # Standard personalization
            if "account_type" in customer_info:
                response = response.replace(
                    "Thank you for contacting our support team",
                    f"Thank you for contacting our support team as a valued {customer_info['account_type']} customer"
                )
            
            # Deep personalization
            if personalization_level == "deep":
                if "purchase_history" in customer_info and customer_info["purchase_history"]:
                    recent_purchase = customer_info["purchase_history"][0]
                    response += f"\n\nWe hope you're enjoying your recent purchase of {recent_purchase}."
                
                if "support_history" in customer_info and customer_info["support_history"]:
                    response += "\n\nWe appreciate your patience as we've worked together on previous support requests."
        
        return response
    
    return enhanced_function

# Apply personalization enhancement
support_response_gen.enhance(add_personalization, "1.1")

# Define knowledge base integration
def add_knowledge_base(function):
    """Integrates knowledge base articles into responses."""
    
    def enhanced_function(issue_description, issue_type="general", customer_info=None, 
                          personalization_level="standard", include_kb_articles=True,
                          max_articles=2):
        """
        Enhanced support response with knowledge base integration.
        
        Args:
            issue_description (str): Description of the customer's issue
            issue_type (str): Category of the issue
            customer_info (dict, optional): Customer information
            personalization_level (str): Level of personalization
            include_kb_articles (bool): Whether to include knowledge base articles
            max_articles (int): Maximum number of articles to include
            
        Returns:
            str: Support response with knowledge articles
        """
        # Get enhanced response from previous version
        response = function(issue_description, issue_type, customer_info, personalization_level)
        
        # Add knowledge base articles if requested
        if include_kb_articles:
            # In real implementation, would search knowledge base for relevant articles
            # Simplified example:
            kb_section = "\n\nYou may find these resources helpful:\n"
            
            if issue_type == "technical":
                kb_section += "- Troubleshooting Guide: https://support.example.com/troubleshooting\n"
                kb_section += "- Common Technical Issues FAQ: https://support.example.com/tech-faq\n"
            elif issue_type == "billing":
                kb_section += "- Billing FAQ: https://support.example.com/billing-faq\n"
                kb_section += "- Payment Methods Guide: https://support.example.com/payment-methods\n"
            else:
                kb_section += "- General Support Resources: https://support.example.com/general\n"
            
            response = response.replace("Best regards,", kb_section + "\nBest regards,")
        
        return response
    
    return enhanced_function

# Apply knowledge base enhancement
support_response_gen.enhance(add_knowledge_base, "1.2")

# Define multi-channel formatting
def add_multichannel_support(function):
    """Adds support for different communication channels."""
    
    def enhanced_function(issue_description, issue_type="general", customer_info=None, 
                          personalization_level="standard", include_kb_articles=True,
                          max_articles=2, channel="email"):
        """
        Enhanced support response with multi-channel formatting.
        
        Args:
            issue_description (str): Description of the customer's issue
            issue_type (str): Category of the issue
            customer_info (dict, optional): Customer information
            personalization_level (str): Level of personalization
            include_kb_articles (bool): Whether to include knowledge base articles
            max_articles (int): Maximum number of articles to include
            channel (str): Communication channel (email, chat, sms, social)
            
        Returns:
            str or dict: Formatted support response
        """
        # Get the enhanced response from the previous version
        response = function(issue_description, issue_type, customer_info, 
                           personalization_level, include_kb_articles, max_articles)
        
        # Format based on channel
        if channel == "email":
            # Email is the default format, no changes needed
            return response
        
        elif channel == "chat":
            # Simplify for chat
            response = response.replace("Dear ", "Hi ")
            response = response.replace("\n\n", "\n")
            response = response.replace("Best regards,\nCustomer Support Team", "- Support Team")
            return response
        
        elif channel == "sms":
            # Create a very concise version for SMS
            lines = response.split("\n")
            greeting = lines[0].replace("Dear ", "Hi ")
            
            # Extract main content
            main_content = ""
            for line in lines:
                if "your concern" in line:
                    main_content = "We received your request about " + line.split("\"")[1]
                    break
            
            # Create SMS version
            sms = f"{greeting} {main_content} We'll follow up soon. Support Team"
            return sms
        
        elif channel == "social":
            # For social media, return structured data
            return {
                "public_reply": f"Thanks for reaching out about your {issue_type} issue. We're looking into this!",
                "private_message": response
            }
        
        return response
    
    return enhanced_function

# Apply multi-channel enhancement
support_response_gen.enhance(add_multichannel_support, "2.0")

# Enable specific capabilities
support_response_gen.enable_capability('input_validation')
support_response_gen.enable_capability('performance_metrics')

# Example usage:
# response = support_response_gen(
#     issue_description="I can't log into my account after the recent update",
#     issue_type="technical",
#     customer_info={
#         "name": "Sarah Johnson",
#         "account_type": "premium",
#         "purchase_history": ["Pro Plan Subscription", "Mobile App"],
#         "support_history": ["Password Reset (2 weeks ago)"]
#     },
#     personalization_level="deep",
#     include_kb_articles=True,
#     channel="chat"
# )

Progressive Function Enhancement creates a sustainable path for function evolution without disrupting existing implementations. This approach enables functions to grow more sophisticated, efficient, and versatile over time while maintaining compatibility across iterations. By planning enhancement paths and applying improvements incrementally, CRAFT functions can adapt to changing requirements and incorporate new capabilities with minimal friction.

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Great, please expand on Collaborative Function Building with details, benefits, explanations , examples and anything else that you think will be helpful.

Edit

Collaborative Function Building for CRAFT Framework

Document

# Collaborative Function Building for CRAFT Framework ## Core Concept Collaborative Function Building represents a transformative approach to function development within the CRAFT framework, enabling multiple stakeholders—both human and AI—to contr

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Continue

Edit

Collaborative Function Building for CRAFT Framework

Document ∙ Version 2

def process_feedback(learning_state, feedback): """Processes user feedback to inform function improvements.""" # Extract patterns from successful executions if feedback.get('rating', 0) >= learning_state['parameters']['feedback_threshold'

Collaborative Function Building for CRAFT Framework

Document ∙ Version 3

### Collaborative Skills Acquisition ```python def create_skill_learning_function(base_function, learning_parameters=None): """ Creates a function that improves through collaborative learning. Args: base_function (function):

Collaborative Function Building for CRAFT Framework

Document ∙ Version 4

### Cross-Organization Function Governance ```python def create_governed_function(base_function, governance_policies=None, stakeholder_groups=None): """ Creates a function with formal governance structures for cross-organizational use.

Collaborative Function Building for CRAFT Framework

Document ∙ Version 5

## Integration with CRAFT Principles Collaborative Function Building aligns with several CRAFT principles: 1. **Structure Enables Freedom**: By providing formalized mechanisms for collaboration, it creates the structure needed for multiple stakehol

Great! Let's continue with the final function type from the CRAFT framework: Function Libraries.

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yes, please do

Edit

Function Libraries for CRAFT Framework

Core Concept

Function Libraries represent a sophisticated approach to organizing, discovering, and using collections of related functions within the CRAFT framework. Rather than requiring users to repeatedly define similar functions or remember a large catalog of individual functions, Function Libraries provide a structured way to group, categorize, access, and extend sets of related functions.

This system transforms individual functions into cohesive, organized collections with consistent interfaces, standardized documentation, and intelligent discovery mechanisms. Function Libraries enable higher-level organization that makes the growing complexity of a CRAFT implementation manageable, improving both developer experience and system maintainability.

By categorizing functions by domain, purpose, or capability, libraries create intuitive "namespaces" that prevent collisions, clarify function relationships, and make the system more navigable. They also enable consistent patterns, shared infrastructure, and unified error handling across related functions.

Implementation Details

python

def create_function_library(library_name, description=None, categories=None, namespace=None):
    """
    Creates a function library for organizing related functions.
    
    Args:
        library_name (str): Name of the library
        description (str, optional): Library description
        categories (list, optional): Function categories within the library
        namespace (str, optional): Namespace prefix for functions
        
    Returns:
        dict: Function library object
    """
    #H->AI::LibraryCreation: (Create organized collection of related functions)
    
    # Set defaults
    if description is None:
        description = f"A collection of related functions for {library_name}"
    
    if categories is None:
        categories = ["general"]
    
    if namespace is None:
        namespace = library_name.lower().replace(" ", "_")
    
    # Initialize library structure
    library = {
        'name': library_name,
        'description': description,
        'namespace': namespace,
        'categories': categories,
        'functions': {},
        'metadata': {
            'created': get_current_timestamp(),
            'last_updated': get_current_timestamp(),
            'version': '0.1',
            'function_count': 0
        }
    }
    
    return library


def add_function_to_library(library, function, name=None, category="general", 
                           description=None, examples=None, aliases=None):
    """
    Adds a function to a function library.
    
    Args:
        library (dict): Function library
        function (function): Function to add
        name (str, optional): Function name (defaults to function.__name__)
        category (str): Function category within the library
        description (str, optional): Function description
        examples (list, optional): Usage examples
        aliases (list, optional): Alternative names for the function
        
    Returns:
        str: Fully qualified function name
    """
    #H->AI::LibraryAddition: (Add function to organized collection)
    
    # Use function's name if not provided
    if name is None:
        name = function.__name__
    
    # Generate fully qualified name with namespace
    qualified_name = f"{library['namespace']}.{name}"
    
    # Extract description from docstring if not provided
    if description is None and function.__doc__:
        description = function.__doc__.split('\n')[0].strip()
    elif description is None:
        description = f"Function {name} in the {library['name']} library"
    
    # Ensure category exists
    if category not in library['categories']:
        library['categories'].append(category)
    
    # Set up aliases
    if aliases is None:
        aliases = []
    
    # Create function entry
    function_entry = {
        'name': name,
        'qualified_name': qualified_name,
        'function': function,
        'category': category,
        'description': description,
        'examples': examples or [],
        'aliases': aliases,
        'added': get_current_timestamp(),
        'last_updated': get_current_timestamp(),
        'usage_count': 0
    }
    
    # Add to library
    library['functions'][qualified_name] = function_entry
    
    # Update library metadata
    library['metadata']['function_count'] += 1
    library['metadata']['last_updated'] = get_current_timestamp()
    
    return qualified_name


def get_function_from_library(library, function_name, allow_alias=True):
    """
    Retrieves a function from a library by name or alias.
    
    Args:
        library (dict): Function library
        function_name (str): Function name or alias
        allow_alias (bool): Whether to check aliases
        
    Returns:
        function: Retrieved function or None if not found
    """
    #H->AI::FunctionRetrieval: (Get function from library)
    
    # Check if full qualified name is provided
    if function_name in library['functions']:
        return library['functions'][function_name]['function']
    
    # Check if name without namespace is provided
    namespace_prefix = library['namespace'] + "."
    if not function_name.startswith(namespace_prefix):
        qualified_name = namespace_prefix + function_name
        if qualified_name in library['functions']:
            return library['functions'][qualified_name]['function']
    
    # Check aliases if allowed
    if allow_alias:
        for qualified_name, func_entry in library['functions'].items():
            if function_name in func_entry['aliases']:
                return func_entry['function']
    
    # Not found
    return None


def execute_library_function(library, function_name, *args, **kwargs):
    """
    Executes a function from the library.
    
    Args:
        library (dict): Function library
        function_name (str): Function name or alias
        *args: Positional arguments
        **kwargs: Keyword arguments
        
    Returns:
        any: Function result
    """
    #H->AI::FunctionExecution: (Run function from library)
    
    # Get the function
    func = get_function_from_library(library, function_name)
    
    if func is None:
        raise ValueError(f"Function '{function_name}' not found in library '{library['name']}'")
    
    # Update usage statistics
    qualified_name = function_name
    if function_name not in library['functions']:
        # Find the qualified name
        for name, entry in library['functions'].items():
            if entry['function'] == func:
                qualified_name = name
                break
    
    library['functions'][qualified_name]['usage_count'] += 1
    
    # Execute the function
    return func(*args, **kwargs)


def get_library_catalog(library, category=None, include_examples=False, format="dict"):
    """
    Gets a catalog of functions in the library.
    
    Args:
        library (dict): Function library
        category (str, optional): Filter by category
        include_examples (bool): Whether to include usage examples
        format (str): Output format (dict, table, markdown)
        
    Returns:
        any: Function catalog in requested format
    """
    #H->AI::LibraryCatalog: (Generate overview of library contents)
    
    # Filter functions by category if specified
    functions = library['functions'].values()
    if category:
        functions = [f for f in functions if f['category'] == category]
    
    # Build catalog based on format
    if format == "dict":
        catalog = {
            'library_name': library['name'],
            'description': library['description'],
            'function_count': library['metadata']['function_count'],
            'categories': library['categories'],
            'functions': []
        }
        
        for func in functions:
            func_entry = {
                'name': func['name'],
                'qualified_name': func['qualified_name'],
                'category': func['category'],
                'description': func['description'],
                'aliases': func['aliases']
            }
            
            if include_examples and func['examples']:
                func_entry['examples'] = func['examples']
            
            catalog['functions'].append(func_entry)
        
        return catalog
    
    elif format == "table":
        # Create a text-based table
        table = f"LIBRARY: {library['name']} ({library['description']})\n"
        table += f"Functions: {library['metadata']['function_count']}  |  Categories: {', '.join(library['categories'])}\n"
        table += "-" * 80 + "\n"
        table += f"{'NAME':<25} | {'CATEGORY':<15} | {'DESCRIPTION':<40}\n"
        table += "-" * 80 + "\n"
        
        for func in sorted(functions, key=lambda x: x['name']):
            table += f"{func['name']:<25} | {func['category']:<15} | {func['description'][:40]}\n"
        
        if include_examples:
            table += "\nEXAMPLES:\n"
            table += "=" * 80 + "\n"
            
            for func in functions:
                if func['examples']:
                    table += f"{func['name']}:\n"
                    for i, example in enumerate(func['examples']):
                        table += f"  {i+1}. {example}\n"
                    table += "\n"
        
        return table
    
    elif format == "markdown":
        # Create markdown documentation
        md = f"# {library['name']}\n\n{library['description']}\n\n"
        md += f"**Functions:** {library['metadata']['function_count']}  \n"
        md += f"**Categories:** {', '.join(library['categories'])}\n\n"
        
        # Group by category
        by_category = {}
        for func in functions:
            category = func['category']
            if category not in by_category:
                by_category[category] = []
            by_category[category].append(func)
        
        for category, funcs in by_category.items():
            md += f"## {category.title()}\n\n"
            
            for func in sorted(funcs, key=lambda x: x['name']):
                md += f"### {func['name']}\n\n"
                md += f"{func['description']}\n\n"
                
                if func['aliases']:
                    md += f"**Aliases:** {', '.join(func['aliases'])}\n\n"
                
                if include_examples and func['examples']:
                    md += "**Examples:**\n\n"
                    for example in func['examples']:
                        md += f"```\n{example}\n```\n\n"
        
        return md
    
    # Default to dict format
    return get_library_catalog(library, category, include_examples, "dict")


def search_library(library, query, search_examples=True, minimum_score=0.2):
    """
    Searches the library for functions matching a query.
    
    Args:
        library (dict): Function library
        query (str): Search query
        search_examples (bool): Whether to search in examples
        minimum_score (float): Minimum relevance score (0-1)
        
    Returns:
        list: Matching functions with relevance scores
    """
    #H->AI::LibrarySearch: (Find relevant functions in library)
    
    query_terms = query.lower().split()
    results = []
    
    for func_name, func_entry in library['functions'].items():
        score = 0.0
        matches = []
        
        # Check function name
        name_score = calculate_text_match_score(func_entry['name'], query_terms)
        if name_score > 0:
            score += name_score * 3  # Name matches are most important
            matches.append(f"Name match: {name_score:.2f}")
        
        # Check aliases
        for alias in func_entry['aliases']:
            alias_score = calculate_text_match_score(alias, query_terms)
            if alias_score > 0:
                score += alias_score * 2
                matches.append(f"Alias match ({alias}): {alias_score:.2f}")
        
        # Check description
        desc_score = calculate_text_match_score(func_entry['description'], query_terms)
        if desc_score > 0:
            score += desc_score * 1.5
            matches.append(f"Description match: {desc_score:.2f}")
        
        # Check category
        cat_score = calculate_text_match_score(func_entry['category'], query_terms)
        if cat_score > 0:
            score += cat_score
            matches.append(f"Category match: {cat_score:.2f}")
        
        # Check examples if enabled
        if search_examples and func_entry['examples']:
            for i, example in enumerate(func_entry['examples']):
                ex_score = calculate_text_match_score(example, query_terms)
                if ex_score > 0:
                    score += ex_score * 0.8
                    matches.append(f"Example {i+1} match: {ex_score:.2f}")
        
        # Normalize score to 0-1 range
        normalized_score = min(1.0, score / 6.0)
        
        # Add to results if score meets minimum
        if normalized_score >= minimum_score:
            results.append({
                'function': func_entry,
                'score': normalized_score,
                'matches': matches
            })
    
    # Sort results by score (highest first)
    return sorted(results, key=lambda x: x['score'], reverse=True)


def calculate_text_match_score(text, query_terms):
    """
    Calculates a relevance score for text matching query terms.
    
    Args:
        text (str): Text to check
        query_terms (list): Query terms to match
        
    Returns:
        float: Match score (0-1)
    """
    if not text or not query_terms:
        return 0.0
    
    text_lower = text.lower()
    text_terms = text_lower.split()
    
    # Exact match of the full query
    full_query = " ".join(query_terms)
    if full_query in text_lower:
        return 1.0
    
    # Count matching terms
    matches = sum(1 for term in query_terms if term in text_lower)
    
    # Calculate score based on proportion of matching terms
    if matches == 0:
        return 0.0
    
    return matches / len(query_terms)


def get_function_recommendations(library, context, max_recommendations=3):
    """
    Gets function recommendations based on context.
    
    Args:
        library (dict): Function library
        context (dict): Context information
        max_recommendations (int): Maximum number of recommendations
        
    Returns:
        list: Recommended functions
    """
    #H->AI::FunctionRecommendation: (Suggest relevant functions based on context)
    
    # Extract relevant information from context
    user_goal = context.get('goal', '')
    recent_usage = context.get('recent_functions', [])
    user_data = context.get('data_type', '')
    
    # Score functions based on relevance to context
    scores = {}
    
    for name, func in library['functions'].items():
        score = 0.0
        
        # Score by relevance to user goal
        if user_goal:
            goal_match = calculate_text_match_score(func['description'], user_goal.lower().split())
            score += goal_match * 3
        
        # Boost recently used functions in the same category
        for recent in recent_usage:
            if recent in library['functions']:
                recent_func = library['functions'][recent]
                if recent_func['category'] == func['category']:
                    score += 0.5
        
        # Score by relevance to data type
        if user_data:
            # Simple check for data type mentions in description or examples
            data_terms = user_data.lower().split()
            
            desc_match = calculate_text_match_score(func['description'], data_terms)
            score += desc_match * 2
            
            # Check examples for data type relevance
            for example in func['examples']:
                example_match = calculate_text_match_score(example, data_terms)
                score += example_match
        
        # Add usage frequency bonus (frequently used functions are likely useful)
        usage_bonus = min(0.5, func['usage_count'] / 10)
        score += usage_bonus
        
        scores[name] = score
    
    # Get top recommendations
    recommendations = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    top_recommendations = recommendations[:max_recommendations]
    
    # Format recommendations
    results = []
    for name, score in top_recommendations:
        if score > 0:
            func = library['functions'][name]
            results.append({
                'name': func['name'],
                'qualified_name': func['qualified_name'],
                'description': func['description'],
                'relevance_score': score,
                'example': func['examples'][0] if func['examples'] else None
            })
    
    return results


def create_function_group(library, group_name, function_names, description=None):
    """
    Creates a function group for related functions.
    
    Args:
        library (dict): Function library
        group_name (str): Name of the function group
        function_names (list): Names of functions to include
        description (str, optional): Group description
        
    Returns:
        dict: Function group object
    """
    #H->AI::GroupCreation: (Create logical grouping of related functions)
    
    if description is None:
        description = f"A group of related functions for {group_name}"
    
    # Resolve function names to actual functions
    functions = []
    for name in function_names:
        func = get_function_from_library(library, name)
        if func is not None:
            # Find the function entry
            for qualified_name, entry in library['functions'].items():
                if entry['function'] == func:
                    functions.append(entry)
                    break
    
    # Create the group
    group = {
        'name': group_name,
        'description': description,
        'functions': functions,
        'created': get_current_timestamp()
    }
    
    # Add group to library if it has a groups container
    if 'groups' not in library:
        library['groups'] = {}
    
    library['groups'][group_name] = group
    
    return group


def extend_library(base_library, extension_name, description=None):
    """
    Creates an extension of an existing library.
    
    Args:
        base_library (dict): Base library to extend
        extension_name (str): Name of the extension
        description (str, optional): Extension description
        
    Returns:
        dict: Extended library
    """
    #H->AI::LibraryExtension: (Create specialized version of existing library)
    
    if description is None:
        description = f"An extension of the {base_library['name']} library"
    
    # Create new library with extended namespace
    extended_namespace = f"{base_library['namespace']}.{extension_name.lower().replace(' ', '_')}"
    
    extension = create_function_library(
        library_name=f"{base_library['name']} - {extension_name}",
        description=description,
        categories=base_library['categories'].copy(),
        namespace=extended_namespace
    )
    
    # Add link to base library
    extension['extends'] = {
        'library': base_library['name'],
        'namespace': base_library['namespace']
    }
    
    # Add method to access base library functions
    extension['get_base_function'] = lambda name: get_function_from_library(base_library, name)
    
    return extension


def get_current_timestamp():
    """Gets current timestamp for versioning."""
    #H->AI::TimeTracking: (Record timestamp for version history)
    
    # In real implementation, would return actual timestamp
    return "2025-05-17T12:34:56Z"

Example Scenarios

Scenario 1: Text Processing Library

python

# Create a text processing library
text_library = create_function_library(
    library_name="Text Processing Library",
    description="A collection of functions for processing and analyzing text",
    categories=["cleaning", "analysis", "formatting", "extraction"]
)

# Define some text processing functions
def clean_text(text, remove_punctuation=True, lowercase=True, remove_extra_spaces=True):
    """
    Cleans text by removing punctuation, converting to lowercase, and normalizing spaces.
    
    Args:
        text (str): Text to clean
        remove_punctuation (bool): Whether to remove punctuation
        lowercase (bool): Whether to convert to lowercase
        remove_extra_spaces (bool): Whether to normalize spaces
        
    Returns:
        str: Cleaned text
    """
    result = text
    
    if remove_punctuation:
        import string
        result = ''.join(c for c in result if c not in string.punctuation)
    
    if lowercase:
        result = result.lower()
    
    if remove_extra_spaces:
        result = ' '.join(result.split())
    
    return result

def count_words(text, exclude_stopwords=False):
    """
    Counts words in text.
    
    Args:
        text (str): Text to analyze
        exclude_stopwords (bool): Whether to exclude common stopwords
        
    Returns:
        int: Word count
    """
    words = text.split()
    
    if exclude_stopwords:
        stopwords = ["the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for", "with", "by"]
        words = [w for w in words if w.lower() not in stopwords]
    
    return len(words)

def format_as_bullet_points(items, bullet_char="•"):
    """
    Formats a list of items as bullet points.
    
    Args:
        items (list): Items to format
        bullet_char (str): Character to use for bullets
        
    Returns:
        str: Formatted bullet points
    """
    return "\n".join(f"{bullet_char} {item}" for item in items)

def extract_key_phrases(text, max_phrases=5):
    """
    Extracts key phrases from text.
    
    Args:
        text (str): Text to analyze
        max_phrases (int): Maximum number of phrases to extract
        
    Returns:
        list: Extracted key phrases
    """
    # Simplified implementation for example
    words = clean_text(text).split()
    word_freq = {}
    
    for word in words:
        if len(word) > 3:  # Only consider words longer than 3 characters
            word_freq[word] = word_freq.get(word, 0) + 1
    
    # Sort by frequency
    sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
    
    # Extract top phrases (just words in this simplified example)
    return [word for word, freq in sorted_words[:max_phrases]]

# Add functions to the library
add_function_to_library(
    library=text_library,
    function=clean_text,
    category="cleaning",
    examples=[
        "text_processing.clean_text('Hello, World!', remove_punctuation=True, lowercase=True)",
        "text_processing.clean_text('  Multiple   spaces   here  ', remove_extra_spaces=True)"
    ],
    aliases=["sanitize_text", "normalize_text"]
)

add_function_to_library(
    library=text_library,
    function=count_words,
    category="analysis",
    examples=[
        "text_processing.count_words('This is a sample text with 7 words.')",
        "text_processing.count_words('The cat sat on the mat.', exclude_stopwords=True)"
    ]
)

add_function_to_library(
    library=text_library,
    function=format_as_bullet_points,
    category="formatting",
    examples=[
        "text_processing.format_as_bullet_points(['Item 1', 'Item 2', 'Item 3'])",
        "text_processing.format_as_bullet_points(['Apple', 'Banana', 'Cherry'], bullet_char='-')"
    ],
    aliases=["bulletize"]
)

add_function_to_library(
    library=text_library,
    function=extract_key_phrases,
    category="extraction",
    examples=[
        "text_processing.extract_key_phrases('Artificial intelligence is transforming how we interact with technology.')",
        "text_processing.extract_key_phrases(long_article, max_phrases=10)"
    ],
    aliases=["get_key_terms"]
)

# Use the library catalog
library_catalog = get_library_catalog(text_library, format="markdown", include_examples=True)
# print(library_catalog)

# Search the library
search_results = search_library(text_library, "format bullet list")
# for result in search_results:
#     print(f"{result['function']['name']} - Score: {result['score']:.2f}")
#     print(f"  {result['function']['description']}")
#     print(f"  Matches: {', '.join(result['matches'])}")
#     print()

# Execute a function from the library
# formatted_list = execute_library_function(
#     text_library, 
#     "format_as_bullet_points", 
#     ["First item", "Second item", "Third item"]
# )
# print(formatted_list)

# Get function recommendations based on context
context = {
    'goal': 'format a list of items for presentation',
    'recent_functions': ['text_processing.clean_text'],
    'data_type': 'list of strings'
}

recommendations = get_function_recommendations(text_library, context)
# for rec in recommendations:
#     print(f"{rec['name']} - Relevance: {rec['relevance_score']:.2f}")
#     print(f"  {rec['description']}")
#     if rec['example']:
#         print(f"  Example: {rec['example']}")
#     print()

Scenario 2: Multi-Domain Library Ecosystem with Extensions

python

# Create a data analysis library
data_library = create_function_library(
    library_name="Data Analysis Library",
    description="Functions for analyzing and visualizing data",
    categories=["statistics", "visualization", "processing", "reporting"]
)

# Define some data analysis functions
def calculate_statistics(data, measures=None):
    """
    Calculates basic statistics for a dataset.
    
    Args:
        data (list): Numerical data
        measures (list, optional): Statistics to calculate
        
    Returns:
        dict: Calculated statistics
    """
    if not data:
        return {}
    
    if measures is None:
        measures = ["mean", "median", "min", "max", "std_dev"]
    
    results = {}
    
    if "mean" in measures:
        results["mean"] = sum(data) / len(data)
    
    if "median" in measures:
        sorted_data = sorted(data)
        mid = len(sorted_data) // 2
        if len(sorted_data) % 2 == 0:
            results["median"] = (sorted_data[mid-1] + sorted_data[mid]) / 2
        else:
            results["median"] = sorted_data[mid]
    
    if "min" in measures:
        results["min"] = min(data)
    
    if "max" in measures:
        results["max"] = max(data)
    
    if "std_dev" in measures:
        mean = results.get("mean", sum(data) / len(data))
        variance = sum((x - mean) ** 2 for x in data) / len(data)
        results["std_dev"] = variance ** 0.5
    
    return results

def generate_histogram_data(data, bins=10):
    """
    Generates histogram data for visualization.
    
    Args:
        data (list): Numerical data
        bins (int): Number of bins
        
    Returns:
        dict: Histogram data
    """
    if not data:
        return {"bins": [], "counts": []}
    
    min_val = min(data)
    max_val = max(data)
    bin_width = (max_val - min_val) / bins
    
    # Create bin edges
    bin_edges = [min_val + i * bin_width for i in range(bins + 1)]
    
    # Count values in each bin
    bin_counts = [0] * bins
    for value in data:
        bin_index = min(bins - 1, max(0, int((value - min_val) / bin_width)))
        bin_counts[bin_index] += 1
    
    return {
        "bin_edges": bin_edges,
        "bin_counts": bin_counts
    }

def correlation_analysis(data_x, data_y):
    """
    Performs correlation analysis between two datasets.
    
    Args:
        data_x (list): First dataset
        data_y (list): Second dataset
        
    Returns:
        dict: Correlation results
    """
    if len(data_x) != len(data_y) or len(data_x) == 0:
        return {"error": "Datasets must have the same non-zero length"}
    
    # Calculate means
    mean_x = sum(data_x) / len(data_x)
    mean_y = sum(data_y) / len(data_y)
    
    # Calculate covariance and variances
    covariance = sum((x - mean_x) * (y - mean_y) for x, y in zip(data_x, data_y)) / len(data_x)
    variance_x = sum((x - mean_x) ** 2 for x in data_x) / len(data_x)
    variance_y = sum((y - mean_y) ** 2 for y in data_y) / len(data_y)
    
    # Calculate Pearson correlation coefficient
    correlation = covariance / ((variance_x ** 0.5) * (variance_y ** 0.5))
    
    return {
        "correlation": correlation,
        "covariance": covariance,
        "r_squared": correlation ** 2
    }

def generate_summary_report(data, title="Data Summary Report"):
    """
    Generates a summary report for a dataset.
    
    Args:
        data (list): Numerical data
        title (str): Report title
        
    Returns:
        str: Formatted report
    """
    stats = calculate_statistics(data)
    
    report = f"## {title}\n\n"
    report += f"Dataset size: {len(data)} points\n\n"
    report += "### Summary Statistics\n\n"
    report += f"- Mean: {stats['mean']:.2f}\n"
    report += f"- Median: {stats['median']:.2f}\n"
    report += f"- Minimum: {stats['min']:.2f}\n"
    report += f"- Maximum: {stats['max']:.2f}\n"
    report += f"- Standard Deviation: {stats['std_dev']:.2f}\n\n"
    
    report += "### Data Range\n\n"
    report += f"The data ranges from {stats['min']:.2f} to {stats['max']:.2f}, "
    report += f"spanning {stats['max'] - stats['min']:.2f} units."
    
    return report

# Add functions to the data library
add_function_to_library(
    library=data_library,
    function=calculate_statistics,
    category="statistics",
    examples=[
        "data_analysis.calculate_statistics([1, 2, 3, 4, 5])",
        "data_analysis.calculate_statistics(temperature_data, measures=['mean', 'min', 'max'])"
    ]
)

add_function_to_library(
    library=data_library,
    function=generate_histogram_data,
    category="visualization",
    examples=[
        "data_analysis.generate_histogram_data([1, 2, 2, 3, 3, 3, 4, 4, 5])",
        "data_analysis.generate_histogram_data(height_data, bins=20)"
    ],
    aliases=["histogram", "create_histogram"]
)

add_function_to_library(
    library=data_library,
    function=correlation_analysis,
    category="statistics",
    examples=[
        "data_analysis.correlation_analysis(x_values, y_values)",
        "data_analysis.correlation_analysis(heights, weights)"
    ],
    aliases=["correlate", "pearson_correlation"]
)

add_function_to_library(
    library=data_library,
    function=generate_summary_report,
    category="reporting",
    examples=[
        "data_analysis.generate_summary_report(temperature_data)",
        "data_analysis.generate_summary_report(sales_figures, title='Monthly Sales Summary')"
    ],
    aliases=["summarize_data", "data_report"]
)

# Create an extension for specialized financial analysis
finance_library = extend_library(
    base_library=data_library,
    extension_name="Financial Analysis",
    description="Specialized data analysis functions for financial data"
)

# Add financial-specific functions
def calculate_roi(initial_investment, final_value):
    """
    Calculates Return on Investment (ROI).
    
    Args:
        initial_investment (float): Initial investment amount
        final_value (float): Final value of investment
        
    Returns:
        float: ROI as a percentage
    """
    return ((final_value - initial_investment) / initial_investment) * 100

def calculate_cagr(initial_value, final_value, years):
    """
    Calculates Compound Annual Growth Rate (CAGR).
    
    Args:
        initial_value (float): Initial value
        final_value (float): Final value
        years (float): Number of years
        
    Returns:
        float: CAGR as a percentage
    """
    if years <= 0 or initial_value <= 0:
        return 0
    
    return (((final_value / initial_value) ** (1 / years)) - 1) * 100

def risk_adjusted_return(returns, risk_free_rate=0.0):
    """
    Calculates risk-adjusted return metrics.
    
    Args:
        returns (list): List of period returns
        risk_free_rate (float): Risk-free rate as a percentage
        
    Returns:
        dict: Risk-adjusted return metrics
    """
    stats = calculate_statistics(returns)
    
    mean_return = stats["mean"]
    std_dev = stats["std_dev"]
    
    # Convert risk-free rate to decimal
    rf_decimal = risk_free_rate / 100
    
    # Calculate Sharpe ratio
    sharpe_ratio = (mean_return - rf_decimal) / std_dev if std_dev > 0 else 0
    
    # Calculate Sortino ratio (simplified - only considering downside deviation)
    downside_returns = [r for r in returns if r < rf_decimal]
    downside_deviation = (sum((r - rf_decimal) ** 2 for r in downside_returns) / len(returns)) ** 0.5 if downside_returns else 0
    sortino_ratio = (mean_return - rf_decimal) / downside_deviation if downside_deviation > 0 else 0
    
    return {
        "sharpe_ratio": sharpe_ratio,
        "sortino_ratio": sortino_ratio,
        "mean_return": mean_return,
        "volatility": std_dev
    }

# Add functions to the finance library extension
add_function_to_library(
    library=finance_library,
    function=calculate_roi,
    category="statistics",
    examples=[
        "data_analysis.financial_analysis.calculate_roi(1000, 1500)",
        "data_analysis.financial_analysis.calculate_roi(initial_capital, final_portfolio_value)"
    ],
    aliases=["roi", "return_on_investment"]
)

add_function_to_library(
    library=finance_library,
    function=calculate_cagr,
    category="statistics",
    examples=[
        "data_analysis.financial_analysis.calculate_cagr(10000, 16000, 5)",
        "data_analysis.financial_analysis.calculate_cagr(initial_value, current_value, investment_years)"
    ],
    aliases=["cagr", "annual_growth_rate"]
)

add_function_to_library(
    library=finance_library,
    function=risk_adjusted_return,
    category="statistics",
    examples=[
        "data_analysis.financial_analysis.risk_adjusted_return([0.05, -0.02, 0.03, 0.08, -0.01])",
        "data_analysis.financial_analysis.risk_adjusted_return(monthly_returns, risk_free_rate=2.5)"
    ],
    aliases=["risk_metrics", "sharpe_ratio"]
)

# Create function groups for related functions
create_function_group(
    library=data_library,
    group_name="Basic Analysis",
    function_names=["calculate_statistics", "correlation_analysis"],
    description="Core statistical analysis functions"
)

create_function_group(
    library=finance_library,
    group_name="Investment Analysis",
    function_names=["calculate_roi", "calculate_cagr", "risk_adjusted_return"],
    description="Functions for analyzing investment performance"
)

# Execute a function from the extension library that builds on base library
# investment_analysis = execute_library_function(
#     finance_library,
#     "risk_adjusted_return",
#     [0.05, -0.02, 0.03, 0.08, -0.01],
#     risk_free_rate=2.0
# )
# print(investment_analysis)

# The extended library can also access base library functions
# histogram_data = execute_library_function(
#     finance_library,
#     "generate_histogram_data",  # This is from the base library
#     [1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5]
# )
# print(histogram_data)

Scenario 3: Enterprise Workflow Library with Domain-Specific Extensions

python

# Create a core workflow library
workflow_library = create_function_library(
    library_name="Enterprise Workflow Library",
    description="Core functions for enterprise workflow automation",
    categories=["process", "validation", "notification", "integration"]
)

# Basic workflow functions
def validate_input(data, schema, strict=True):
    """
    Validates input data against a schema.
    
    Args:
        data (dict): Input data to validate
        schema (dict): Validation schema
        strict (bool): Whether to enforce strict validation
        
    Returns:
        dict: Validation results
    """
    errors = []
    
    # Check required fields
    for field, field_schema in schema.items():
        if field_schema.get("required", False) and field not in data:
            errors.append(f"Required field '{field}' is missing")
            continue
        
        if field in data:
            # Type validation
            expected_type = field_schema.get("type")
            if expected_type:
                type_map = {
                    "string": str,
                    "integer": int,
                    "float": float,
                    "boolean": bool,
                    "list": list,
                    "dict": dict
                }
                
                if expected_type in type_map:
                    if not isinstance(data[field], type_map[expected_type]):
                        errors.append(f"Field '{field}' should be of type '{expected_type}'")
            
            # Range validation for numeric types
            if "min" in field_schema and isinstance(data[field], (int, float)):
                if data[field] < field_schema["min"]:
                    errors.append(f"Field '{field}' should be at least {field_schema['min']}")
            
            if "max" in field_schema and isinstance(data[field], (int, float)):
                if data[field] > field_schema["max"]:
                    errors.append(f"Field '{field}' should be at most {field_schema['max']}")
            
            # Pattern validation for strings
            if "pattern" in field_schema and isinstance(data[field], str):
                import re
                if not re.match(field_schema["pattern"], data[field]):
                    errors.append(f"Field '{field}' does not match the required pattern")
    
    return {
        "valid": len(errors) == 0,
        "errors": errors,
        "validated_data": data if len(errors) == 0 or not strict else None
    }

def process_workflow_step(step_name, input_data, workflow_config):
    """
    Processes a single workflow step.
    
    Args:
        step_name (str): Name of the workflow step
        input_data (dict): Input data for the step
        workflow_config (dict): Workflow configuration
        
    Returns:
        dict: Step processing results
    """
    if step_name not in workflow_config["steps"]:
        return {
            "status": "error",
            "message": f"Step '{step_name}' not found in workflow configuration"
        }
    
    step_config = workflow_config["steps"][step_name]
    
    # Validate input against step schema
    if "input_schema" in step_config:
        validation = validate_input(input_data, step_config["input_schema"])
        if not validation["valid"]:
            return {
                "status": "validation_error",
                "errors": validation["errors"]
            }
    
    # Process step (simplified for example)
    result = {
        "status": "completed",
        "step": step_name,
        "timestamp": get_current_timestamp(),
        "output": {
            "message": f"Processed step '{step_name}' successfully"
        }
    }
    
    # Add step-specific processing logic here
    if "transform" in step_config:
        # Apply data transformations
        transform_type = step_config["transform"].get("type", "")
        
        if transform_type == "map":
            # Map input fields to output fields
            mapping = step_config["transform"].get("mapping", {})
            result["output"]["data"] = {
                out_field: input_data.get(in_field)
                for out_field, in_field in mapping.items()
                if in_field in input_data
            }
        
        elif transform_type == "calculate":
            # Perform calculations
            expression = step_config["transform"].get("expression", "")
            variables = {k: v for k, v in input_data.items() if isinstance(v, (int, float))}
            
            try:
                # Very simplified expression evaluation (unsafe in real code)
                from math import *  # Makes math functions available in eval
                result["output"]["result"] = eval(expression, {"__builtins__": {}}, variables)
            except Exception as e:
                result["output"]["error"] = str(e)
    
    # Set next step if available
    if "next" in step_config:
        result["next_step"] = step_config["next"]
    
    return result

def send_notification(recipient, message, channel="email", metadata=None):
    """
    Sends a notification through the specified channel.
    
    Args:
        recipient (str): Notification recipient
        message (str): Notification message
        channel (str): Notification channel (email, sms, app)
        metadata (dict, optional): Additional notification metadata
        
    Returns:
        dict: Notification result
    """
    if metadata is None:
        metadata = {}
    
    # Simplified notification (would integrate with actual systems in real code)
    notification = {
        "recipient": recipient,
        "message": message,
        "channel": channel,
        "metadata": metadata,
        "timestamp": get_current_timestamp(),
        "status": "sent"  # Simulated success
    }
    
    # Channel-specific formatting
    if channel == "email":
        notification["email_subject"] = metadata.get("subject", "Notification")
    elif channel == "sms":
        # Truncate long messages for SMS
        if len(message) > 160:
            notification["message"] = message[:157] + "..."
    elif channel == "app":
        notification["priority"] = metadata.get("priority", "normal")
        notification["action_url"] = metadata.get("action_url")
    
    return notification

def execute_workflow(workflow_name, initial_data, workflow_config):
    """
    Executes a complete workflow from start to finish.
    
    Args:
        workflow_name (str): Name of the workflow to execute
        initial_data (dict): Initial input data
        workflow_config (dict): Workflow configuration
        
    Returns:
        dict: Workflow execution results
    """
    if workflow_name not in workflow_config["workflows"]:
        return {
            "status": "error",
            "message": f"Workflow '{workflow_name}' not found in configuration"
        }
    
    workflow_def = workflow_config["workflows"][workflow_name]
    
    # Initialize execution
    execution = {
        "workflow": workflow_name,
        "start_time": get_current_timestamp(),
        "status": "in_progress",
        "steps": [],
        "current_data": initial_data
    }
    
    # Get starting step
    current_step = workflow_def.get("start_step")
    if not current_step:
        execution["status"] = "error"
        execution["message"] = "No start_step defined for workflow"
        return execution
    
    # Execute steps until completion or error
    while current_step:
        # Process the current step
        step_result = process_workflow_step(current_step, execution["current_data"], workflow_config)
        execution["steps"].append({
            "name": current_step,
            "result": step_result
        })
        
        # Check for errors
        if step_result["status"] in ["error", "validation_error"]:
            execution["status"] = "failed"
            execution["error"] = step_result.get("message", "Step execution failed")
            execution["failed_step"] = current_step
            break
        
        # Update current data with step output
        if "data" in step_result.get("output", {}):
            execution["current_data"].update(step_result["output"]["data"])
        
        # Move to next step
        if "next_step" in step_result:
            current_step = step_result["next_step"]
        else:
            # End of workflow
            current_step = None
    
    # Finalize execution
    execution["end_time"] = get_current_timestamp()
    if execution["status"] != "failed":
        execution["status"] = "completed"
    
    return execution

# Add functions to the workflow library
add_function_to_library(
    library=workflow_library,
    function=validate_input,
    category="validation",
    examples=[
        "workflow.validate_input(form_data, user_schema)",
        "workflow.validate_input(api_response, response_schema, strict=False)"
    ],
    aliases=["validate", "check_data"]
)

add_function_to_library(
    library=workflow_library,
    function=process_workflow_step,
    category="process",
    examples=[
        "workflow.process_workflow_step('data_validation', input_data, workflow_config)",
        "workflow.process_workflow_step(next_step, transformed_data, process_definition)"
    ]
)

add_function_to_library(
    library=workflow_library,
    function=send_notification,
    category="notification",
    examples=[
        "workflow.send_notification('user@example.com', 'Your request has been approved')",
        "workflow.send_notification(user_phone, 'Verification code: 123456', channel='sms')"
    ],
    aliases=["notify", "alert"]
)

add_function_to_library(
    library=workflow_library,
    function=execute_workflow,
    category="process",
    examples=[
        "workflow.execute_workflow('new_user_onboarding', user_data, system_workflows)",
        "workflow.execute_workflow('invoice_approval', invoice_data, finance_workflows)"
    ],
    aliases=["run_workflow", "workflow_execute"]
)

# Create a specialized extension for HR workflows
hr_workflow_library = extend_library(
    base_library=workflow_library,
    extension_name="HR Workflows",
    description="Specialized workflow functions for HR processes"
)

# Add HR-specific workflow functions
def generate_employee_onboarding_workflow(custom_steps=None):
    """
    Generates a standard employee onboarding workflow configuration.
    
    Args:
        custom_steps (dict, optional): Custom steps to add to the workflow
        
    Returns:
        dict: Onboarding workflow configuration
    """
    # Standard onboarding steps
    standard_steps = {
        "validate_employee_data": {
            "input_schema": {
                "first_name": {"type": "string", "required": True},
                "last_name": {"type": "string", "required": True},
                "email": {"type": "string", "required": True, "pattern": r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"},
                "position": {"type": "string", "required": True},
                "department": {"type": "string", "required": True},
                "start_date": {"type": "string", "required": True}
            },
            "next": "create_accounts"
        },
        "create_accounts": {
            "transform": {
                "type": "map",
                "mapping": {
                    "username": "email",
                    "display_name": "first_name"
                }
            },
            "next": "assign_equipment"
        },
        "assign_equipment": {
            "input_schema": {
                "position": {"type": "string", "required": True},
                "department": {"type": "string", "required": True}
            },
            "next": "schedule_orientation"
        },
        "schedule_orientation": {
            "input_schema": {
                "start_date": {"type": "string", "required": True}
            },
            "next": "send_welcome_email"
        },
        "send_welcome_email": {
            "transform": {
                "type": "map",
                "mapping": {
                    "recipient": "email",
                    "name": "first_name"
                }
            }
        }
    }
    
    # Add or override with custom steps
    if custom_steps:
        for step_name, step_config in custom_steps.items():
            standard_steps[step_name] = step_config
    
    # Create the workflow configuration
    workflow_config = {
        "steps": standard_steps,
        "workflows": {
            "employee_onboarding": {
                "name": "New Employee Onboarding",
                "description": "Process for onboarding new employees",
                "start_step": "validate_employee_data"
            }
        }
    }
    
    return workflow_config

def employee_status_change_workflow(status_change_type):
    """
    Generates a workflow for employee status changes.
    
    Args:
        status_change_type (str): Type of status change (promotion, transfer, leave, termination)
        
    Returns:
        dict: Status change workflow configuration
    """
    # Base workflow structure
    workflow_config = {
        "steps": {
            "validate_request": {
                "input_schema": {
                    "employee_id": {"type": "string", "required": True},
                    "requester_id": {"type": "string", "required": True},
                    "effective_date": {"type": "string", "required": True},
                    "reason": {"type": "string", "required": True}
                },
                "next": "manager_approval"
            },
            "manager_approval": {
                "next": "hr_review"
            },
            "hr_review": {
                "next": "update_systems"
            },
            "update_systems": {
                "next": "notify_stakeholders"
            },
            "notify_stakeholders": {}
        },
        "workflows": {
            f"{status_change_type}_workflow": {
                "name": f"Employee {status_change_type.title()} Process",
                "description": f"Workflow for processing employee {status_change_type}",
                "start_step": "validate_request"
            }
        }
    }
    
    # Add status-specific configurations
    if status_change_type == "promotion":
        workflow_config["steps"]["validate_request"]["input_schema"].update({
            "new_title": {"type": "string", "required": True},
            "new_compensation": {"type": "float", "required": True},
            "performance_review": {"type": "string", "required": True}
        })
        
    elif status_change_type == "transfer":
        workflow_config["steps"]["validate_request"]["input_schema"].update({
            "new_department": {"type": "string", "required": True},
            "new_manager": {"type": "string", "required": True},
            "transfer_reason": {"type": "string", "required": True}
        })
        
    elif status_change_type == "leave":
        workflow_config["steps"]["validate_request"]["input_schema"].update({
            "leave_type": {"type": "string", "required": True},
            "start_date": {"type": "string", "required": True},
            "end_date": {"type": "string", "required": True},
            "return_date": {"type": "string", "required": False}
        })
        
    elif status_change_type == "termination":
        workflow_config["steps"]["validate_request"]["input_schema"].update({
            "termination_type": {"type": "string", "required": True},
            "last_day": {"type": "string", "required": True},
            "exit_interview_date": {"type": "string", "required": False}
        })
        
        # Add additional termination-specific steps
        workflow_config["steps"]["exit_checklist"] = {
            "next": "final_payroll"
        }
        workflow_config["steps"]["final_payroll"] = {
            "next": "revoke_access"
        }
        workflow_config["steps"]["revoke_access"] = {
            "next": "notify_stakeholders"
        }
        
        # Update flow
        workflow_config["steps"]["hr_review"]["next"] = "exit_checklist"
    
    return workflow_config

def generate_hr_notification_template(notification_type, employee_data):
    """
    Generates an HR notification based on template type.
    
    Args:
        notification_type (str): Type of notification
        employee_data (dict): Employee data for the notification
        
    Returns:
        dict: Notification content and metadata
    """
    templates = {
        "welcome": {
            "subject": "Welcome to our company!",
            "message": f"Welcome {employee_data.get('first_name', '')}!\n\nWe're excited to have you join our team on {employee_data.get('start_date', '')}. Please report to the reception desk at 9:00 AM on your first day.\n\nYour orientation schedule is attached to this email.",
            "channel": "email",
            "metadata": {
                "priority": "high",
                "attachments": ["orientation_schedule.pdf", "employee_handbook.pdf"]
            }
        },
        "promotion": {
            "subject": "Congratulations on your promotion!",
            "message": f"Congratulations {employee_data.get('first_name', '')}!\n\nWe're pleased to inform you that your promotion to {employee_data.get('new_title', '')} has been approved, effective {employee_data.get('effective_date', '')}.",
            "channel": "email",
            "metadata": {
                "priority": "high",
                "cc": ["hr@example.com"]
            }
        },
        "leave_approval": {
            "subject": "Leave Request Approved",
            "message": f"Hello {employee_data.get('first_name', '')},\n\nYour leave request from {employee_data.get('start_date', '')} to {employee_data.get('end_date', '')} has been approved.",
            "channel": "email",
            "metadata": {
                "priority": "normal",
                "calendar_invite": True
            }
        },
        "termination": {
            "subject": "Employment Separation Information",
            "message": f"Dear {employee_data.get('first_name', '')},\n\nThis email contains important information regarding your separation from the company effective {employee_data.get('last_day', '')}. Please review the attached documents for details about benefits continuation and final pay.",
            "channel": "email",
            "metadata": {
                "priority": "high",
                "confidential": True,
                "attachments": ["separation_documents.pdf", "benefits_continuation.pdf"]
            }
        }
    }
    
    # Get the appropriate template
    template = templates.get(notification_type, {
        "subject": "HR Notification",
        "message": "This is an HR notification.",
        "channel": "email",
        "metadata": {"priority": "normal"}
    })
    
    # Customize with employee data
    for key, value in employee_data.items():
        placeholder = f"{{{key}}}"
        template["message"] = template["message"].replace(placeholder, str(value))
    
    return template

# Add HR-specific functions to the library
add_function_to_library(
    library=hr_workflow_library,
    function=generate_employee_onboarding_workflow,
    category="process",
    examples=[
        "workflow.hr_workflows.generate_employee_onboarding_workflow()",
        "workflow.hr_workflows.generate_employee_onboarding_workflow(custom_steps={'compliance_training': {'next': 'team_introduction'}})"
    ],
    aliases=["onboarding_workflow", "new_employee_process"]
)

add_function_to_library(
    library=hr_workflow_library,
    function=employee_status_change_workflow,
    category="process",
    examples=[
        "workflow.hr_workflows.employee_status_change_workflow('promotion')",
        "workflow.hr_workflows.employee_status_change_workflow('termination')"
    ],
    aliases=["status_change", "employee_transition"]
)

add_function_to_library(
    library=hr_workflow_library,
    function=generate_hr_notification_template,
    category="notification",
    examples=[
        "workflow.hr_workflows.generate_hr_notification_template('welcome', {'first_name': 'John', 'start_date': '2025-06-01'})",
        "workflow.hr_workflows.generate_hr_notification_template('promotion', employee_data)"
    ],
    aliases=["hr_notify_template", "employee_notification"]
)

# Create groups for related functions
create_function_group(
    library=workflow_library,
    group_name="Core Workflow Execution",
    function_names=["process_workflow_step", "execute_workflow"],
    description="Primary functions for workflow execution"
)

create_function_group(
    library=hr_workflow_library,
    group_name="Employee Lifecycle",
    function_names=["generate_employee_onboarding_workflow", "employee_status_change_workflow"],
    description="Functions for managing employee lifecycle workflows"
)

# Get recommendations based on HR context
hr_context = {
    'goal': 'set up workflow for new employee starting next week',
    'recent_functions': ['workflow.validate_input', 'workflow.send_notification'],
    'data_type': 'employee records'
}

hr_recommendations = get_function_recommendations(hr_workflow_library, hr_context)
# for rec in hr_recommendations:
#     print(f"{rec['name']} - Relevance: {rec['relevance_score']:.2f}")
#     print(f"  {rec['description']}")
#     if rec['example']:
#         print(f"  Example: {rec['example']}")
#     print()

# Execute an HR workflow function that builds on base library capabilities
# onboarding_config = execute_library_function(
#     hr_workflow_library,
#     "generate_employee_onboarding_workflow"
# )
#
# new_employee = {
#     "first_name": "Jane",
#     "last_name": "Smith",
#     "email": "jane.smith@example.com",
#     "position": "Software Engineer",
#     "department": "Engineering",
#     "start_date": "2025-06-01"
# }
#
# # Execute the workflow using a base library function
# onboarding_result = execute_library_function(
#     hr_workflow_library,
#     "execute_workflow",  # This is from the base library
#     "employee_onboarding",
#     new_employee,
#     onboarding_config
# )
# print(onboarding_result)

Benefits of Function Libraries

Organized Structure: Provides a clear, hierarchical organization for functions, making large collections manageable.
Discoverability: Makes it easier to find relevant functions through search, recommendations, and browsing.
Consistency: Enforces consistent patterns, documentation, and interfaces across related functions.
Namespace Management: Prevents naming conflicts and clarifies the source and purpose of each function.
Contextual Recommendations: Suggests relevant functions based on the user's current context and goals.
Extensibility: Allows libraries to be extended and specialized for specific domains without modifying the original.
Documentation: Automatically generates comprehensive documentation for all functions in the library.
Logical Grouping: Groups related functions together, improving understanding of function relationships.
Version Tracking: Maintains version history for libraries and their functions.
Reduced Redundancy: Encourages reuse of existing functions rather than duplicating functionality.
Simplified Integration: Makes it easier to integrate functions from different domains through consistent interfaces.
Knowledge Preservation: Captures usage examples and relationships that help preserve institutional knowledge.

Advanced Applications

Function Library Visualization

python

def generate_library_visualization(library, format="d3"):
    """
    Generates a visualization of library structure and relationships.
    
    Args:
        library (dict): Function library
        format (str): Visualization format (d3, mermaid, dot)
        
    Returns:
        str: Visualization code
    """
    #H->AI::VisualizeLibrary: (Create visual representation of library structure)
    
    if format == "mermaid":
        # Generate Mermaid.js flowchart
        mermaid = "graph TD\n"
        
        # Add library node
        lib_id = library['namespace'].replace(".", "_")
        mermaid += f"    {lib_id}[{library['name']}]\n"
        
        # Add category nodes
        for category in library['categories']:
            cat_id = f"{lib_id}_{category}"
            mermaid += f"    {cat_id}({category})\n"
            mermaid += f"    {lib_id} --> {cat_id}\n"
        
        # Add function nodes
        for func_name, func in library['functions'].items():
            func_id = func_name.replace(".", "_")
            mermaid += f"    {func_id}[\"{func['name']}\"]\n"
            
            # Connect to category
            cat_id = f"{lib_id}_{func['category']}"
            mermaid += f"    {cat_id} --> {func_id}\n"
        
        # Add function groups if present
        if 'groups' in library:
            for group_name, group in library['groups'].items():
                group_id = f"{lib_id}_group_{group_name.replace(' ', '_')}"
                mermaid += f"    {group_id}{{Group: {group_name}}}\n"
                
                # Connect to functions
                for func in group['functions']:
                    func_id = func['qualified_name'].replace(".", "_")
                    mermaid += f"    {group_id} -.-> {func_id}\n"
        
        # Add extension relationships if present
        if 'extends' in library:
            base_lib = library['extends']['library']
            base_id = library['extends']['namespace'].replace(".", "_")
            mermaid += f"    {base_id}[{base_lib}]\n"
            mermaid += f"    {base_id} ==> {lib_id}\n"
        
        return mermaid
        
    elif format == "d3":
        # Generate D3.js visualization data
        nodes = []
        links = []
        
        # Add library node
        lib

Retry

Continue

Edit

python

def generate_library_visualization(library, format="d3"):
    """
    Generates a visualization of library structure and relationships.
    
    Args:
        library (dict): Function library
        format (str): Visualization format (d3, mermaid, dot)
        
    Returns:
        str: Visualization code
    """
    #H->AI::VisualizeLibrary: (Create visual representation of library structure)
    
    if format == "mermaid":
        # Generate Mermaid.js flowchart
        mermaid = "graph TD\n"
        
        # Add library node
        lib_id = library['namespace'].replace(".", "_")
        mermaid += f"    {lib_id}[{library['name']}]\n"
        
        # Add category nodes
        for category in library['categories']:
            cat_id = f"{lib_id}_{category}"
            mermaid += f"    {cat_id}({category})\n"
            mermaid += f"    {lib_id} --> {cat_id}\n"
        
        # Add function nodes
        for func_name, func in library['functions'].items():
            func_id = func_name.replace(".", "_")
            mermaid += f"    {func_id}[\"{func['name']}\"]\n"
            
            # Connect to category
            cat_id = f"{lib_id}_{func['category']}"
            mermaid += f"    {cat_id} --> {func_id}\n"
        
        # Add function groups if present
        if 'groups' in library:
            for group_name, group in library['groups'].items():
                group_id = f"{lib_id}_group_{group_name.replace(' ', '_')}"
                mermaid += f"    {group_id}{{Group: {group_name}}}\n"
                
                # Connect to functions
                for func in group['functions']:
                    func_id = func['qualified_name'].replace(".", "_")
                    mermaid += f"    {group_id} -.-> {func_id}\n"
        
        # Add extension relationships if present
        if 'extends' in library:
            base_lib = library['extends']['library']
            base_id = library['extends']['namespace'].replace(".", "_")
            mermaid += f"    {base_id}[{base_lib}]\n"
            mermaid += f"    {base_id} ==> {lib_id}\n"
        
        return mermaid
        
    elif format == "d3":
        # Generate D3.js visualization data
        nodes = []
        links = []
        
        # Add library node
        lib_id = library['namespace']
        nodes.append({
            "id": lib_id,
            "name": library['name'],
            "type": "library",
            "function_count": library['metadata']['function_count']
        })
        
        # Add category nodes
        for category in library['categories']:
            cat_id = f"{lib_id}.{category}"
            nodes.append({
                "id": cat_id,
                "name": category,
                "type": "category"
            })
            
            links.append({
                "source": lib_id,
                "target": cat_id,
                "type": "contains"
            })
        
        # Add function nodes
        for func_name, func in library['functions'].items():
            nodes.append({
                "id": func_name,
                "name": func['name'],
                "type": "function",
                "category": func['category'],
                "usage_count": func['usage_count']
            })
            
            # Connect to category
            cat_id = f"{lib_id}.{func['category']}"
            links.append({
                "source": cat_id,
                "target": func_name,
                "type": "contains"
            })
        
        # Add extension relationships
        if 'extends' in library:
            base_id = library['extends']['namespace']
            links.append({
                "source": base_id,
                "target": lib_id,
                "type": "extends"
            })
        
        # Return D3 visualization data
        return {
            "nodes": nodes,
            "links": links
        }
    
    elif format == "dot":
        # Generate GraphViz DOT format
        dot = "digraph LibraryStructure {\n"
        dot += "    rankdir=TB;\n"
        dot += "    node [shape=box, style=filled, fillcolor=lightblue];\n"
        
        # Add library node
        lib_id = library['namespace'].replace(".", "_")
        dot += f'    {lib_id} [label="{library["name"]}", fillcolor=lightgreen];\n'
        
        # Add category nodes
        for category in library['categories']:
            cat_id = f"{lib_id}_{category}"
            dot += f'    {cat_id} [label="{category}", shape=ellipse, fillcolor=lightyellow];\n'
            dot += f'    {lib_id} -> {cat_id};\n'
        
        # Add function nodes
        for func_name, func in library['functions'].items():
            func_id = func_name.replace(".", "_")
            dot += f'    {func_id} [label="{func["name"]}"];\n'
            
            # Connect to category
            cat_id = f"{lib_id}_{func["category"]}"
            dot += f'    {cat_id} -> {func_id};\n'
        
        # Add extension relationships
        if 'extends' in library:
            base_id = library['extends']['namespace'].replace(".", "_")
            dot += f'    {base_id} [label="{library["extends"]["library"]}", fillcolor=lightgreen];\n'
            dot += f'    {base_id} -> {lib_id} [style=dashed, label="extends"];\n'
        
        dot += "}\n"
        return dot
    
    # Default to simpler text representation
    return get_library_catalog(library, format="markdown")

Cross-Library Integration and Universal Registry

python

def create_library_registry():
    """
    Creates a central registry for tracking and accessing multiple function libraries.
    
    Returns:
        dict: Library registry
    """
    #H->AI::LibraryRegistry: (Create central repository of function libraries)
    
    registry = {
        'libraries': {},
        'functions': {},
        'categories': set(),
        'created': get_current_timestamp(),
        'last_updated': get_current_timestamp()
    }
    
    return registry


def register_library(registry, library):
    """
    Registers a library in the central registry.
    
    Args:
        registry (dict): Library registry
        library (dict): Library to register
        
    Returns:
        bool: Success status
    """
    #H->AI::LibraryRegistration: (Add library to central registry)
    
    # Check if library already exists
    if library['namespace'] in registry['libraries']:
        return False
    
    # Add library to registry
    registry['libraries'][library['namespace']] = {
        'name': library['name'],
        'description': library['description'],
        'namespace': library['namespace'],
        'function_count': library['metadata']['function_count'],
        'categories': library['categories'],
        'registered': get_current_timestamp()
    }
    
    # Register all functions
    for func_name, func in library['functions'].items():
        registry['functions'][func_name] = {
            'name': func['name'],
            'qualified_name': func['qualified_name'],
            'description': func['description'],
            'category': func['category'],
            'library': library['namespace'],
            'aliases': func['aliases']
        }
    
    # Update categories
    registry['categories'].update(library['categories'])
    
    # Update registry metadata
    registry['last_updated'] = get_current_timestamp()
    
    return True


def search_registry(registry, query, categories=None, libraries=None, limit=10):
    """
    Searches across all registered libraries for matching functions.
    
    Args:
        registry (dict): Library registry
        query (str): Search query
        categories (list, optional): Categories to search in
        libraries (list, optional): Libraries to search in
        limit (int): Maximum results to return
        
    Returns:
        list: Search results across libraries
    """
    #H->AI::GlobalSearch: (Search across all registered libraries)
    
    query_terms = query.lower().split()
    results = []
    
    # Filter functions by library and category if specified
    functions = registry['functions'].values()
    
    if libraries:
        functions = [f for f in functions if f['library'] in libraries]
    
    if categories:
        functions = [f for f in functions if f['category'] in categories]
    
    # Score and rank functions
    for func in functions:
        score = 0.0
        matches = []
        
        # Check function name
        name_score = calculate_text_match_score(func['name'], query_terms)
        if name_score > 0:
            score += name_score * 3
            matches.append(f"Name match: {name_score:.2f}")
        
        # Check aliases
        for alias in func['aliases']:
            alias_score = calculate_text_match_score(alias, query_terms)
            if alias_score > 0:
                score += alias_score * 2
                matches.append(f"Alias match ({alias}): {alias_score:.2f}")
        
        # Check description
        desc_score = calculate_text_match_score(func['description'], query_terms)
        if desc_score > 0:
            score += desc_score * 1.5
            matches.append(f"Description match: {desc_score:.2f}")
        
        # Add to results if score is positive
        if score > 0:
            results.append({
                'function': func,
                'score': score,
                'matches': matches
            })
    
    # Sort by score and limit results
    results = sorted(results, key=lambda x: x['score'], reverse=True)[:limit]
    
    return results


def execute_registry_function(registry, function_name, *args, **kwargs):
    """
    Executes a function from any registered library.
    
    Args:
        registry (dict): Library registry
        function_name (str): Function name or qualified name
        *args: Positional arguments
        **kwargs: Keyword arguments
        
    Returns:
        any: Function result
    """
    #H->AI::UniversalExecution: (Execute function from any registered library)
    
    # Check if function exists in registry
    if function_name in registry['functions']:
        func_info = registry['functions'][function_name]
        library_name = func_info['library']
        
        # Get the library
        if library_name in registry['libraries']:
            # Get original library object (would be stored in a separate field in real implementation)
            library = None
            for lib_obj in registry.get('library_objects', []):
                if lib_obj['namespace'] == library_name:
                    library = lib_obj
                    break
            
            if library:
                # Execute the function
                return execute_library_function(library, function_name, *args, **kwargs)
    
    # Check if function exists by unqualified name or alias
    for qualified_name, func_info in registry['functions'].items():
        if (function_name == func_info['name'] or 
            function_name in func_info['aliases']):
            
            library_name = func_info['library']
            
            # Get the library
            if library_name in registry['libraries']:
                # Get original library object
                library = None
                for lib_obj in registry.get('library_objects', []):
                    if lib_obj['namespace'] == library_name:
                        library = lib_obj
                        break
                
                if library:
                    # Execute the function
                    return execute_library_function(library, qualified_name, *args, **kwargs)
    
    raise ValueError(f"Function '{function_name}' not found in any registered library")


def suggest_function_combinations(registry, goal, data_types=None, limit=5):
    """
    Suggests combinations of functions from different libraries to achieve a goal.
    
    Args:
        registry (dict): Library registry
        goal (str): User goal
        data_types (list, optional): Types of data being processed
        limit (int): Maximum combinations to suggest
        
    Returns:
        list: Suggested function combinations
    """
    #H->AI::CombinationSuggestions: (Suggest multi-function workflows)
    
    goal_terms = goal.lower().split()
    
    # Find functions relevant to the goal
    relevant_functions = []
    for func_name, func in registry['functions'].items():
        relevance = (
            calculate_text_match_score(func['description'], goal_terms) * 2 +
            calculate_text_match_score(func['name'], goal_terms) * 1.5 +
            calculate_text_match_score(func['category'], goal_terms)
        )
        
        # Check data type relevance if specified
        if data_types:
            for data_type in data_types:
                data_type_terms = data_type.lower().split()
                relevance += calculate_text_match_score(func['description'], data_type_terms)
        
        if relevance > 0:
            relevant_functions.append({
                'function': func,
                'relevance': relevance
            })
    
    # Sort by relevance
    relevant_functions = sorted(relevant_functions, key=lambda x: x['relevance'], reverse=True)
    
    # Generate potential combinations
    combinations = []
    
    # Look for input-processing-output patterns
    input_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower() 
                                                      for term in ['input', 'read', 'load', 'parse'])]
    
    process_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower() 
                                                        for term in ['process', 'analyze', 'calculate', 'transform'])]
    
    output_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower() 
                                                       for term in ['output', 'generate', 'create', 'format', 'report'])]
    
    # Create typical workflows
    for i in range(min(limit, max(len(input_funcs), 1) * max(len(process_funcs), 1) * max(len(output_funcs), 1))):
        input_func = input_funcs[i % max(len(input_funcs), 1)] if input_funcs else None
        process_func = process_funcs[i % max(len(process_funcs), 1)] if process_funcs else None
        output_func = output_funcs[i % max(len(output_funcs), 1)] if output_funcs else None
        
        if input_func or process_func or output_func:
            workflow = {
                'steps': [],
                'total_relevance': 0,
                'description': f"Workflow to {goal}"
            }
            
            if input_func:
                workflow['steps'].append({
                    'function': input_func['function'],
                    'role': 'input',
                    'relevance': input_func['relevance']
                })
                workflow['total_relevance'] += input_func['relevance']
            
            if process_func:
                workflow['steps'].append({
                    'function': process_func['function'],
                    'role': 'processing',
                    'relevance': process_func['relevance']
                })
                workflow['total_relevance'] += process_func['relevance']
            
            if output_func:
                workflow['steps'].append({
                    'function': output_func['function'],
                    'role': 'output',
                    'relevance': output_func['relevance']
                })
                workflow['total_relevance'] += output_func['relevance']
            
            combinations.append(workflow)
    
    # Sort by total relevance
    combinations = sorted(combinations, key=lambda x: x['total_relevance'], reverse=True)
    
    return combinations[:limit]

Library Analytics and Usage Optimization

python

def analyze_library_usage(library, usage_logs):
    """
    Analyzes usage patterns of a function library.
    
    Args:
        library (dict): Function library
        usage_logs (list): Function usage logs
        
    Returns:
        dict: Usage analytics
    """
    #H->AI::UsageAnalytics: (Analyze function usage patterns)
    
    analytics = {
        'total_calls': len(usage_logs),
        'function_usage': {},
        'category_usage': {},
        'usage_over_time': {},
        'common_patterns': [],
        'unused_functions': [],
        'most_used_functions': [],
        'potential_optimizations': []
    }
    
    # Process usage logs
    for log in usage_logs:
        function_name = log.get('function_name')
        timestamp = log.get('timestamp')
        success = log.get('success', True)
        execution_time = log.get('execution_time', 0)
        
        # Skip invalid logs
        if not function_name or not timestamp:
            continue
        
        # Track function usage
        if function_name not in analytics['function_usage']:
            analytics['function_usage'][function_name] = {
                'call_count': 0,
                'success_count': 0,
                'failure_count': 0,
                'avg_execution_time': 0,
                'total_execution_time': 0
            }
        
        analytics['function_usage'][function_name]['call_count'] += 1
        
        if success:
            analytics['function_usage'][function_name]['success_count'] += 1
        else:
            analytics['function_usage'][function_name]['failure_count'] += 1
        
        # Update execution time stats
        func_stats = analytics['function_usage'][function_name]
        func_stats['total_execution_time'] += execution_time
        func_stats['avg_execution_time'] = func_stats['total_execution_time'] / func_stats['call_count']
        
        # Track category usage if function exists in library
        if function_name in library['functions']:
            category = library['functions'][function_name]['category']
            
            if category not in analytics['category_usage']:
                analytics['category_usage'][category] = 0
            
            analytics['category_usage'][category] += 1
        
        # Track usage over time (by date)
        date = timestamp.split('T')[0]
        if date not in analytics['usage_over_time']:
            analytics['usage_over_time'][date] = 0
        
        analytics['usage_over_time'][date] += 1
    
    # Identify unused functions
    used_functions = set(analytics['function_usage'].keys())
    all_functions = set(library['functions'].keys())
    analytics['unused_functions'] = list(all_functions - used_functions)
    
    # Find most used functions
    sorted_functions = sorted(
        analytics['function_usage'].items(),
        key=lambda x: x[1]['call_count'],
        reverse=True
    )
    
    analytics['most_used_functions'] = sorted_functions[:10]
    
    # Identify common usage patterns (sequences of function calls)
    if len(usage_logs) >= 2:
        call_sequences = []
        for i in range(len(usage_logs) - 1):
            current = usage_logs[i].get('function_name')
            next_func = usage_logs[i+1].get('function_name')
            
            if current and next_func:
                call_sequences.append((current, next_func))
        
        # Count sequence frequencies
        sequence_counts = {}
        for seq in call_sequences:
            if seq not in sequence_counts:
                sequence_counts[seq] = 0
            sequence_counts[seq] += 1
        
        # Find most common sequences
        common_sequences = sorted(
            sequence_counts.items(),
            key=lambda x: x[1],
            reverse=True
        )
        
        analytics['common_patterns'] = common_sequences[:5]
    
    # Generate optimization suggestions
    # 1. Suggest combining frequently used together functions
    for seq, count in analytics.get('common_patterns', []):
        if count > 3:  # Arbitrary threshold
            analytics['potential_optimizations'].append({
                'type': 'combine_functions',
                'functions': seq,
                'frequency': count,
                'suggestion': f"Consider creating a combined function for {seq[0]} and {seq[1]}"
            })
    
    # 2. Suggest optimizing slow functions
    for func_name, stats in analytics['function_usage'].items():
        if stats['avg_execution_time'] > 1.0:  # Arbitrary threshold (1 second)
            analytics['potential_optimizations'].append({
                'type': 'optimize_performance',
                'function': func_name,
                'avg_time': stats['avg_execution_time'],
                'call_count': stats['call_count'],
                'suggestion': f"Consider optimizing {func_name} for better performance"
            })
    
    # 3. Suggest removing or deprecating unused functions
    if len(analytics['unused_functions']) > 0:
        analytics['potential_optimizations'].append({
            'type': 'remove_unused',
            'functions': analytics['unused_functions'],
            'suggestion': "Consider removing or deprecating unused functions"
        })
    
    return analytics


def optimize_library(library, optimization_suggestions):
    """
    Applies optimization suggestions to a function library.
    
    Args:
        library (dict): Function library
        optimization_suggestions (list): Suggestions to apply
        
    Returns:
        dict: Optimized library
    """
    #H->AI::LibraryOptimization: (Apply performance improvements to library)
    
    # Create a copy of the library to avoid modifying the original
    from copy import deepcopy
    optimized_library = deepcopy(library)
    
    # Track applied optimizations
    applied_optimizations = []
    
    for suggestion in optimization_suggestions:
        suggestion_type = suggestion.get('type')
        
        if suggestion_type == 'combine_functions':
            # Create a combined function
            functions_to_combine = suggestion.get('functions', [])
            
            if len(functions_to_combine) != 2:
                continue
            
            func1_name, func2_name = functions_to_combine
            
            # Check if both functions exist
            if (func1_name not in optimized_library['functions'] or 
                func2_name not in optimized_library['functions']):
                continue
            
            func1 = optimized_library['functions'][func1_name]['function']
            func2 = optimized_library['functions'][func2_name]['function']
            
            # Create combined function
            def combined_function(*args, **kwargs):
                """Combined function created through library optimization."""
                result1 = func1(*args, **kwargs)
                result2 = func2(result1, **kwargs)
                return result2
            
            # Generate a name for the combined function
            func1_short = func1_name.split('.')[-1]
            func2_short = func2_name.split('.')[-1]
            combined_name = f"{func1_short}_then_{func2_short}"
            
            # Add to library
            add_function_to_library(
                library=optimized_library,
                function=combined_function,
                name=combined_name,
                category="optimized",
                description=f"Combined function that executes {func1_name} followed by {func2_name}",
                examples=[f"{optimized_library['namespace']}.{combined_name}(data)"],
                aliases=[f"{func1_short}{func2_short}"]
            )
            
            applied_optimizations.append({
                'type': 'combined_functions',
                'original_functions': [func1_name, func2_name],
                'new_function': combined_name
            })
        
        elif suggestion_type == 'optimize_performance':
            # Apply performance optimization (demonstration only - real implementation would vary)
            func_name = suggestion.get('function')
            
            if func_name not in optimized_library['functions']:
                continue
            
            # Create an "optimized" version (in reality, this would involve actual optimization)
            original_func = optimized_library['functions'][func_name]['function']
            
            def optimized_function(*args, **kwargs):
                """Performance-optimized version of the original function."""
                # In a real implementation, this would include actual optimization logic
                return original_func(*args, **kwargs)
            
            # Replace the original function
            optimized_library['functions'][func_name]['function'] = optimized_function
            optimized_library['functions'][func_name]['last_updated'] = get_current_timestamp()
            
            applied_optimizations.append({
                'type': 'performance_optimized',
                'function': func_name
            })
        
        elif suggestion_type == 'remove_unused':
            # Mark unused functions as deprecated
            unused_functions = suggestion.get('functions', [])
            
            for func_name in unused_functions:
                if func_name in optimized_library['functions']:
                    # Mark as deprecated (instead of removing)
                    optimized_library['functions'][func_name]['deprecated'] = True
                    optimized_library['functions'][func_name]['last_updated'] = get_current_timestamp()
                    
                    applied_optimizations.append({
                        'type': 'marked_deprecated',
                        'function': func_name
                    })
    
    # Update library metadata
    optimized_library['metadata']['last_updated'] = get_current_timestamp()
    optimized_library['metadata']['optimizations'] = applied_optimizations
    
    # Increment version
    version_parts = optimized_library['metadata']['version'].split('.')
    if len(version_parts) >= 2:
        version_parts[-1] = str(int(version_parts[-1]) + 1)
        optimized_library['metadata']['version'] = '.'.join(version_parts)
    
    return optimized_library

Integration with CRAFT Principles

Function Libraries align with several CRAFT principles:

Structure Enables Freedom: By providing structured organization for functions, libraries make it easier to discover and use the right tools for the job, which paradoxically creates more creative freedom.
Code as Conversation: Libraries transform collections of functions into richly annotated, searchable resources that bridge the gap between code and natural language interaction.
Cumulative Intelligence: Libraries preserve and accumulate knowledge about function relationships, usage patterns, and examples, creating an evolving repository of functional capabilities.
Human-AI Partnership: Library discovery mechanisms like searching, recommendations, and visualization help both humans and AI understand available capabilities and how to use them effectively.
Accessibility Through Familiarity: Libraries use familiar organizational concepts like categories and namespaces while making them accessible through natural language interactions.
Conservation of Tokens: Libraries reduce the need to repeatedly explain function capabilities by providing standardized documentation and examples.

Implementation Recommendations

Start with Clear Categorization: Begin by carefully defining the categories that will organize your functions, ensuring they're intuitive and non-overlapping.
Include Rich Metadata: Always provide detailed descriptions, examples, and aliases when adding functions to a library.
Build Search First: Implement robust search capabilities early to ensure functions remain discoverable as the library grows.
Design for Extension: Structure libraries with extension in mind, making it easy to create specialized versions without modifying the original.
Use Consistent Documentation: Follow a consistent documentation pattern for all functions to make the library more predictable and usable.
Leverage Function Groups: Create logical function groups to highlight related functions that are commonly used together.
Track Usage Patterns: Implement usage logging to identify which functions are most valuable and which might need improvement.
Visualize Relationships: Create visualizations of library structure to help users understand function relationships.
Prioritize Discoverability: Focus on making functions easy to find and understand, even for users unfamiliar with the library.
Regular Optimization: Periodically review and optimize libraries based on usage patterns and feedback.

Practical Integration with Other CRAFT Functions

python

# Integration with Context-Aware Functions
def create_context_aware_library(library_name, description=None, context_variables=None):
    """
    Creates a function library with context awareness.
    
    Args:
        library_name (str): Name of the library
        description (str, optional): Library description
        context_variables (list, optional): Context variables to track
        
    Returns:
        dict: Context-aware library
    """
    # Create standard library
    library = create_function_library(
        library_name=library_name,
        description=description
    )
    
    # Initialize context tracking
    if context_variables is None:
        context_variables = ['user_preferences', 'environment', 'previous_results']
    
    # Add context tracking to the library
    library['context'] = {
        'variables': context_variables,
        'current_context': {},
        'context_history': []
    }
    
    # Add context-aware function execution
    original_execute = execute_library_function
    
    def context_aware_execute(library, function_name, *args, **kwargs):
        """Context-aware function execution."""
        # Extract context
        context = kwargs.pop('context', {})
        update_context = kwargs.pop('update_context', True)
        
        # Update current context
        if update_context:
            for var in library['context']['variables']:
                if var in context:
                    library['context']['current_context'][var] = context[var]
        
        # Execute function
        result = original_execute(library, function_name, *args, **kwargs)
        
        # Update context with result
        if update_context:
            library['context']['context_history'].append({
                'function': function_name,
                'timestamp': get_current_timestamp(),
                'context_before': library['context']['current_context'].copy(),
                'result_summary': str(result)[:100] + ('...' if len(str(result)) > 100 else '')
            })
            
            # Update context with result
            library['context']['current_context']['previous_results'] = library['context']['current_context'].get('previous_results', [])
            library['context']['current_context']['previous_results'].append({
                'function': function_name,
                'timestamp': get_current_timestamp()
            })
        
        return result
    
    # Replace execution function
    library['execute_function'] = context_aware_execute
    
    # Add context management methods
    library['get_context'] = lambda: library['context']['current_context']
    library['update_context'] = lambda updates: library['context']['current_context'].update(updates)
    library['clear_context'] = lambda: library['context']['current_context'].clear()
    
    return library


# Integration with Collaborative Function Building
def create_collaborative_library(library_name, description=None, collaborator_roles=None):
    """
    Creates a function library with collaborative development capabilities.
    
    Args:
        library_name (str): Name of the library
        description (str, optional): Library description
        collaborator_roles (dict, optional): Defined roles for collaborators
        
    Returns:
        dict: Collaborative library
    """
    # Create standard library
    library = create_function_library(
        library_name=library_name,
        description=description
    )
    
    # Set default collaborator roles if not provided
    if collaborator_roles is None:
        collaborator_roles = {
            "maintainer": "Library maintainer with full access",
            "contributor": "Can propose function additions or changes",
            "reviewer": "Reviews and approves function changes"
        }
    
    # Add collaborative structure
    library['collaboration'] = {
        'roles': collaborator_roles,
        'collaborators': {},
        'proposed_changes': [],
        'activity_log': []
    }
    
    # Add collaborative methods
    library['add_collaborator'] = lambda name, role: add_collaborator_to_library(library, name, role)
    library['propose_function'] = lambda function, name, description, author: propose_function_to_library(library, function, name, description, author)
    library['review_proposal'] = lambda proposal_id, reviewer, decision, comments=None: review_library_proposal(library, proposal_id, reviewer, decision, comments)
    
    return library


def add_collaborator_to_library(library, name, role):
    """
    Adds a collaborator to a collaborative library.
    
    Args:
        library (dict): Collaborative library
        name (str): Collaborator name
        role (str): Assigned role
        
    Returns:
        bool: Success status
    """
    # Validate role
    if role not in library['collaboration']['roles']:
        return False
    
    # Add collaborator
    library['collaboration']['collaborators'][name] = {
        'role': role,
        'added': get_current_timestamp(),
        'contributions': []
    }
    
    # Log activity
    library['collaboration']['activity_log'].append({
        'action': 'add_collaborator',
        'collaborator': name,
        'role': role,
        'timestamp': get_current_timestamp()
    })
    
    return True


def propose_function_to_library(library, function, name, description, author):
    """
    Proposes adding or changing a function in a collaborative library.
    
    Args:
        library (dict): Collaborative library
        function (function): Function implementation
        name (str): Function name
        description (str): Function description
        author (str): Proposal author
        
    Returns:
        int: Proposal ID
    """
    # Validate author
    if author not in library['collaboration']['collaborators']:
        return -1
    
    # Create proposal
    proposal_id = len(library['collaboration']['proposed_changes'])
    proposal = {
        'id': proposal_id,
        'type': 'add_function' if name not in library['functions'] else 'update_function',
        'function': function,
        'name': name,
        'description': description,
        'author': author,
        'status': 'pending',
        'timestamp': get_current_timestamp(),
        'reviews': []
    }
    
    # Add proposal
    library['collaboration']['proposed_changes'].append(proposal)
    
    # Log activity
    library['collaboration']['activity_log'].append({
        'action': 'propose_function',
        'proposal_id': proposal_id,
        'function_name': name,
        'author': author,
        'timestamp': get_current_timestamp()
    })
    
    # Track contribution
    library['collaboration']['collaborators'][author]['contributions'].append({
        'type': 'proposal',
        'id': proposal_id,
        'timestamp': get_current_timestamp()
    })
    
    return proposal_id


def review_library_proposal(library, proposal_id, reviewer, decision, comments=None):
    """
    Reviews a proposed function change.
    
    Args:
        library (dict): Collaborative library
        proposal_id (int): Proposal ID
        reviewer (str): Reviewer name
        decision (str): Review decision (approve, reject, request_changes)
        comments (str, optional): Review comments
        
    Returns:
        bool: Success status
    """
    # Validate reviewer
    if reviewer not in library['collaboration']['collaborators']:
        return False
    
    # Validate proposal
    if proposal_id < 0 or proposal_id >= len(library['collaboration']['proposed_changes']):
        return False
    
    proposal = library['collaboration']['proposed_changes'][proposal_id]
    
    # Add review
    review = {
        'reviewer': reviewer,
        'decision': decision,
        'comments': comments,
        'timestamp': get_current_timestamp()
    }
    
    proposal['reviews'].append(review)
    
    # Update proposal status
    if decision == 'approve':
        # Check if the reviewer has approval rights
        reviewer_role = library['collaboration']['collaborators'][reviewer]['role']
        can_approve = reviewer_role in ['maintainer', 'reviewer']
        
        if can_approve:
            # Apply the function change
            if proposal['type'] == 'add_function':
                add_function_to_library(
                    library=library,
                    function=proposal['function'],
                    name=proposal['name'],
                    description=proposal['description']
                )
            elif proposal['type'] == 'update_function':
                # Update existing function
                if proposal['name'] in library['functions']:
                    library['functions'][proposal['name']]['function'] = proposal['function']
                    library['functions'][proposal['name']]['description'] = proposal['description']
                    library['functions'][proposal['name']]['last_updated'] = get_current_timestamp()
            
            proposal['status'] = 'approved'
        
    elif decision == 'reject':
        proposal['status'] = 'rejected'
    
    elif decision == 'request_changes':
        proposal['status'] = 'changes_requested'
    
    # Log activity
    library['collaboration']['activity_log'].append({
        'action': 'review_proposal',
        'proposal_id': proposal_id,
        'reviewer': reviewer,
        'decision': decision,
        'timestamp': get_current_timestamp()
    })
    
    # Track contribution
    library['collaboration']['collaborators'][reviewer]['contributions'].append({
        'type': 'review',
        'proposal_id': proposal_id,
        'decision': decision,
        'timestamp': get_current_timestamp()
    })
    
    return True

Real-World Application Example

python

# Example: Enterprise Data Processing Framework with Function Libraries

# Create core data processing library
data_processing = create_function_library(
    library_name="Enterprise Data Processing",
    description="Core functions for data transformation and analysis",
    categories=["import", "transform", "analyze", "export"]
)

# Define essential data processing functions
def import_csv(file_path, headers=True, delimiter=",", encoding="utf-8"):
    """
    Imports data from a CSV file.
    
    Args:
        file_path (str): Path to the CSV file
        headers (bool): Whether the file has headers
        delimiter (str): Field delimiter
        encoding (str): File encoding
        
    Returns:
        dict: Imported data structure
    """
    # Simplified implementation
    print(f"Importing {file_path} as CSV with delimiter={delimiter}, headers={headers}, encoding={encoding}")
    
    # In a real implementation, would read the file
    # Simulated data for example
    result = {
        "source": file_path,
        "format": "csv",
        "row_count": 1000,
        "columns": ["id", "name", "value", "date"],
        "data": [
            {"id": 1, "name": "Item 1", "value": 10.5, "date": "2025-01-15"},
            {"id": 2, "name": "Item 2", "value": 20.3, "date": "2025-02-20"},
            # ... more rows would be here in real implementation
        ]
    }
    
    return result

def import_json(file_path, encoding="utf-8"):
    """
    Imports data from a JSON file.
    
    Args:
        file_path (str): Path to the JSON file
        encoding (str): File encoding
        
    Returns:
        dict: Imported data structure
    """
    # Simplified implementation
    print(f"Importing {file_path} as JSON with encoding={encoding}")
    
    # Simulated data
    result = {
        "source": file_path,
        "format": "json",
        "structure": "hierarchical",
        "data": {
            "metadata": {
                "created": "2025-03-15",
                "version": "1.2"
            },
            "items": [
                {"id": 1, "name": "Item 1", "value": 10.5, "date": "2025-01-15"},
                {"id": 2, "name": "Item 2", "value": 20.3, "date": "2025-02-20"},
                # ... more items would be here in real implementation
            ]
        }
    }
    
    return result

def filter_data(data, filter_criteria):
    """
    Filters data based on specified criteria.
    
    Args:
        data (dict): Data structure to filter
        filter_criteria (dict): Filtering criteria
        
    Returns:
        dict: Filtered data
    """
    # Simplified implementation
    print(f"Filtering data with criteria: {filter_criteria}")
    
    filtered_result = {"source": data.get("source"), "format": data.get("format")}
    
    # Handle different data formats
    if data.get("format") == "csv":
        original_rows = data.get("data", [])
        filtered_rows = []
        
        for row in original_rows:
            include_row = True
            
            for field, criteria in filter_criteria.items():
                if field in row:
                    # Handle different criteria types
                    if isinstance(criteria, dict):
                        # Operator-based criteria
                        if "gt" in criteria and row[field] <= criteria["gt"]:
                            include_row = False
                        if "lt" in criteria and row[field] >= criteria["lt"]:
                            include_row = False
                        if "eq" in criteria and row[field] != criteria["eq"]:
                            include_row = False
                    else:
                        # Direct value comparison
                        if row[field] != criteria:
                            include_row = False
            
            if include_row:
                filtered_rows.append(row)
        
        filtered_result["data"] = filtered_rows
        filtered_result["row_count"] = len(filtered_rows)
        filtered_result["columns"] = data.get("columns")
    
    elif data.get("format") == "json":
        # Handle hierarchical JSON data
        if "items" in data.get("data", {}):
            original_items = data["data"]["items"]
            filtered_items = []
            
            for item in original_items:
                include_item = True
                
                for field, criteria in filter_criteria.items():
                    if field in item:
                        # Handle different criteria types
                        if isinstance(criteria, dict):
                            # Operator-based criteria
                            if "gt" in criteria and item[field] <= criteria["gt"]:
                                include_item = False
                            if "lt" in criteria and item[field] >= criteria["lt"]:
                                include_item = False
                            if "eq" in criteria and item[field] != criteria["eq"]:
                                include_item = False
                        else:
                            # Direct value comparison
                            if item[field] != criteria:
                                include_item = False
                
                if include_item:
                    filtered_items.append(item)
            
            filtered_result["data"] = {
                "metadata": data["data"].get("metadata", {}),
                "items": filtered_items
            }
    
    return filtered_result

def aggregate_data(data, group_by, aggregations):
    """
    Aggregates data by specified fields and calculations.
    
    Args:
        data (dict): Data structure to aggregate
        group_by (str or list): Field(s) to group by
        aggregations (dict): Aggregation functions to apply
        
    Returns:
        dict: Aggregated data
    """
    # Simplified implementation
    print(f"Aggregating data by {group_by} with {aggregations}")
    
    # Convert group_by to list if it's a single field
    if isinstance(group_by, str):
        group_by = [group_by]
    
    result = {
        "source": data.get("source"),
        "aggregated": True,
        "group_by": group_by,
        "groups": {}
    }
    
    # Extract rows based on data format
    rows = []
    if data.get("format") == "csv":
        rows = data.get("data", [])
    elif data.get("format") == "json" and "items" in data.get("data", {}):
        rows = data["data"]["items"]
    
    # Group data
    for row in rows:
        # Create group key
        group_values = []
        for field in group_by:
            group_values.append(str(row.get(field, "")))
        
        group_key = "|".join(group_values)
        
        # Initialize group if not exists
        if group_key not in result["groups"]:
            result["groups"][group_key] = {
                "key": dict(zip(group_by, [row.get(field) for field in group_by])),
                "count": 0,
                "aggregates": {}
            }
        
        # Update group
        group = result["groups"][group_key]
        group["count"] += 1
        
        # Apply aggregations
        for field, agg_funcs in aggregations.items():
            if field in row:
                field_value = row[field]
                
                # Initialize aggregates for field
                if field not in group["aggregates"]:
                    group["aggregates"][field] = {}
                
                # Apply aggregation functions
                for agg_func in agg_funcs:
                    if agg_func == "sum":
                        if "sum" not in group["aggregates"][field]:
                            group["aggregates"][field]["sum"] = 0
                        
                        try:
                            group["aggregates"][field]["sum"] += float(field_value)
                        except (ValueError, TypeError):
                            pass
                    
                    elif agg_func == "avg":
                        if "sum" not in group["aggregates"][field]:
                            group["aggregates"][field]["sum"] = 0
                        
                        try:
                            group["aggregates"][field]["sum"] += float(field_value)
                            group["aggregates"][field]["avg"] = group["aggregates"][field]["sum"] / group["count"]
                        except (ValueError, TypeError):
                            pass
                    
                    elif agg_func == "min":
                        try:
                            current_value = float(field_value)
                            if "min" not in group["aggregates"][field] or current_value < group["aggregates"][field]["min"]:
                                group["aggregates"][field]["min"] = current_value
                        except (ValueError, TypeError):
                            pass
                    
                    elif agg_func == "max":
                        try:
                            current_value = float(field_value)
                            if "max" not in group["aggregates"][field] or current_value > group["aggregates"][field]["max"]:
                                group["aggregates"][field]["max"] = current_value
                        except (ValueError, TypeError):
                            pass
    
    return result

def export_to_csv(data, file_path, headers=True, delimiter=","):
    """
    Exports data to a CSV file.
    
    Args:
        data (dict): Data to export
        file_path (str): Path to save the CSV file
        headers (bool): Whether to include headers
        delimiter (str): Field delimiter
        
    Returns:
        dict: Export result
    """
    # Simplified implementation
    print(f"Exporting data to {file_path} as CSV with delimiter={delimiter}, headers={headers}")
    
    # In real implementation, would write to file
    result = {
        "success": True,
        "file_path": file_path,
        "format": "csv",
        "rows_exported": 0
    }
    
    # Handle different input data formats
    if "data" in data and isinstance(data["data"], list):
        # CSV-like data
        result["rows_exported"] = len(data["data"])
    elif "data" in data and "items" in data["data"]:
        # JSON-like data
        result["rows_exported"] = len(data["data"]["items"])
    elif "groups" in data:
        # Aggregated data
        result["rows_exported"] = len(data["groups"])
    
    return result

# Add functions to the library
add_function_to_library(
    library=data_processing,
    function=import_csv,
    category="import",
    examples=[
        "data_processing.import_csv('data/sales.csv')",
        "data_processing.import_csv('data/transactions.csv', delimiter=';', encoding='latin-1')"
    ],
    aliases=["read_csv", "load_csv"]
)

add_function_to_library(
    library=data_processing,
    function=import_json,
    category="import",
    examples=[
        "data_processing.import_json('data/config.json')",
        "data_processing.import_json('data/api_response.json', encoding='utf-8')"
    ],
    aliases=["read_json", "load_json"]
)

add_function_to_library(
    library=data_processing,
    function=filter_data,
    category="transform",
    examples=[
        "data_processing.filter_data(sales_data, {'region': 'North'})",
        "data_processing.filter_data(transactions, {'amount': {'gt': 1000}, 'status': 'completed'})"
    ],
    aliases=["query", "filter"]
)

add_function_to_library(
    library=data_processing,
    function=aggregate_data,
    category="analyze",
    examples=[
        "data_processing.aggregate_data(sales_data, 'region', {'amount': ['sum', 'avg']})",
        "data_processing.aggregate_data(transactions, ['category', 'status'], {'value': ['min', 'max', 'avg']})"
    ],
    aliases=["summarize", "group_by"]
)

add_function_to_library(
    library=data_processing,
    function=export_to_csv,
    category="export",
    examples=[
        "data_processing.export_to_csv(filtered_data, 'output/filtered_sales.csv')",
        "data_processing.export_to_csv(summary, 'output/summary.csv', delimiter=';')"
    ],
    aliases=["save_csv", "write_csv"]
)

# Create an extension for department-specific data processing
finance_data_processing = extend_library(
    base_library=data_processing,
    extension_name="Finance",
    description="Specialized data processing for financial data"
)

# Define finance-specific functions
def calculate_financial_metrics(data, metrics=None):
    """
    Calculates financial metrics based on data.
    
    Args:
        data (dict): Financial data
        metrics (list, optional): Specific metrics to calculate
        
    Returns:
        dict: Calculated financial metrics
    """
    if metrics is None:
        metrics = ["growth", "ratio", "profitability"]
    
    # Simplified implementation
    print(f"Calculating financial metrics: {metrics}")
    
    # Extract rows based on data format
    rows = []
    if data.get("format") == "csv":
        rows = data.get("data", [])
    elif data.get("format") == "json" and "items" in data.get("data", {}):
        rows = data["data"]["items"]
    
    result = {
        "source": data.get("source"),
        "metrics": {}
    }
    
    # Simulate metric calculations (simplified)
    if "growth" in metrics:
        result["metrics"]["growth"] = {
            "revenue_growth": 0.15,
            "profit_growth": 0.08,
            "customer_growth": 0.12
        }
    
    if "ratio" in metrics:
        result["metrics"]["ratio"] = {
            "current_ratio": 1.8,
            "debt_to_equity": 0.4,
            "return_on_assets": 0.09
        }
    
    if "profitability" in metrics:
        result["metrics"]["profitability"] = {
            "gross_margin": 0.65,
            "net_margin": 0.12,
            "operating_margin": 0.18
        }
    
    return result

def generate_financial_report(data, report_type="summary", period=None):
    """
    Generates a formatted financial report.
    
    Args:
        data (dict): Financial data or metrics
        report_type (str): Type of report to generate
        period (str, optional): Reporting period
        
    Returns:
        str: Formatted financial report
    """
    # Simplified implementation
    print(f"Generating {report_type} financial report for period: {period}")
    
    # Initialize report
    report = f"# Financial {report_type.title()} Report\n\n"
    
    if period:
        report += f"Period: {period}\n\n"
    
    # Add report content based on type
    if report_type == "summary":
        report += "## Key Metrics\n\n"
        
        # Add metrics if available
        if "metrics" in data:
            for category, metrics in data["metrics"].items():
                report += f"### {category.title()} Metrics\n\n"
                for name, value in metrics.items():
                    formatted_name = " ".join(word.title() for word in name.split("_"))
                    
                    # Format value based on type
                    if isinstance(value, float):
                        # Format as percentage if likely a ratio
                        if name.endswith("growth") or name.endswith("margin") or name == "return_on_assets":
                            formatted_value = f"{value * 100:.2f}%"
                        else:
                            formatted_value = f"{value:.2f}"
                    else:
                        formatted_value = str(value)
                    
                    report += f"- **{formatted_name}**: {formatted_value}\n"
                
                report += "\n"
    
    elif report_type == "detailed":
        report += "## Detailed Financial Analysis\n\n"
        
        # Add metrics with more detailed explanation
        if "metrics" in data:
            for category, metrics in data["metrics"].items():
                report += f"### {category.title()} Analysis\n\n"
                
                for name, value in metrics.items():
                    formatted_name = " ".join(word.title() for word in name.split("_"))
                    
                    # Format value based on type
                    if isinstance(value, float):
                        # Format as percentage if likely a ratio
                        if name.endswith("growth") or name.endswith("margin") or name == "return_on_assets":
                            formatted_value = f"{value * 100:.2f}%"
                        else:
                            formatted_value = f"{value:.2f}"
                    else:
                        formatted_value = str(value)
                    
                    report += f"#### {formatted_name}\n\n"
                    report += f"Value: **{formatted_value}**\n\n"
                    
                    # Add explanations (simplified)
                    if name == "revenue_growth":
                        report += "Revenue growth measures the increase in revenue over the given period. "
                        if value > 0.1:
                            report += "Current growth is strong, indicating successful market expansion.\n\n"
                        elif value > 0:
                            report += "Current growth is positive but moderate.\n\n"
                        else:
                            report += "Current growth is negative, suggesting potential market challenges.\n\n"
                    
                    elif name == "current_ratio":
                        report += "Current ratio measures the company's ability to pay short-term obligations. "
                        if value > 2:
                            report += "The ratio indicates strong liquidity position.\n\n"
                        elif value > 1:
                            report += "The ratio is healthy, indicating adequate liquidity.\n\n"
                        else:
                            report += "The ratio is below 1, indicating potential liquidity concerns.\n\n"
    
    elif report_type == "compliance":
        report += "## Compliance Report\n\n"
        report += "This report examines financial data for regulatory compliance purposes.\n\n"
        
        # Add simulated compliance checks
        report += "### Compliance Status\n\n"
        report += "- **Data Completeness**: Satisfactory\n"
        report += "- **Reporting Timeliness**: Compliant\n"
        report += "- **Audit Trail**: Complete\n\n"
        
        report += "### Exceptions\n\n"
        report += "No compliance exceptions detected.\n\n"
    
    # Add report footer
    report += "---\n"
    report += "Generated by Enterprise Finance Data Processing Module\n"
    report += f"Date: {get_current_timestamp().split('T')[0]}\n"
    
    return report

# Add functions to the finance library
add_function_to_library(
    library=finance_data_processing,
    function=calculate_financial_metrics,
    category="analyze",
    examples=[
        "data_processing.finance.calculate_financial_metrics(financial_data)",
        "data_processing.finance.calculate_financial_metrics(quarterly_data, metrics=['growth', 'profitability'])"
    ],
    aliases=["financial_analysis", "compute_metrics"]
)

add_function_to_library(
    library=finance_data_processing,
    function=generate_financial_report,
    category="export",
    examples=[
        "data_processing.finance.generate_financial_report(metrics_data)",
        "data_processing.finance.generate_financial_report(financials, report_type='detailed', period='Q1 2025')"
    ],
    aliases=["financial_report", "create_report"]
)

# Create function groups
create_function_group(
    library=data_processing,
    group_name="ETL Pipeline",
    function_names=["import_csv", "filter_data", "export_to_csv"],
    description="Functions for basic extract-transform-load operations"
)

create_function_group(
    library=finance_data_processing,
    group_name="Financial Reporting",
    function_names=["calculate_financial_metrics", "generate_financial_report"],
    description="Functions for financial analysis and reporting"
)

# Example usage: Data processing workflow
# Step 1: Import data
# sales_data = execute_library_function(data_processing, "import_csv", "data/quarterly_sales.csv")

# Step 2: Filter relevant data
# filtered_data = execute_library_function(data_processing, "filter_data", sales_data, {"quarter": "Q1 2025"})

# Step 3: Calculate financial metrics
# financial_metrics = execute_library_function(finance_data_processing, "calculate_financial_metrics", filtered_data)

# Step 4: Generate quarterly report
# quarterly_report = execute_library_function(
#     finance_data_processing, 
#     "generate_financial_report", 
#     financial_metrics, 
#     "detailed", 
#     "Q1 2025"
# )

# Create a registry for enterprise-wide function discovery
registry = create_library_registry()
register_library(registry, data_processing)
register_library(registry, finance_data_processing)

# Example: Cross-library search
search_results = search_registry(registry, "export data report")
# for result in search_results:
#     print(f"{result['function']['qualified_name']} - Score: {result['score']:.2f}")
#     print(f"  {result['function']['description']}")
#     print(f"  From: {result['function']['library']}")
#     print()

# Example: Function combination suggestions
goal = "analyze quarterly financial data and create a compliance report"
combinations = suggest_function_combinations(registry, goal, ["financial data", "quarterly reports"])
# for i, combo in enumerate(combinations, 1):
#     print(f"Suggestion {i}:")
#     print(f"  Workflow: {combo['description']}")
#     print("  Steps:")
#     for step in combo['steps']:
#         print(f"    - {step['function']['qualified_name']} ({step['role']})")
#     print(f"  Relevance Score: {combo['total_relevance']:.2f}")
#     print()

Function Libraries provide a powerful way to organize, discover, and use functions within the CRAFT framework. By creating structured collections with consistent interfaces, rich documentation, and intuitive organization, they make it easier for both humans and AI to leverage the full power of available functions. Whether used for individual projects or enterprise-wide function ecosystems, libraries transform disparate functions into cohesive, accessible toolsets that can grow and evolve over time.

Sure thing, Richard—here’s an outline of the key ways we can rethink “functions” for CRAFT, mapped to your six principles:

CRAFT Function Definition
– Introduce a func block to encapsulate a reusable prompt pattern
– Mirrors Python’s def but in natural-language style
Parameterization as Context Injection
– Use named parameters (e.g. func translate(text, to_language)) to feed dynamic content into prompts
– Aligns with ACCESSIBILITY THROUGH FAMILIARITY
Return Templates for Structured Replies
– Define explicit “return” sections that specify the desired JSON/object shape of the AI’s output
– Enforces CONSERVATION OF TOKENS by cutting filler
Composable (Higher-Order) Prompt Chains
– Allow functions to accept other functions as arguments (e.g. summarize_then_translate = compose(summarize, translate))
– Leverages CODE AS CONVERSATION for dynamic workflows
Stateful Functions for CUMULATIVE INTELLIGENCE
– Embed a state object that persists across invocations (e.g. dialogue history, variable store)
– Builds on CUMULATIVE INTELLIGENCE by carrying forward context
Async & Streaming Functions
– Define async functions that let the AI stream partial results or ask follow-up questions
– Enhances HUMAN-AI PARTNERSHIP through interactive back-and-forth
Recursion & Iterative Refinement
– Enable a function to call itself with modified parameters (e.g. refine(text, depth=3))
– Supports layered reasoning while conserving tokens
Namespaces & Modules for Organization
– Group related functions into modules (e.g. nlp.translate, nlp.summarize)
– Provides STRUCTURE ENABLES FREEDOM by reducing overload
Decorators & Hooks
– Pre- and post-processing wrappers (e.g. @validate_input, @log_usage) to add checks or analytics
– Improves clarity and maintains token budgets
Token-Budgeted Signatures
– Optimize function signatures to only include essential parameters
– Directly addresses CONSERVATION OF TOKENS

Next, I can dive into detailed examples of each—just let me know which items you’d like to explore first!

You said:

Great, please expand on item 1 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 12 seconds

Here’s a deep dive into CRAFT Function Definitions—how they work, why they matter, and concrete examples you can start using today.

What Is a CRAFT Function?

A CRAFT function (func) is a named, self-contained block that packages up a reusable prompt pattern or workflow. It mirrors Python’s def syntax but is designed for natural-language clarity in AI chats.

Syntax Skeleton
craft
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func function_name(param1, param2=default, ...): """Optional docstring describing purpose""" prompt: "Your templated prompt here, using {param1}, {param2}, etc." return output_variable
Key Difference from Raw Prompts
Instead of pasting the same long prompt over and over, you define it once under func—then simply call it.

Benefits

Modularity & Reusability
Once defined, you can call the same function in multiple contexts without rewriting. Change the prompt in one place; every call picks up the update—just like a Python library function .
Clarity & Maintainability
Crowded chats become more readable when you hide complex instructions behind a clean function header. It’s akin to abstracting away implementation details in code, letting you focus on “what” rather than “how.”
Token Conservation
Functions let you reference long prompt bodies by name instead of re-sending dozens of lines each time. This directly supports CRAFT’s CONSERVATION OF TOKENS principle by cutting down repetition .
Version Control & Documentation
With an optional docstring under func, you document exactly what the function does and how to use it—facilitating CUMULATIVE INTELLIGENCE across sessions and collaborators.

Anatomy of a CRAFT Function

Header
- Begins with func
- Includes positional and keyword parameters
craft
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func summarize(text, style="concise"):
Docstring (Optional)
- Triple-quoted natural-language description
craft
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"""Summarize `text` in two sentences using the given style."""
Prompt Body
- The meat of your instruction, with placeholders matching parameter names
craft
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prompt: "Please summarize the following text in two sentences ({style}):\n\n{text}"
Return Clause
- Declares the output variable(s) you expect back
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return summary

Example: A Summarization Function

craft

CopyEdit

func summarize(text, style="concise"): """Summarize `text` in two sentences using the given style.""" prompt: "Summarize the following in two sentences ({style}):\n\n{text}" return summary

Usage in Chat:

craft

CopyEdit

# Call with default style my_summary = summarize(article_text) # Call with custom style detailed_summary = summarize(article_text, style="detailed")

Each call sends only the function name and parameters, not the full prompt—saving tokens and keeping the chat tidy.

Mapping to CRAFT Principles

Structure Enables Freedom
By wrapping complex logic in func, you remove repetitive clutter and free your mind for creativity .
Code as Conversation
Functions turn natural-language chats into a semi-code interface, letting you “call” behaviors instead of re-typing them.
Cumulative Intelligence
Defined functions persist across sessions (via your BUILDINGCRAFT file), so you don’t reinvent the wheel each time.
Accessibility through Familiarity
Python-style func is immediately approachable for anyone with basic coding experience—and even newbies can learn the pattern quickly.
Conservation of Tokens
Only parameters and function names traverse the token limit, instead of entire paragraphs of instructions.

Next Steps

Try It Out: Pick a recurring prompt in your workflow (e.g., “translate,” “analyze sentiment,” “generate blog outline”) and wrap it in a func block.
Document It: Add a docstring describing expected inputs, outputs, and any constraints.
Share & Iterate: Include your new function in the PROJECT_FUNCTIONS section of your BUILDINGCRAFT file so it’s available in every session.

Let me know if you’d like a second example—perhaps for a translation or formatting function!

Sources

You said:

Great, please expand on item 2 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 10 seconds

Here’s a deep dive into Parameterization as Context Injection—how we turn function parameters into dynamic prompt inputs, why it matters, and concrete examples to get you started.

What Is Parameterization as Context Injection?

Instead of hard-coding every piece of context into a prompt, you define named parameters in your func signature—then inject those values at call-time. Think of it like ordering coffee: you specify “latte, extra shot, oat milk,” rather than the barista guessing every detail each time.

craft

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func translate(text, to_language): """Translate `text` into the specified `to_language`.""" prompt: "Please translate the following into {to_language}:\n\n{text}" return translated_text

At invocation, you only supply the two slots—text and to_language—and the CRAFT engine fills them in for you.

Why This Matters

Dynamic Customization
You can reuse one function for countless scenarios simply by swapping parameters. Need French today and Japanese tomorrow? Call
craft
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translate(report, "French") translate(report, "Japanese")
No duplicate prompt bodies needed.
Clarity & Self-Documentation
Parameter names become mini-labels, making it obvious what each piece of data does. When you see
craft
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summarize(article, style="detailed")
you immediately know you’ll get a fuller breakdown than with style="concise".
Token Efficiency
Only the parameter values traverse the API boundary, not the entire prompt text. If your base prompt is 100 tokens, injecting “Spanish” (1 token) instead of re-sending the whole instruction every time saves you dozens of tokens—and dollars—over many calls.
Separation of Concerns
The prompt author focuses on the shape of the instruction; the caller focuses on the content. This mirrors clean code practice where a library function’s signature tells you exactly what it needs.

Anatomy of Parameter Injection

Define Parameters
In your CRAFT spec’s PROJECT_FUNCTIONS section, declare each parameter with a clear name and, optionally, a default:
python
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def build_complex_persona(name, role, goals, pain_points, tech_comfort): """Creates a standardized persona variable with consistent format.""" :contentReference[oaicite:0]{index=0}:contentReference[oaicite:1]{index=1} …
Map to Placeholders
Inside the prompt: block, wrap parameter names in braces {}—CRAFT will replace them at runtime.
Type Hints & Constraints
While CRAFT doesn’t enforce Python types, you can note expected formats in your docstring or via inline comments:
craft
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func analyze_sentiment(text: string, language: string="en"): """Return sentiment score (–1 to 1) for English or other language text.""" prompt: "Analyze sentiment of this {language} text:\n\n{text}" return score
Default Values
Defaults let callers omit common parameters:
craft
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func summarize(text, style="concise"): …
Calling summarize(long_doc) uses the concise style automatically.

Examples

1. Translation Function

craft

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func translate(text, to_language): """Translate `text` into the specified `to_language`.""" prompt: "Translate the following into {to_language}:\n\n{text}" return translated_text

Call: translate(email_body, "Spanish")
Benefit: One definition handles every target language.

2. Blog-Post Outline Generator

craft

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func outline(topic, sections=["Intro","Body","Conclusion"], length=300): """Generate an outline for a blog post on `topic` with specified sections and approximate word `length`.""" prompt: "Create a blog post outline on '{topic}' with these sections: {sections}. Aim for around {length} words total." return outline_struct

Call: outline("AI in Small Business", sections=["Why","How","Case Study"], length=500)
Benefit: Fine-tune structure and scale without rewriting the prompt.

3. Data Table Formatter

craft

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func tabulate(data, headers, format="markdown"): """Turn `data` into a {format} table using `headers`.""" prompt: "Here’s some data: {data}. Format it as a {format} table with these headers: {headers}." return table_output

Call: tabulate(sales_numbers, ["Month","Revenue","Cost"])
Benefit: Uniform table style across every dataset.

How It Maps to CRAFT Principles

Accessibility Through Familiarity
Python-style parameters feel natural to anyone who’s ever written a function .
Structure Enables Freedom
Parameters remove repetitive boilerplate, so you can focus on fresh ideas every time.
Conservation of Tokens
Only small variable values move across the token boundary, slashing costs.
Code as Conversation
The function signature itself becomes part of your chat, guiding the AI like a mini-contract.

Summary

Parameterization lets you inject fresh context into a single, reusable function definition—boosting flexibility, readability, and token savings. By treating prompts like functions with named slots, you get the best of code-style modularity and natural-language ease. Ready to swap out your first prompt with parameter injection? Let me know, and we’ll craft one together!

Sources

You said:

Great, please expand on item 3 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 7 seconds

Here’s a deep dive into Return Templates for Structured Replies—how to define and enforce the shape of your AI’s output, why it matters, and concrete examples to get you started.

What Is a Return Template?

A return template is an explicit specification—inside your func—of the exact data structure you expect back from the AI. Instead of “just give me a summary,” you tell the AI:

“Return an object with these fields:
• title (string)
• bullet_points (array of strings)
• estimated_read_time (integer minutes)”

This turns free-form text into predictable, parseable output.

Why Use Return Templates?

Consistency Across Calls
Every invocation yields the same shape of data. Your downstream code or workflows never have to guess at where to find the summary versus the key points.
Easier Post-Processing
When the AI’s reply is valid JSON or a clearly delimited structure, you can programmatically ingest, transform, or display it (e.g., feed it into a spreadsheet, database, or UI component).
Token Efficiency
By guiding the AI to skip fluff and stick to the fields you care about, you cut out unnecessary prose—furthering CONSERVATION OF TOKENS.
Error Checking & Validation
If the AI returns something that doesn’t fit your template, you can detect it immediately (e.g., missing fields, wrong types) and retry or alert the user.
Cumulative Intelligence
As your func library grows, you build a catalog of interoperable building blocks—each producing standard outputs that other functions can consume.

Anatomy of a Return Template

Within your func, add a return block that describes:

Field names and their types (string, number, array, object)
Any nested structures
Optional vs. required fields

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func generate_blog_summary(text): """ Summarize a blog article and extract key metadata. """ prompt: """ Read the following article and provide: - A one‐sentence `title` - A list of three `bullet_points` - An `estimated_read_time` in minutes (integer) Article: {text} """ return { "title": string, "bullet_points": [string, string, string], "estimated_read_time": integer }

When you call generate_blog_summary(long_article), the AI knows to reply with exactly that JSON shape.

Examples

1. Persona Generator

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func build_persona(name, role, goals): """ Create a structured persona for marketing or UX. """ prompt: """ Create a user persona object with: - `name` - `role` - `age` (integer) - `goals` (list of strings) - `pain_points` (list of strings) Persona details: Name: {name} Role: {role} Goals: {goals} """ return { "name": string, "role": string, "age": integer, "goals": [string], "pain_points": [string] }

Benefit: downstream code can loop over goals or calculate an average age without parsing prose.

2. Expense Report Extractor

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func extract_expenses(report_text): """ Parse an expense report into line‐items. """ prompt: """ From the following text, extract each expense as an object: - `date` (YYYY-MM-DD) - `category` - `amount` (float) Report: {report_text} """ return [ { "date": string, "category": string, "amount": number } ]

Benefit: you get back a JSON array you can import directly into your accounting system.

Mapping to CRAFT Principles

Structure Enables Freedom
Templates hide formatting details, so you focus on the data you need, not on prose style.
Code as Conversation
The return { … } block becomes part of your chat “API,” making expectations explicit.
Cumulative Intelligence
Standardized outputs let you chain functions—one’s output becomes another’s input without glue code.
Conservation of Tokens
By limiting the AI to only the fields you specify, you eliminate filler text and lower token usage.

Next Steps

Choose a High-Value Use Case
Identify a recurring task (e.g., extracting contact info, generating FAQs, summarizing docs).
Draft Your Return Schema
List the exact fields and types you need.
Implement & Test
Add the return block to your func, invoke it in chat, and verify the AI’s reply matches.
Build Validation
Consider a small wrapper that checks the AI’s JSON against your schema and retries on mismatch.

In Summary:
Return templates turn loosely-formatted AI replies into structured data you can trust and automate around. They boost consistency, token-efficiency, and enable seamless chaining of CRAFT functions—paving the way for truly modular, maintainable AI workflows.

You said:

Great, please expand on item 4 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 8 seconds

Here’s a deep dive into Composable (Higher-Order) Prompt Chains—how to build flexible, multi-step workflows by treating functions as first-class values, why it matters, and concrete examples to get you started.

What Is a Higher-Order Prompt Chain?

A higher-order function is one that accepts other functions as arguments or returns a new function. In CRAFT, that means you can build mini-pipelines of prompt logic—chain analysis, transformation, and formatting steps together without rewriting the glue code each time.

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func compose(func_a, func_b): """ Return a new function that applies func_a, then feeds its output to func_b. """ prompt: | First, run `{func_a}` on its inputs. Then take the result and run `{func_b}` on that output. return composed_func

You can then do:

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summarize_then_translate = compose(summarize, translate) result = summarize_then_translate(article_text, "French")

Why This Matters

Extreme Reusability
Instead of building monolithic prompts for each new pipeline, you stitch together small, well-tested functions. Swap components in and out—e.g. compose(analyze_sentiment, format_report) vs. compose(generate_outline, expand_section)—and instantly get new behaviors.
Separation of Concerns
Each function focuses on one responsibility (summarizing, translating, formatting). The composer handles orchestration. This mirrors clean-code best practices, making your chat workflows clearer and easier to debug.
Token Efficiency & Clarity
You only define orchestration logic once. Downstream calls like summarize_then_translate(article, "German") send minimal orchestration tokens, since the heavy lifting lives in the function definitions.
Cumulative Intelligence
As you build up a library of small functions, higher-order chains let you leverage your entire catalog. Over time, you develop an ecosystem of interoperable steps that evolve together—no more reinventing pipelines from scratch.
Human-AI Partnership
By codifying orchestration in CRAFT, you shift the AI’s role from “I’ll do whatever you type” to “I’ll follow this mini-program.” That clear contract reduces misunderstandings and lets you focus on high-level strategy.

Anatomy of a Prompt Composer

Composer Definition
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func compose(func1, func2): """ Take two CRAFT functions and return a new function that applies them in sequence. """ prompt: | Step 1: Call `{func1}` with its parameters. Step 2: Take `{func1}`'s output and call `{func2}` on it. return composed
Dynamic Invocation
The returned composed function itself has a signature matching the union of both functions’ parameters (you’ll define that in your BUILDINGCRAFT spec).
Chaining Multiple Steps
You can build a multi-step chain by composing more than two:
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summarizer = summarize translator = translate formatter = format_markdown pipeline = compose(compose(summarizer, translator), formatter) result = pipeline(article, "Italian")

Examples

A. Summarize → Translate → Style-Guide Check

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func style_check(text, guide): """Validate that `text` follows `guide` rules, returning a list of violations.""" prompt: "Check this text against style guide `{guide}`:\n\n{text}" return violations # Build the pipeline sum_trans = compose(summarize, translate) full_pipeline = compose(sum_trans, style_check) # Use it final_report = full_pipeline(report_text, "Spanish", "AP Style Guide")

B. Sentiment → Topic-Extraction → Bullet-List Formatter

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# Assumes analyze_sentiment and extract_topics exist sent_topic = compose(analyze_sentiment, extract_topics) full_chain = compose(sent_topic, format_as_bullets) insights = full_chain(customer_feedback)

Mapping to CRAFT Principles

Structure Enables Freedom
You get creative power to mix-and-match behaviors without wrestling with bulky prompts.
Code as Conversation
The chain itself is a mini “program” in your chat—readable, shareable, and versioned.
Cumulative Intelligence
Chains build on existing functions, so every new pipeline adds to your collective AI know-how.
Human-AI Partnership
The AI knows exactly which step it’s on and what to expect, reducing guesswork and misfires.
Conservation of Tokens
Orchestration logic lives in predefined functions—you only send concise calls, not full step descriptions each time.

Next Steps

Identify Core Functions: Pick two or three high-value CRAFT functions you’ve already defined (e.g., summarize, translate, format_markdown).
Write a Composer: Add the compose function to your BUILDINGCRAFT file and document its signature.
Test a Pipeline: In chat, call your new composed function with real inputs and verify the multi-step output.
Iterate & Generalize: If you need more flexibility (e.g., variable step counts), consider a pipeline function that takes a list of functions and applies them in order.

In Summary:
Higher-order prompt chains let you treat CRAFT functions like Lego blocks—snap them together into complex workflows with minimal extra code. You gain modularity, clarity, and efficiency, unlocking powerful multi-step automations in your AI chats. Ready to wire up your first pipeline?

You said:

Great, please expand on item 5 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 18 seconds

Here’s an in-depth look at Stateful Functions for CUMULATIVE INTELLIGENCE—how they work, why they matter, and concrete examples to get you started.

What Are Stateful Functions?

A stateful function in CRAFT is one that not only returns a result but also reads from and updates a persistent state object across invocations. Rather than each call standing alone, the function can build on previous context—tracking dialogue history, variable values, or workflow progress—so your AI interactions evolve over time rather than reset at each turn .

Core Concepts

State Object
A designated variable (often named state or context) that lives in your PROJECT_VARIABLES or PROJECT_OBJECTS section and persists between calls.
Read–Modify–Write Pattern
Each function invocation can inspect state, perform its logic, then emit an updated copy of state.
Immutable vs. Mutable
- Immutable approach returns a new state object, leaving the old one intact (functional style).
- Mutable approach updates state in place (procedural style).

Why Stateful Functions Matter

Cumulative Intelligence
By carrying knowledge forward—user preferences, prior answers, or discovered facts—you prevent “starting from zero” each time and let your AI build on what came before .
Reduced Repetition
Instead of re-feeding the same background or configuration on every call, you store it once in state and rely on the function to pull it in. This slashes token usage and chat clutter.
Dynamic Workflows
You can orchestrate multi-step processes that branch based on earlier outcomes. For example, a stateful approval workflow can track “pending” → “reviewed” → “approved” without manual hand-offs.
Error Recovery & Rollback
If a function detects invalid input or an unexpected AI response, it can revert to an earlier state snapshot or flag an error state—much like transactions in a database.

Anatomy of a Stateful Function

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# In PROJECT_VARIABLES or PROJECT_OBJECTS: state = { "conversation_history": [], "variables": {}, "last_action": null } # In PROJECT_FUNCTIONS: func track_message(state, new_message): """ Append a user or AI message to conversation_history and return updated state. """ # Read existing history history = state["conversation_history"] # Append the new entry history.append(new_message) # Update last_action state["last_action"] = "track_message" # Return both the result and updated state return { "result": "message_tracked", "state": state }

Each call to track_message:

Reads state["conversation_history"]
Modifies it by appending new_message
Writes back the updated state object in the return block

Stateful Examples

A. Variable Store

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func set_variable(state, name, value): """Store or update a named variable in state.""" state["variables"][name] = value return { "confirmation": f"Set {name}", "state": state } func get_variable(state, name): """Retrieve a named variable from state.""" val = state["variables"].get(name, null) return { "value": val, "state": state }

Benefit: You can define a variable once (e.g. set_variable(state, "target_language", "French")) and reuse it in other prompts without re-passing it.

B. Multi-Step Workflow

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func start_approval(state, document): state["workflow"] = { "step": "draft_review", "document": document } return { "status": "review_started", "state": state } func advance_approval(state, approval_decision): if approval_decision == "approve": state["workflow"]["step"] = "finalize" else: state["workflow"]["step"] = "revise" return { "status": state["workflow"]["step"], "state": state }

Benefit: Tracks where you are in the approval cycle and enforces appropriate next-step logic automatically.

Mapping to CRAFT Principles

CUMULATIVE INTELLIGENCE
Stateful functions are the engine that carries context forward, turning isolated chats into a growing knowledge base .
CONSERVATION OF TOKENS
By storing background details in state, you avoid re-sending large context blocks on each call.
HUMAN-AI PARTNERSHIP
The AI and human both see and modify the same state, creating a shared workspace where each step is transparent.
STRUCTURE ENABLES FREEDOM
A well-defined state schema removes uncertainty about what data is available, letting you focus on creative logic instead of context juggling.

Next Steps

Define Your State Schema
In your BUILDINGCRAFT file’s PROJECT_VARIABLES or PROJECT_OBJECTS, declare a clear structure for state (e.g. history, variables, workflow).
Implement Core Functions
Start with simple getters and setters (get_variable, set_variable, track_message).
Test & Validate
Invoke each function in chat, inspect the returned state, and ensure it matches expectations.
Build Complex Workflows
Layer stateful functions into multi-step pipelines—approval flows, long-running analyses, or dynamic questionnaires.
Document in PROJECT_FUNCTIONS
Add each new stateful function to the PROJECT_FUNCTIONS section of CFT-PROJ-0001_BUILDINGCRAFT-v25d4.txt so it’s available in every session .

By embedding and updating a shared state object, stateful functions let CRAFT truly live up to its promise of cumulative, context-rich AI collaboration.

Sources

You said:

Great, please expand on item 6 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 9 seconds

Here’s a deep dive into Async & Streaming Functions—how to define them in CRAFT, why they matter, and concrete examples you can start using right away.

What Are Async & Streaming Functions?

In many AI APIs, you can request streaming responses (the model emits tokens as they’re ready) or perform asynchronous operations (e.g., long-running analyses). An async/streaming CRAFT function lets you:

Start an operation that may take multiple turns or long processing
Receive partial outputs in real time (e.g., progressive summaries)
Trigger callbacks or follow-up logic as chunks arrive

This turns your chat into an interactive, live pipeline instead of a single “ask‐and‐wait” cycle.

Why This Matters

Improved Responsiveness
Instead of waiting for a 500-token reply all at once, you see the first key points within seconds—letting you spot corrections early and steer the conversation.
Better Human-AI Partnership
Streaming lets you interject mid‐response with clarifications (“That’s not what I meant—focus on the budget section”), making the AI feel more like a teammate.
Token & Time Savings
If you notice early that the AI is veering off, you can cancel or adjust the prompt on the fly—avoiding wasted tokens on irrelevant prose.
Support for Long-Running Tasks
Some workflows (e.g., exhaustive document analysis, batch data processing) can be kicked off and then polled periodically, freeing you to multitask.

Anatomy of an Async/Streaming Function

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# Declare that this function uses streaming func stream_analyze(text, chunk_size=100) async stream: """ Analyze `text` in chunks of roughly {chunk_size} tokens, yielding partial results. """ prompt: | Please analyze the following in segments of about {chunk_size} tokens. After each segment, return interim insights and wait for my “continue” command. return stream_of_insights

async stream modifier tells the CRAFT engine to open a streaming connection
Interim yields let you process each chunk (e.g., display progress bars or validate partial output)
Control tokens like “continue” or “stop” let you guide the flow

Examples

1. Progressive Summarization

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func progressive_summarize(text, steps=5) async stream: """ Produce a 5-stage summary: high-level → detailed step by step. """ prompt: | We will summarize `{text}` in {steps} progressive passes. After each pass, send the summary and wait for “next” to continue. return summary_stream

Usage:

Call progressive_summarize(long_report, 3)
Review first summary pass
Reply “next” to get the deeper, second pass

2. Live Data Extraction

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func extract_entities_stream(text, batch_size=50) async stream: """ Chunk `text` into {batch_size}-token batches and extract named entities in each. """ prompt: | For each batch of {batch_size} tokens from the input, list detected entities. After each batch, wait for my “go” before proceeding. return entity_batches

Benefit: You can inspect each batch’s entities, catch misclassifications early, and adjust parameters mid-stream.

3. Asynchronous Report Generation

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func async_report(data_source) async: """ Initiate a multi-step report generation from `data_source`. Returns a job ID. """ prompt: | Start generating a comprehensive report from `{data_source}`. Return immediately with a `job_id`, then stream progress updates. return job_id

Later, a companion function can poll status:

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func check_report_status(job_id) async stream: """ Stream status updates (“10% done”, “Parsing complete”, etc.) for `job_id`. """ prompt: "Check on report generation job `{job_id}` and stream updates." return status_stream

Mapping to CRAFT Principles

Human-AI Partnership
Real-time streams let you co-author the output as it’s being created.
Conservation of Tokens
Cancel or tweak mid-stream if you see off-track content—no need to burn tokens on full reruns.
Structure Enables Freedom
Async functions abstract away polling and stream-management boilerplate, so you just “call” and “listen.”
Cumulative Intelligence
You can stitch streaming outputs into your state object, accumulating insights incrementally.

Next Steps

Identify Streaming Use Cases
Think about any analysis or transformation where partial feedback would help (summaries, extractions, or long reads).
Define Your Async/Stream Signatures
In your BUILDINGCRAFT file, add the async stream modifier to those func definitions.
Test Iteratively
Run a small text through a streaming function, confirm you get chunked results, then practice “continue” and “stop” controls.
Build UI/UX Hooks
If you have a front-end (e.g., a web chat), wire up progress bars or “more” buttons to the stream events.

In Summary:
Async & streaming functions turn monolithic AI calls into interactive pipelines—boosting responsiveness, control, and efficiency. By defining async stream in CRAFT, you unlock live, multiturn workflows that feel less like chat and more like smooth collaboration. Ready to try your first streaming function? Let me know which use case you’d like to spin up!

You said:

Great, please expand on item 7 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 14 seconds

Here’s a deep dive into Recursion & Iterative Refinement—how to layer multi-pass improvements into your CRAFT functions, why it matters, and concrete examples to get you started.

What Is Recursion & Iterative Refinement?

Recursion means a function calls itself with modified inputs until a base condition is met.
Iterative Refinement is the practical sibling: you loop a function over its own output for a set number of passes or until quality criteria are achieved.

Together, they let you progressively improve content—think of sanding a piece of wood with finer and finer grit until it’s perfectly smooth.

Why This Matters

Layered Reasoning
Complex tasks—like deep editing, multi-stage summarization, or stepwise code optimization—often benefit from tackling them in smaller chunks rather than all at once.
Quality Control
By checking output at each iteration, you can enforce thresholds (e.g. readability score, length constraints) and stop early when you hit your target.
Token & Cost Efficiency
You avoid re-sending the entire original prompt each pass; instead, you feed only the delta or the latest draft, saving tokens over one giant re-prompt.
Cumulative Intelligence
Each pass builds on the last, so the AI “learns” from its own improvements within a single session—rather than re-discovering context every time.

Anatomy of a Recursive/Iterative CRAFT Function

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func refine(text, passes=3, target_readability=8.0): """ Recursively polish `text` for `passes` iterations or until the Flesch-Kincaid readability score ≥ target_readability. """ # Step 1: Analyze current quality prompt: | On a scale of 0 (very hard) to 10 (very easy), rate the readability of this text: {text} return { "score": number, "improved_text": string } # PSEUDO-CODE for recursion (the CRAFT engine handles the loop) if score < target_readability and passes > 1: return refine(improved_text, passes - 1, target_readability) else: return improved_text

How it works:
Analyze: Ask the AI to score the text.
Polish: In the same call (or a chained call), ask it to rewrite for clarity.
Recurse: If the score still falls short and you have passes left, call refine(...) again with the new draft.

Practical Examples

1. Multi-Pass Summary Refinement

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func deep_summarize(text, depth=3): """ Create a summary that gets shorter and more focused with each pass. """ prompt: "Summarize this in one paragraph:\n\n{text}" return draft_summary # After each pass, feed draft_summary back into deep_summarize with depth-1

Benefit: Your final summary is both concise and distilled from progressive focus.

2. Code Clean-Up Loop

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func clean_code(code, iterations=2): """ On each pass, improve naming, remove redundancies, and adhere to PEP8. """ prompt: "Refactor this Python code for clarity and style:\n\n{code}" return cleaned_code # Loop until `iterations` exhausted, returning the final cleaned_code

Benefit: You get a stepwise refactoring, catching higher-level improvements first and style tweaks next.

Mapping to CRAFT Principles

Structure Enables Freedom
You offload orchestration (the loop logic) into func, so your chat stays focused on what to improve, not how to loop.
Cumulative Intelligence
Each recursive call leverages prior improvements, building a richer context within one session.
Conservation of Tokens
Only the latest draft traverses the token boundary each time, not the full history.

Next Steps

Pick a Use Case:
Decide whether you need multi-pass editing, progressive summarization, or iterative data cleansing.
Define Your Base Function:
Draft the prompt: and return schema for one pass.
Add Loop Logic:
In your BUILDINGCRAFT file’s PROJECT_FUNCTIONS, wrap the base in a recursion or pseudo-loop construct.
Test & Tune:
Run with different passes or quality thresholds to find the sweet spot between depth and cost.

In Summary:
Recursion and iterative refinement let you create CRAFT functions that “hone in” on quality—much like sharpening a pencil tip one turn at a time. By structuring your prompts to call themselves (or loop) until a goal is met, you unlock multi-stage workflows that are more precise, efficient, and context-aware. Ready to recursive-ify your next prompt? Let’s pick one and give it a spin!

You said:

Great, please expand on item 7 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 6 seconds

It looks like we’ve already covered Recursion & Iterative Refinement (item 7) in depth. Would you like me to:

Go even deeper on recursion—perhaps more advanced patterns or use-cases?
Move on to item 8 (Namespaces & Modules for Organization)?

Let me know which you’d prefer!

You said:

Good catch, sorry. Great, please expand on item 8 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 20 seconds

Here’s a comprehensive look at how Namespaces & Modules for Organization can bring order and scalability to your CRAFT functions and data types.

What Are Namespaces & Modules?

A namespace (or module) in CRAFT is a named container that groups related functions, objects, or data types under a common label—much like a Python package. Instead of summarize(...) and translate(...) living in a flat global scope, you might nest them:

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module nlp: func summarize(text, style="concise"): … func translate(text, to_language): … module data: func tabulate(data, headers): … func extract_entities(text): …

Then you invoke them as nlp.summarize(report) or data.extract_entities(notes), making their purpose immediately clear.

Key Benefits

Avoid Name Collisions
By isolating functions in modules, you can safely reuse generic names like format or validate in different contexts without conflicts.
Discoverability & Readability
When a new collaborator browses your BUILDINGCRAFT file, seeing analytics.calculate_metric() instantly tells them where to look for analytics-related logic.
Scalability
As your prompt library grows (you already have hundreds of prompts in ketelsen.ai), modules prevent the PROJECT_FUNCTIONS section from becoming a monolithic list .
Contextual Clarity
Namespaces reinforce “code as conversation” by making each call read like a sentence:
marketing.generate_social_post(…) vs. generate_social_post(…).
Token Savings through Implicit Imports
Once a module is declared at the top of your CRAFT file, you don’t need to re-specify its content—only the short module name traverses the chat.

How to Define & Use Modules

Declare Modules in BUILDINGCRAFT
Add a new section after PROJECT_FUNCTIONS, for example:
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# ==================================================== # 6. PROJECT_MODULES – Group related functions # ==================================================== module nlp: import PROJECT_FUNCTIONS.summarize import PROJECT_FUNCTIONS.translate module data: import PROJECT_FUNCTIONS.tabulate import PROJECT_FUNCTIONS.extract_entities
Reference Functions with a Prefix
When you call them in chat, qualify with the module name:
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summary = nlp.summarize(article_text, style="detailed") entities = data.extract_entities(article_text)
Nested Modules for Granularity
You can even nest modules, e.g.:
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module analytics.metric: func calculate_ratio(numerator, denominator): … module analytics.timeseries: func predict_next(data): …
Then call analytics.metric.calculate_ratio(…) or analytics.timeseries.predict_next(…) for maximum clarity.

Mapping to CRAFT’s Philosophical Principles

Structure Enables Freedom
A clear module hierarchy takes mental load off remembering names, so you can focus on creative prompt design.
Code as Conversation
Namespaced calls read like plain English (“analytics.metric.calculate_ratio”), blending code structure with natural language.
Cumulative Intelligence
As your function library grows across sessions, modules help you build a well-organized, reusable knowledge base.
Accessibility Through Familiarity
Python users will find module syntax intuitive; non-technical users benefit from the extra context it provides.
Conservation of Tokens
With modules declared once, subsequent calls only send the short prefix and function name—not full prompt definitions.

Concrete Example

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# In CFT-PROJ-0001_BUILDINGCRAFT-v25d4.txt under PROJECT_MODULES: module nlp: # Summarization & Translation import PROJECT_FUNCTIONS.summarize import PROJECT_FUNCTIONS.translate module ux: # Persona & Audience Tools import PROJECT_FUNCTIONS.build_persona import PROJECT_FUNCTIONS.define_target_audience # Usage in chat: #H->AI::Directive: (Generate a French summary of last week’s report) fr_summary = nlp.translate(nlp.summarize(weekly_report), "French") #H->AI::Directive: (Create a user persona for a small-business owner) owner_persona = ux.build_persona("Jordan", "Owner", ["Scale revenue"], ["Limited cash flow"], "novice")

Next Steps

Update Your BUILDINGCRAFT File
Add a PROJECT_MODULES section and group your existing functions.
Refactor Calls
Replace flat calls in your ongoing scripts with namespaced versions.
Document Module Contents
In your spec or a README, list each module’s purpose and included functions for easy reference.
Iterate & Expand
As you add new functions, immediately slot them into the appropriate module or create new ones.

By structuring your CRAFT library with namespaces and modules, you’ll transform a sprawling list of prompts into a coherent, maintainable, and scalable toolkit—ready for any AI-driven experiment.

Sources

You said:

Great, please expand on item 9 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Here’s a deep dive into Decorators & Hooks—how you can wrap extra behavior around your CRAFT functions, why they’re powerful, and concrete examples to get you started.

What Are Decorators & Hooks?

Decorators are wrappers that add pre- or post-processing to a function without touching its core logic. Think of them like sticky notes around your code that say “Before you run, do X; after you run, do Y.”
Hooks are specific callback points—named events in your function lifecycle (e.g. on_enter, on_exit, on_error) where you can plug in custom behavior.

Together, they let you inject cross-cutting concerns (validation, logging, metrics, retries) in a clean, reusable way.

Why This Matters

Separation of Concerns
Keep your function’s core prompt focused on business logic, while decorators handle orthogonal tasks (input checks, usage tracking).
Reusability
Write one @validate_input decorator and apply it to dozens of functions—no copy-paste of boilerplate validation code.
Consistency
Enforce the same standards (e.g. parameter formats, error messages) across your entire CRAFT library by simply decorating each function.
Observability & Debugging
Hooks like on_error let you capture exceptions or malformed outputs and automatically raise a warning or retry—making your AI workflows more robust.
Token & Time Savings
By handling retries or input normalization in a decorator, you avoid manual cleanup inside every prompt, reducing extra token usage and developer overhead.

Anatomy of a Decorator in CRAFT

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# Define a decorator in your BUILDINGCRAFT spec: decorator validate_params(func): """ Ensure all required params are non-empty strings. """ hook on_enter: for param, val in func.params.items(): if not val: raise Error(f"Parameter {param} cannot be empty") return func # Apply it to a function: @validate_params func translate(text, to_language): """Translate text into the given language.""" prompt: "Translate {text} into {to_language}." return translated_text

decorator keyword declares the wrapper
hook on_enter runs before the inner function’s prompt is sent
func.params gives you access to the call’s argument values
raise Error(...) halts execution with a clear message if validation fails

Common Decorators & Hooks

Decorator/HookPurpose@validate_paramsCheck for required parameters or correct types@token_budget(max)Abort or warn if estimated token use exceeds maxhook on_exitLog execution time, token usage, or output shapehook on_errorCapture mismatches in return templates and trigger retries@cache_result(time)Store and reuse outputs for identical calls within time

Examples

1. Input Validation Decorator

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decorator ensure_nonempty(func): """ Reject calls with any empty or null arguments. """ hook on_enter: for name, val in func.params.items(): if val is null or val == "": raise Error(f"Argument {name} is required.") return func @ensure_nonempty func summarize(text): prompt: "Summarize: {text}" return summary

Benefit: You never waste tokens on an empty summary request.

2. Token-Budget Enforcement

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decorator enforce_budget(max_tokens): hook on_enter: estimated = estimate_tokens(func.prompt, func.params) if estimated > max_tokens: raise Error(f"Estimated {estimated} tokens exceeds budget of {max_tokens}.") hook on_exit: log("TokenUsage", actual_tokens_used()) return func @enforce_budget(150) func outline(topic): prompt: "Create a 300-word outline for: {topic}" return outline_struct

Benefit: Keeps your costs predictable and prevents runaway token storms.

3. Retry on Failure Hook

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decorator retry_on_error(max_retries=2): hook on_error(error): if func.retry_count < max_retries: func.retry_count += 1 return call(func) # retry the same call else: raise error return func @retry_on_error(3) func extract_entities(text): prompt: "List named entities from: {text}" return entities

Benefit: Automatically recovers from transient API hiccups or occasional format glitches.

Mapping to CRAFT Principles

Structure Enables Freedom
Decorators abstract repetitive checks so your core prompts stay crisp.
Code as Conversation
Hooks become part of your “chat API,” signaling exactly when extra logic runs.
Conservation of Tokens
By validating inputs early and caching results, you avoid unnecessary prompt retries.
Human-AI Partnership
With on_error hooks and retries, the AI collaboration feels more resilient and reliable.

Next Steps

Identify Cross-Cutting Concerns:
List validation, logging, caching, or retry behaviors you repeat across functions.
Draft Decorators:
For each concern, write a decorator block with appropriate hooks in your BUILDINGCRAFT file.
Apply & Test:
Sprinkle your new decorators on existing functions, then call them with both valid and invalid inputs to confirm behavior.
Document Usage:
In your spec or a README, note which decorators exist, what they do, and how to apply them.

By introducing decorators and hooks into CRAFT, you’ll gain a powerful, modular way to inject shared behavior—making your prompt library cleaner, safer, and easier to maintain. Let me know when you’re ready to decorate your first function!

You said:

Great, please expand on item 9 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 6 seconds

It looks like we’ve just walked through Decorators & Hooks in depth. To make sure I give you exactly what you need next, would you like me to:

Dive into advanced decorator patterns (e.g. stacking multiple decorators, parameterized decorators, dynamic hook registration), with more code-style examples?
Show a real-world workflow where we apply several decorators/hooks together (e.g. validation + caching + logging) end-to-end?
Or skip ahead to item 10 (Token-Budgeted Signatures)?

Let me know which direction you’d prefer!

You said:

Great, please expand on item 10 with details, benefits, explanations , examples and anything else that you think will be helpful.

ChatGPT said:

Thought for 8 seconds

Here’s an in-depth look at Token-Budgeted Signatures—how to design function interfaces that respect your token limits, why it’s crucial, and practical recipes to get you started.

What Is a Token-Budgeted Signature?

A token-budgeted signature is a function definition whose parameters and overall “call footprint” are deliberately scoped so that each invocation stays within a predictable token budget. Instead of letting calls balloon in size (and cost), you trim the signature to only what’s absolutely necessary.

Analogy: Think of packing a lunchbox—you decide exactly which items fit so nothing spills over. Similarly, a token-budgeted signature packs only essential parameters into each call.

Why This Matters

Cost Control
Every extra token costs money. By capping your signature’s size, you avoid unpleasant surprises on your bill.
Performance & Speed
Smaller payloads mean faster round-trip times. Your AI feels snappier, and you spend less time waiting for large-context responses.
Predictability
When you know “this function call will never exceed X tokens,” you can safely orchestrate multi-step pipelines without token-limit errors mid-flow.
User Experience
Less scrolling in chat, cleaner messages for collaborators, and fewer truncation headaches when context windows fill up.

Designing Token-Budgeted Signatures

Audit Your Parameters
- List every argument in your func signature.
- For each, ask: “Can I derive this from state or another function instead?”
- Remove anything that’s redundant or only marginally useful.
Use Defaults & Implicit Context
- If most calls use the same value, bake it into the default.
- For example, instead of func translate(text, to_language="French"), make to_language default so callers can omit it.
Leverage state for Heavy Context
- Store large context blocks (e.g., user profiles, long templates) in your persistent state object.
- Your function only needs a small reference like user_id instead of the full profile text.
Parameter Aliasing
- Shorten parameter names when clarity permits (e.g. txt instead of text_blob).
- But strike a balance—don’t sacrifice readability for one-token savings.
Schema Compression
- For functions returning or accepting structured data, replace verbose field names with concise keys in the signature, then map them internally.
- E.g. func gen_summary(t, s) instead of func generate_summary(text, style) if your docstring clarifies the mapping.

Examples

A. Lean Translation Function

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# Before (verbose) func translate(text, to_language="English", tone="neutral"): prompt: "Translate {text} into {to_language} with a {tone} tone." return result # After (budgeted) func translate(txt, lang="en"): """Translate txt into lang (ISO code).""" prompt: "Translate {txt} into {lang}." return out

Savings: Shorter names (txt, lang), dropped optional tone—callers omit unused params.

B. Reference-Only Context

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# Heavy signature func personalize_email(name, role, company, interests, past_purchases): prompt: "... use all these details ..." return email # Budgeted via state # Assume state.user_profiles[name] holds role, company, interests, purchases func personalize_email(name): prompt: "Write a personalized email for user {name} based on their profile in state." return email

Savings: Only the user’s name (1–2 tokens) crosses the wire instead of a whole profile.

C. Schema Compression for Reports

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# Verbose schema func generate_report(data, format="markdown", include_summary=true): ... # Token-budgeted schema func gen_rep(d, f="md"): """ d: data object f: 'md' or 'html' """ ...

Savings: Compact param names and codes.

Mapping to CRAFT Principles

Conservation of Tokens
This is the direct embodiment—your signatures become lean lunchboxes, not suitcase-sized prompts.
Structure Enables Freedom
With a tight budget, you’re forced to focus on what truly matters, spurring creative minimalism.
Human-AI Partnership
Predictable budgets mean fewer “context window full” errors, so the AI collaboration flows smoothly.

Next Steps

Inventory Your Top 10 Functions
Review their signatures and count characters/tokens.
Refactor One by One
Apply the audit steps—eliminate, default, alias—until each stays under your target (e.g. 10–15 tokens).
Test for Parity
Ensure outputs remain correct despite the shorter calls.
Document the Changes
In your BUILDINGCRAFT spec, note the original vs. budgeted signatures for future reference.

Summary

By artfully trimming your function signatures—via defaults, state references, and concise naming—you keep every call within a known token budget. This practice saves cost, speeds up interactions, and makes your CRAFT framework more robust and user-friendly. Ready to shrink your first signature? Let’s pick one and optimize it together!

Below is an outline of the key highlights related to how “objects” can be adapted within the CRAFT framework. Wherever specific object definitions or descriptions are referenced, I’ve cited the relevant file excerpts to ground each point in the existing project material.

Role and Purpose of Objects in CRAFT
• Objects as High-Level Abstractions
– In CRAFT, objects bundle related data (variables) and behaviors (functions) into cohesive units, mirroring object‐oriented patterns to organize complex AI interactions. This hybrid approach bridges coding and conversational AI, enabling modular, reusable structures rather than ad‐hoc prompt fragments.
• Enhancing “Code as Conversation”
– By representing instructions or content pieces as objects, CRAFT leverages familiar Python‐style constructs to make natural‐language prompts more structured. Rather than writing free‐form instructions, authors can instantiate or manipulate objects like Workflow, ChartSpec, or ConstraintSet to communicate intent clearly to the AI.
Core Categories of CRAFT Objects
• Content‐Oriented Objects
– Prompt / PromptLibrary: Encapsulate single prompt templates with placeholders or entire libraries of reusable prompts, streamlining the process of selecting and modifying prompts without rewriting text.
– AIRecipe / AIRecipeLibrary: Represent tested, ready‐to‐use “recipes” (structured prompt sequences) for specific tasks; libraries can be iterated as objects to discover and reuse effective patterns.
• Workflow and Decision Objects
– Workflow: Models a named sequence of steps (e.g., Workflow("NewBlogPost", steps=[…])), letting the AI execute multi‐step processes without re‐specifying each instruction every session.
– AIDecisionPoint: Encapsulates conditional branching logic so AI can evaluate metrics or states and choose different paths, enabling dynamic conversation flows without embedded prose “if/then” logic.
• Knowledge and Relationship Objects
– KnowledgeGraphNode: Represents a node or concept in a growing knowledge graph, supporting “Cumulative Intelligence” by charting entities and their properties across sessions.
– DefinedRelationship: Explicitly models links between entities (e.g., Relationship(subject=…, predicate=…, object=…)), improving AI’s contextual reasoning about connections (e.g., partnerships, dependencies).
• Validation and Formatting Objects
– ConstraintSet: Groups rules or limitations (e.g., branding guidelines) that can be attached to any instruction or object, so AI enforces consistent constraints without verbose restatement.
– OutputSchema: Declares expected output structure (fields, types), ensuring AI replies conform to predictable, machine‐readable formats (e.g., JSON‐style reports).
– ComparisonMatrix: Standardizes tabular comparisons (items vs. criteria), enabling entrepreneurs to ask for side‐by‐side evaluations without manually formatting tables.
• Versioning and Iteration Objects
– RevisionRequest: Captures targeted feedback on existing content (RevisionRequest(target_id=…, change=…, rationale=…)), enforcing a structured review loop across multi‐session drafts.
– VersionTag: Associates version labels with specific object states (e.g., documents, workflows), facilitating “Cumulative Intelligence” by tracking iterations over time.
Key Benefits of Using Objects in CRAFT
• Modularity & Reusability
– Once an object type (e.g., ChartSpec, APIRequest, ConditionLogic) is defined, it can be instantiated and reused across different prompts or sessions, reducing boilerplate and ensuring consistency.
• Clarity & Maintainability
– Objects make complex instructions more declarative. For example, instead of describing chart ticks and labels in prose, a ChartSpec(type="line", x="date", y="sales") communicates exactly how to render a chart. This structure is easier to maintain or revise.
• Scalability for Multi‐Session Projects
– By embedding stateful constructs like KnowledgeGraphNode or Workflow, CRAFT preserves context across sessions. Subsequent AI assistants can pick up where the last one left off by re‐loading object instances rather than re‐reading free‐form notes.
• Enhanced Business Alignment
– Objects like BusinessLogic or ComplianceRule encode higher‐level, domain‐specific rules (e.g., “if user.loyalty_years > 1, apply 10% discount”) so that AI can audit or generate workflows compliant with organizational needs.
Creative Adaptation Ideas for CRAFT Objects
• Dynamic Persona Switching via AIPersona Objects
– Define multiple AIPersona objects (e.g., Persona("TechnicalReviewer"), Persona("MarketingCopywriter")) to let AI assume different voices or expertise levels on demand. When drafting a blog post, you might call:
```
css
```
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#H->AI::Instruction: (“Write the product description”).using(Persona("MarketingCopywriter"))
This pattern makes it easy to shift tone or role without rewriting instructions .
• Parametric Prompt Libraries through Prompt Objects
– Create Prompt objects with placeholders (e.g., Prompt(template="Summarize the Q2 results for {company}", variables=["company"])) and store them in a PromptLibrary. At runtime, the AI can iterate over a list of companies, filling in {company} without redefining each prompt.
• Automated Research Pipelines with Workflow + APIRequest Objects
– Model a multi‐step research process as a Workflow("CompetitorAnalysis", steps=[APIRequest(…), ChartSpec(…), RevisionRequest(…)]). This bundles together data‐fetch, analysis, and review steps. An entrepreneur can simply call execute(Workflow("CompetitorAnalysis")), and CRAFT orchestrates each object in sequence.
• Adaptive Decision Paths Using AIDecisionPoint + Metric Objects
– Embed performance or sentiment metrics (e.g., Metric("customer_satisfaction", 0.65, unit="score")) into AI flows. Then use an AIDecisionPoint(trigger=Metric(...), condition="value < 0.7", if_true=ActionTemplate("InvestigateComplaint"), if_false=Instruction("ContinueStandardFollowUp")) object to branch. This introduces simple “if‐then” logic in a declarative manner.
• Evolving Knowledge Graphs via KnowledgeGraphNode + DefinedRelationship Objects
– As new entities emerge in a project (e.g., KnowledgeGraphNode(id="project_alpha", type="Project", properties={…})), link them with DefinedRelationship objects (Relationship(subject="project_alpha", predicate="uses_tech", object="python")). Over time, AI can traverse this graph to surface insights or suggest related tasks, driving “Cumulative Intelligence.”
• Contextual Output Enforcement with OutputSchema + ConstraintSet
– When generating a financial summary, attach an OutputSchema("FinancialReport", fields={…}) plus ConstraintSet("ExecutiveBriefGuidelines", rules=[…]). This tells AI exactly how to format the output (e.g., JSON keys, section headings) while enforcing tone and length constraints.
Practical Steps to Define New Custom Object Types
• Identify Repeating Patterns or Complex Tasks
– Look for any sequence or data structure you keep rewriting in prose (e.g., a legal‐compliance check, a multi‐channel campaign). These are candidates for new object types.
• Draft a Declarative Data Model
– Define the object’s attributes and expected behavior. For example, a RiskAssessment object might have fields name, score_metric, thresholds, and methods like evaluate() or recommend_mitigation().
• Embed Example Usage in CRAFT Spec
– Add code snippets to the CRAFT specification file showing how to instantiate and use the object. For instance:
```
python
```
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RiskAssessment( name="SiteBreachRisk", score_metric=Metric("vulnerability_score", value=0.4), thresholds={“high”: 0.7, “low”: 0.3} )
Include at least one START PYTHON CODE BLOCK snippet in the spec.
• Document Expected AI Behavior
– Under “3.1 DATA TYPE DESCRIPTIONS,” write a concise definition and example so that any AI assistant can parse it without extra instructions. E.g.:
RiskAssessment: Encapsulates a risk evaluation process by comparing a metric against thresholds; returns a risk level and recommended actions. – Example: RiskAssessment(…)
• Test and Iterate
– Use a sample chat to create instances of the new object and verify the AI responds correctly (e.g., if you call RiskAssessment(...), AI should evaluate and return a result). Refine until behavior is consistent.
Summary of Key Takeaways
Objects in CRAFT shift free‐form prompts into structured, reusable modules, reducing token usage and improving maintainability.
Existing Object Types (e.g., Workflow, AIDecisionPoint, KnowledgeGraphNode, ConstraintSet, RevisionRequest) already cover a broad spectrum of needs—workflow orchestration, decision logic, knowledge capture, versioning, and constraints.
Creative Adaptation involves layering these objects to form dynamic, multi‐session pipelines (e.g., calling a Workflow containing APIRequest, ChartSpec, and RevisionRequest objects for an end‐to‐end process).
Extending CRAFT with new objects means identifying recurring patterns, defining a clear schema, embedding examples in the spec, and iterating until AI consistently interprets and acts on them.

This outline should serve as a roadmap for exploring and expanding how object‐oriented concepts can power more structured, scalable AI conversations under the CRAFT framework. Each section draws on existing definitions and examples from the CRAFT specification files (especially and ) so you can see exactly where these ideas live in the current project.

Sources

You said:

Great, please expand on Role and Purpose of Objects in CRAFT

ChatGPT said:

Thought for 16 seconds

Below is a deeper look at how objects function within CRAFT—why they exist, what problems they solve, and how they reshape the way we “talk” to AI. Wherever possible, you’ll see citations back to the CRAFT specification files so you can trace the ideas directly to the source.

Why Objects Matter in CRAFT

At its core, CRAFT was invented to bridge the gap between conversational AI and software‐style rigor. Rather than treating each prompt as free‐form text, objects let us package related data and behavior into single, reusable units. This solves two big headaches:

Preventing Repetition & Drift
When you rely solely on ad‐hoc prompts—paragraph after paragraph of instructions—you inevitably end up retyping versions of the same idea (e.g., “Always follow the brand voice,” “Use these formatting rules,” etc.). Over time, slight wording changes can cause the AI to drift off course. Objects bundle all the relevant rules, data, and methods into one place so you only define them once.
Maintaining Context Across Sessions
Imagine you ask AI to research competitors today, then pick up again next week to write a summary. Without objects, you’d need to remind the AI of every detail (metrics, sources, past decisions). With objects like Workflow or KnowledgeGraphNode, the AI can reload a structured snapshot of the previous session’s state—no manual “recap” needed.

To borrow a household analogy, think of an object like a labeled folder in a filing cabinet, whereas a free‐form prompt is more like a loose stack of papers. Once you put all related documents in one labeled folder, anyone (or any future AI session) can grab that folder and immediately know where everything is. That’s exactly why CRAFT objects exist: to keep all the “attachments” and “instructions” neatly bundled so you don’t lose track.

Objects as High-Level Abstractions

Bundling Data + Behavior

In traditional software, an object might be a Customer class with fields (name, email) and methods (send_invoice()). CRAFT adopts this same idea for AI prompts:

Data: Variables or parameters that an object holds (e.g., a Metric object might store name="conversion_rate", value=3.2, unit="percent").
Behavior: Implicit conventions or instructions tied to that data (e.g., if you ask the AI to “evaluate” a Metric object, it knows to compare it against targets and return an analysis rather than treating it as free text).

By grouping related concepts (data) with the “rules” for handling them (behavior), objects become self-describing modules. In practice, when you write:

python

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Metric("customer_satisfaction", 0.65, unit="score")

you’re not just conveying “customer_satisfaction is 0.65.” You’re implicitly telling the AI: “Here’s a KPI; evaluate it against benchmarks, check for red flags, and format your response as a concise report.” That shared understanding saves hundreds of tokens in explanatory text—and minimizes misunderstandings.

The Transition from Text to Structure

Ordinarily, if you wanted AI to generate a chart, you might write:

“Plot a line chart showing monthly sales over the past year. X-axis is date, Y-axis is revenue. Title it ‘Monthly Sales.’ Use a clean, minimal style and label each axis clearly.”

With a ChartSpec object, you replace that entire paragraph with something like:

python

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ChartSpec(type="line", x="date", y="revenue", title="Monthly Sales")

The AI already “knows” how to interpret a ChartSpec to produce a chart, so you don’t have to spell out every detail. This doesn’t just cut down on tokens—it enforces consistency (every chart created via ChartSpec follows the same conventions) and makes prompts far easier to maintain when requirements change.

Enhancing “Code as Conversation”

Declaring Intent in Python-Style

One of CRAFT’s guiding philosophies is “Code as Conversation.” Rather than writing free-form instructions that the AI must interpret on the fly, you lean on Python-compatible syntax to make intent explicit. For example, instead of:

“Review this write-up and make it friendlier, with shorter sentences.”

You use:

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RevisionRequest(target_id="draft_001", change="Rewrite for a friendly tone with shorter sentences", rationale="Improve readability for lay audience")

This accomplishes two things simultaneously:

Clarity: The AI sees a clear RevisionRequest object with exactly three fields—target_id, change, and rationale—so there’s no ambiguity about what you want done or why.
Reusability: Once you define the RevisionRequest data type, you can spin up as many revision requests as needed without re-explaining the structure. Moreover, if you ever want to change how a “revision” is handled globally (for instance, adding an automatic style check), you only update the RevisionRequest handler in your CRAFT spec, not every prompt.

When AI sees that object, it knows to treat it as an instruction to update existing content identified by "draft_001". This is a drastic shift from asking the AI to parse a paragraph of text instructions and figure out what to do—objects let you speak in a defined “dialect” that AI tools already understand.

Building Multi-Step Processes Naturally

Think about a multi-step task—say, launching a new product. In a free-form chat, you might end up writing something like:

“First, draft a product description.”
“Now analyze competitors’ positioning and summarize the differences.”
“Then create a Go-to-Market outline.”
“Finally, compile everything into a single PDF deck.”

Each step would require the AI to parse and remember the previous context. With CRAFT objects, you instead define a Workflow object:

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Workflow( name="ProductLaunchPlan", steps=[ AIInstruction("Draft product description"), AIInstruction("Analyze competitors and summarize"), AIInstruction("Create Go-to-Market outline"), AIActionTemplate("Compile presentation", format="PDF") ] )

By presenting that single Workflow object, you’re telling the AI: “Here are four discrete tasks. Execute them in order, pass outputs from one to the next, and return the final deliverable.” Because CRAFT’s engine already knows how to interpret Workflow and its constituent objects, you don’t need to restate the sequence or dependencies.

From a user’s perspective, this is like giving someone a recipe card with numbered steps rather than an essay describing how to cook. Recipes are concise, unambiguous, and easy to follow; objects turn AI prompts into that same familiar pattern.

Grounding Objects in CRAFT Philosophy

CRAFT’s designers intentionally chose Python-compatible syntax because it’s both human-readable and machine-friendly. Let’s connect the dots back to the six guiding principles:

Structure Enables Freedom
When you offload repetition and nuance into objects, you free up “creative headspace” to focus on innovation rather than logistics. You no longer need to remember if you asked AI to “always use Oxford commas”—you embed that rule in a ConstraintSet object once and for all.
Code as Conversation
Objects are literally code structures—Python classes, data types, and methods—that the AI recognizes as “instructions” rather than free-text. By thinking in objects, you turn every AI chat into a mini-coding environment where the conversation itself is structured code.
Cumulative Intelligence
Because objects can be serialized (converted into JSON or Python dictionaries) and passed between sessions, they preserve state. A KnowledgeGraphNode created in session one can be revisited in session seven without re-explaining its properties—thus steadily building a shared knowledge base.
Human-AI Partnership
By shifting low-value grunt work (restating boilerplate instructions) into object definitions, humans and AI collaborate more synergistically. You define the high-level strategy; the AI executes as if running your code. That’s true partnership—humans set intent, AI carries it out.
Accessibility Through Familiarity
Many entrepreneurs and content creators already know basic Python syntax. Even if you’re not a coder, objects like AIPersona("MarketingCopywriter") read like pseudo-English: “Use the AI persona that writes marketing copy.” You don’t need to invent a new prompt-language grammar; CRAFT uses Python-style names and conventions you already recognize.
Conservation of Tokens
Every time you replace a multi-sentence instruction with a single object instantiation, you save tokens—and tokens are money. Instead of spending 50 tokens describing how to format a table, you write ComparisonMatrix(...), and the AI knows exactly how to render it.

Summary of “Role and Purpose of Objects in CRAFT”

High-Level Abstractions: Objects bundle related data and behaviors into one unit, preventing repetition and simplifying maintenance (e.g., Metric, RevisionRequest, ChartSpec).
Code-Like Conversation: By using Python-style syntax and predefined object types, prompts become declarative code rather than free-form text. This makes AI’s job clearer and responses more consistent (e.g., Workflow, ConstraintSet).
Modular & Reusable: Once an object type is defined, you can instantiate it anywhere without rewriting its schema. Changing the core logic of a workflow or rule only requires updating the object definition in the CRAFT spec—every prompt referencing that object automatically inherits the change.
Context Persistence: Objects like KnowledgeGraphNode and VersionTag let you hand off state from one session to the next, creating cumulative knowledge rather than isolated one-off chats.
Business Alignment: Complex business logic and compliance rules can be encoded as objects (BusinessLogic, ComplianceRule) so AI can automatically audit or enforce them—no need for manual checks in every prompt.

By embracing objects, CRAFT transforms AI chats from scattered instructions into a structured, code-driven workflow. You end up with a repeatable, maintainable, and efficient system—much like software development—while still leveraging the natural-language strengths of conversational AI.

Sources

You said:

Great, please expand on Core Categories of CRAFT Objects

ChatGPT said:

Thought for 9 seconds

Below is a detailed expansion of the Core Categories of CRAFT Objects, drawn directly from the framework specification. Each category bundles together related data types and behaviors, making it easier to structure AI interactions. After each section, you’ll find a brief summary to reinforce the main takeaways.

1. Content-Oriented Objects

Purpose & Rationale

Content-Oriented Objects encapsulate the building blocks you’d use when working on any AI-driven content task—blog posts, social media updates, reports, or prompt recipes. Rather than embedding every detail in free-form text, these objects let you declare “I need a prompt template,” “I want to store this prompt,” or “I’m creating a reusable recipe.” In practice, this modularizes and speeds up content creation, since you don’t have to retype or rediscover your favorite prompt structures each time.

Key Object Types

Prompt / PromptLibrary
- Prompt: A template with variables that can be filled in at runtime.
```
python
```
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  Prompt( template="Summarize the quarterly earnings for {company_name}.", variables=["company_name"] )
  By using a Prompt object, you avoid rewriting “Summarize the quarterly earnings for XYZ” over and over. Instead, you instantiate the same template with different company names.
- PromptLibrary: A collection of Prompt objects, often organized by category (e.g., “research prompts,” “SEO prompts,” “email prompts”).
```
python
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  PromptLibrary( name="SEO_Prompts", prompts=[ Prompt(template="Generate meta description for {article_title}", variables=["article_title"]), Prompt(template="List top keywords for {topic}", variables=["topic"]) ] )
  Rather than searching through old chats or documents for that “perfect” SEO prompt, you load the library and reference it directly.
AIRecipe / AIRecipeLibrary
- AIRecipe: Represents a tried-and-true sequence of prompts (a “recipe”) that accomplishes a specific task—say, “audit website accessibility.” Each recipe can reference other CRAFT objects (variables, workflows, etc.).
```
python
```
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  AIRecipe( name="AccessibilityAudit", steps=[ AIInstruction("List common accessibility issues for {page_url}"), AIInstruction("Generate a prioritized action plan to fix those issues"), RevisionRequest(target_id="audit_plan", change="Ensure steps follow WCAG guidelines", rationale="Compliance requirement") ] )
  Once you’ve proven that “AccessibilityAudit” works reliably, you store it as an AIRecipe and reuse it without re-engineering.
- AIRecipeLibrary: A repository of multiple AIRecipe objects, helping entrepreneurs quickly browse and leverage best practices—everything from “write a press release” to “generate investor‐pitch bullet points.”
AIActionTemplate
- Think of this as a partially completed prompt blueprint for a specific action (e.g., “email the customer”), with placeholders for parameters.
```
python
```
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  AIActionTemplate( name="EmailCustomer", parameters=["recipient_name", "product_issue"], template="Write an email to {recipient_name} apologizing for the {product_issue} and offering a resolution." )
  When you need to send a similar email, you simply call AIActionTemplate("EmailCustomer", recipient_name="Alex", product_issue="delivery delay") and pass it to the AI.
Conversational / Social Media Objects
- SocialPost: Automatically formats a piece of content for social media—factoring in character limits, hashtags, and tone.
```
python
```
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  SocialPost( platform="LinkedIn", topic="AI in Cybersecurity", detail_level="concise" )
- RichContent: Wraps formatted text, links, or multimedia references so AI knows to preserve styling (bold, italics, hyperlinks). This object helps you seamlessly create blog or article drafts without worrying about markdown syntax.
Reusable Snippet Objects
- CodeSnippet: When you need the AI to analyze or modify code, you enclose it in a CodeSnippet so the AI can treat it as code rather than prose.
```
python
```
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  CodeSnippet( language="python", content="def calculate_roi(cost, revenue): return (revenue - cost) / cost" )
  This ensures the AI doesn’t accidentally “translate” your code into plain English or reformat it incorrectly.

Everyday Analogy

Imagine you’re a chef:

A Prompt is like your favorite pancake recipe (with blanks for “type of flour” or “sweetener”).
A PromptLibrary is your entire cookbook of pancake variations.
An AIRecipe is a multi‐course dinner menu—each “step” points to a different recipe.
And an AIActionTemplate is a partially written dinner invite that you can quickly complete with names and dates.

Using these objects, you can whip up a consistent breakfast or an entire banquet without rewriting recipes from scratch each time.

Summary

Prompt/PromptLibrary and AIRecipe/AIRecipeLibrary bundle content templates into one place, eliminating repetition.
AIActionTemplate streamlines recurring actions (e.g., “send email,” “generate outline”) into reusable blocks.
SocialPost, RichContent, and CodeSnippet focus on preserving formatting and structure, so you don’t worry about markdown or code syntax.

2. Workflow & Decision Objects

Purpose & Rationale

Many entrepreneurial tasks are rarely one-and-done—they involve multiple decision points, conditional branches, and handoffs. Workflow and AIDecisionPoint objects give you a way to encode those multi-step processes declaratively. Instead of writing “First do this, then do that, if X occurs do Y,” you wrap everything in structured objects that the AI can interpret and execute.

Key Object Types

Workflow
- Definition: Represents a named sequence of steps or actions, each of which can be another CRAFT object (e.g., AIInstruction, APIRequest, RevisionRequest).
- Example:
```
python
```
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  Workflow( name="NewBlogPost", steps=[ AIInstruction("Research keywords for topic {topic}"), AIActionTemplate("GenerateOutline", topic="{topic}"), AIInstruction("Write draft based on outline"), RevisionRequest( target_id="draft_{topic}", change="Adjust tone to be more conversational", rationale="Target audience is non-technical entrepreneurs" ) ] )
  Once you call Workflow("NewBlogPost"), the AI knows to execute each step in order—passing outputs along the chain.
- Why This Matters: Entrepreneurs can define a “content‐creation pipeline” once and then reuse it for multiple topics. It’s like setting up a production line—each step is clearly defined, and the AI worker knows exactly what to do next.
AIDecisionPoint
- Definition: Encapsulates conditional logic within a workflow. Instead of embedding “if…then…” statements in prose, you declare a trigger metric and specify branches.
- Example:
```
python
```
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  AIDecisionPoint( trigger=Metric("CustomerSatisfaction", value=site_feedback_score, unit="score"), condition="value < 0.7", if_true=AIActionTemplate("InitiateEscalation", department="Support"), if_false=Instruction("SendThankYouEmail", template="StandardThankYou") )
  When the AI processes this, it evaluates the Metric—if the score is below 0.7, it executes InitiateEscalation; otherwise, it sends a thank-you email.
- Why This Matters: You no longer need to write out long “if X happens, do Y” instructions in paragraphs. Everything is wrapped in a single, readable object that the AI can parse directly—reducing misunderstandings and ensuring consistent branching.
APIRequest & APIResponse (closely tied to workflow)
- APIRequest: When part of your workflow involves fetching data (e.g., competitor pricing, weather data, or social-media metrics), you use an APIRequest object.
```
python
```
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  APIRequest( endpoint="/v1/competitors", method="GET", params={"industry": "cybersecurity", "region": "US"} )
- APIResponse: Once the AI receives that data, you wrap the response in APIResponse so it can parse status codes and payloads without misinterpreting them as free text.

Everyday Analogy

Imagine you’re assembling IKEA furniture:

A Workflow is like the instruction booklet that says, “Step 1: Attach leg A to shelf B. Step 2: Use screw X. Step 3: Tighten until flush.”
An AIDecisionPoint is like a note in the manual that says, “If you have Part C instead of Part D, use the alternate bracket.”
APIRequest/APIResponse is like ordering replacement parts—you place the order and then handle the delivered items differently depending on whether they’re correct (200 OK) or need to be exchanged (404 or 500).

By encapsulating everything in these objects, you minimize fumbling around figuring out “what comes next” and reduce the chance of assembling it backward.

Summary

Workflow bundles multi-step processes into one unit.
AIDecisionPoint encodes conditional branching so your AI interaction can adapt dynamically.
APIRequest/APIResponse integrate external data calls directly into your workflow, eliminating separate instructions.

3. Knowledge & Relationship Objects

Purpose & Rationale

CRAFT’s philosophy of “Cumulative Intelligence” means each session should build on the last, gradually forming a richer, shared knowledge base. Knowledge & Relationship Objects allow you to explicitly track entities and how they connect—rather than hoping the AI “remembers” something from weeks ago.

Key Object Types

KnowledgeGraphNode
- Definition: Represents a distinct entity or concept (e.g., a project, a competitor, a product) with associated properties. You can link nodes to one another, forming a mini-knowledge graph.
- Example:
```
python
```
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  KnowledgeGraphNode( id="proj_alpha", type="Project", properties={"status": "Ongoing", "budget": 50000}, relationships=[("managed_by", "user_jane"), ("uses_tech", "python")] )
  Now, if you later ask “Which projects use Python?” the AI can traverse that KnowledgeGraphNode instead of re-reading old chat logs.
- Why This Matters: Entrepreneurs often juggle multiple projects, vendors, or products. By capturing each as a node, you create a self-documenting “map” of your domain—so you only define “proj_alpha” once and refer to it everywhere.
DefinedRelationship
- Definition: Explicitly models the link between two entities (subjects and objects), along with an optional confidence score.
- Example:
```
python
```
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  DefinedRelationship( subject=Entity("Company A", category="organization"), predicate="partnership_with", object=Entity("Company B", category="organization"), confidence=0.95 )
  The AI now knows there’s a high-confidence partnership between Company A and Company B—making it easier to ask “List all partnerships for Company A.”
- Why This Matters: When you’re comparing competitors, suppliers, or collaborators, relationships matter. Rather than re-explaining “Company A works with Company B,” you declare it once and reference it programmatically.

Everyday Analogy

Imagine building a family tree:

Each KnowledgeGraphNode is like a person’s record (name, birthdate, interests).
A DefinedRelationship is like “Alice is Bob’s sibling” or “Charlie is Alice’s father.”
Once that family tree is in place, you can answer questions like “Who are Alice’s cousins?” without rehashing the entire lineage in each description.

Summary

KnowledgeGraphNode lets you capture entities and their properties once.
DefinedRelationship maps explicit links between those entities, boosting context and reasoning.

4. Validation & Formatting Objects

Purpose & Rationale

When you hand an AI a complex task—say, “Generate a compliance report in JSON”—it’s easy for the output to wander off spec. Validation & Formatting Objects define the rules and expected structure so that, instead of policing AI outputs manually, you embed the guardrails directly in your instructions.

Key Object Types

ConstraintSet
- Definition: A named, reusable collection of rules (e.g., tone, length, must-include/avoid terms). Once defined, you can attach a ConstraintSet to any instruction or object, and the AI will enforce those rules consistently.
- Example:
```
python
```
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  ConstraintSet( name="BrandVoiceGuidelines", rules=[ "Tone: professional_yet_approachable", "Must include: call_to_action", "Avoid: overly technical terms" ] )
  Later, if you ask AIInstruction("Write ad copy for product X").with(ConstraintSet("BrandVoiceGuidelines")), AI knows exactly which guardrails to apply.
- Why This Matters: Entrepreneurs often adhere to brand or regulatory guidelines. Embedding these rules into ConstraintSet ensures every AI output aligns with company policy—without rewriting guidelines every single time.
OutputSchema
- Definition: Specifies the exact fields, data types, and format for AI’s response. This goes beyond “make it bullet points”—you can demand a JSON object with keys for “competitor_name,” “market_share,” “growth_rate,” etc.
- Example:
```
python
```
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  OutputSchema( name="CompetitorReport", fields={ "competitor_name": "string", "key_product": "string", "estimated_revenue": Metric(unit="USD"), "summary": Summary(max_length=150) } )
  Now, AIInstruction("Generate a competitor report for {company}").with(OutputSchema("CompetitorReport")) guarantees you get a machine-parseable JSON—not a free-form paragraph.
- Why This Matters: Whether you’re feeding AI outputs into a dashboard, a spreadsheet, or another system, you need predictable structure. OutputSchema enforces that.
ComparisonMatrix
- Definition: A structured object for side-by-side comparisons. Entrepreneurs frequently evaluate options—software tools, vendors, pricing tiers. Instead of instructing AI to “layout a comparison table,” you give it a ComparisonMatrix object.
- Example:
```
python
```
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  ComparisonMatrix( items=["Software A", "Software B"], criteria=["Pricing", "Ease of Use", "Feature Set"], data=[ ["$50/mo", "High", True], ["$45/mo", "Medium", False] ] )
  You no longer have to tell AI “Make columns, label them X, Y, Z.” The ComparisonMatrix “knows” you want a 2×3 table with specific headers.
- Why This Matters: It drastically reduces back-and-forth when you need a clear, consistent format for evaluations—saving time and tokens.
Form & MultipleChoice
- Form: Declares a set of input fields (e.g., email, age, feedback). The AI can generate a form interface or validate entries without you writing validation logic in prose.
```
python
```
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  Form( fields=[ {"name": "email", "type": "string", "required": True}, {"name": "age", "type": "integer", "required": False} ] )
- MultipleChoice: Models multiple-choice questions so AI can render options and collect responses cleanly.
```
python
```
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  MultipleChoice( question="Preferred contact method?", options=["email", "phone", "sms"] )
- Why This Matters: When you need user input—surveys, polls, or data collection—these objects simplify the process, taking validation and formatting off your plate.

Everyday Analogy

Think of ConstraintSet as a “style guide” folder on your desktop—once you save it, every document you produce uses the same fonts, margins, and branding without retyping the instructions.
OutputSchema is like the template your accountant uses to submit quarterly figures—it has defined fields for “Revenue,” “Expenses,” “Profit,” etc., so you never have missing data.
A ComparisonMatrix is analogous to a spreadsheet you’d set up to compare two business proposals side by side, complete with rows for “Cost,” “Timeline,” and “ROI.”

Summary

ConstraintSet ensures every AI output follows predefined rules—tone, mandatory terms, forbidden terms.
OutputSchema guarantees structured, machine-readable responses (JSON, CSV), making downstream processing seamless.
ComparisonMatrix provides a standardized way to generate comparison tables, saving formatting hassles.
Form/MultipleChoice handle user input cleanly, enabling AI to validate and format responses automatically.

5. Versioning & Iteration Objects

Purpose & Rationale

Good projects evolve. As you refine strategies, iterate on drafts, or tweak workflows, you need a way to track and reference past states so you can roll back if needed or compare versions side by side. RevisionRequest and VersionTag give you that version control, directly in your AI chats.

Key Object Types

RevisionRequest
- Definition: A structured instruction to modify a specific piece of content you’ve already generated. It references the content by an identifier (target_id), states what needs to change, and provides a rationale.
- Example:
```
python
```
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  RevisionRequest( target_id="blog_post_draft_1", change="Rewrite the introduction to be more engaging", rationale="Current version is too dry for our audience" )
  Once you send this, the AI knows exactly which draft to update and why—no need to paste paragraphs or re-explain.
- Why This Matters: In multi-session projects, you can request revisions without recontextualizing everything. You simply say, “Revise X,” and the AI picks up the content from its stored state.
VersionTag
- Definition: Assigns a version label (and an optional timestamp) to any CRAFT object or piece of content. This makes it easy to refer back to “v1.0” or “v2.1-FinalReview” without confusion.
- Example:
```
python
```
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  VersionTag( target_id="business_plan_doc", tag="v2.1-FinalReview", timestamp="2025-05-05T12:30:00Z" )
  Later, if you need to compare “v2.0” and “v2.1,” the AI can fetch both states and show you the diffs—much like Git but in natural language.
- Why This Matters: Entrepreneurs working on evolving documents—business plans, pitch decks, brand guidelines—can keep a clean history and avoid accidentally building on outdated content.

Everyday Analogy

This is analogous to version control in software (Git tags) or the “Track Changes” feature in Word:

A RevisionRequest is like inserting a comment in Word that says, “Rewrite paragraph 2 to sound friendlier.”
A VersionTag is like saving the document as “BusinessPlan_v2.1” so you always know which iteration you’re looking at.

Summary

RevisionRequest organizes feedback and iteration loops, letting AI update content directly by reference.
VersionTag provides explicit labels for object states, facilitating side-by-side comparisons and rollbacks.

6. Extended & Specialized Objects

While the above four categories cover a majority of common use cases, CRAFT also includes specialized objects that handle more nuanced tasks. Below are a few noteworthy examples:

Metric & TimeSeries
- Metric: Stores a named value (e.g., “conversion_rate”) with units, trends, and context—standardizing how you track KPIs.
```
python
```
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  Metric("conversion_rate", 3.2, unit="percent", trend="increasing")
  Whenever you ask AI to “evaluate our KPIs,” you pass a list of Metric objects rather than embedding numbers in text.
- TimeSeries: Encapsulates a sequence of data points over time, which AI can use to generate trend analyses or forecasts.
```
python
```
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  TimeSeries( data=[{"date": "2025-04-01", "value": 120}, {"date": "2025-05-01", "value": 135}], metric="sales" )
  This object tells AI, “Here’s your sales history—plot it, analyze seasonality, or forecast next month.”
BusinessLogic & ComplianceRule
- BusinessLogic: Encodes company-specific decision rules (e.g., “if loyalty_years > 1, apply 10% discount”).
```
python
```
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  BusinessLogic( name="DiscountEligibility", condition="user.loyalty_years > 1", action="apply_discount(10%)" )
  Rather than describing your discount policy in a paragraph, you give AI a formal rule to follow.
- ComplianceRule: Captures regulatory guidelines (GDPR, HIPAA, etc.) so AI can flag or handle potential compliance issues.
```
python
```
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  ComplianceRule( regulation="GDPR", requirement="user_consent_required", action="log_consent()" )
  This ensures that whenever AI processes personal data, it automatically enforces “log_consent()” if needed—no manual reminders.
Checklist, Form, MultipleChoice (overview)
- Checklist: Turns a to-do list into a structured object that AI can mark “complete” or “pending,” then remind you if any items are left.
- Form / MultipleChoice: As discussed earlier, handles user input with built-in validation and formatting.
StructuredArgument
- Definition: Packages a claim supported by evidence and reasoning. This is particularly useful for building persuasive business cases or investor decks.
```
python
```
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  StructuredArgument( claim="Invest in Solar Panels", evidence=[ Metric("Energy Savings", value=15, unit="percent"), MediaReference("CaseStudy.pdf") ], reasoning="Long-term cost reduction and environmental benefits outweigh initial investment." )
  AI can process this to produce a well-structured, logical argument rather than a meandering essay.

Everyday Analogy

Think of these specialized objects as the “power tools” in your entrepreneur’s toolkit:

Metric and TimeSeries are like your digital dashboard, updating you on KPIs in real time.
BusinessLogic and ComplianceRule are your company’s policy manuals—AI simply follows the instructions without you having to repeat them.
StructuredArgument is like your “elevator pitch” template—concise, evidence-backed, and ready for investors.

Summary

Metric/TimeSeries turn raw numbers into standardized data objects for trend analysis.
BusinessLogic/ComplianceRule codify company rules and regulations, so AI enforces them automatically.
StructuredArgument ensures persuasive content follows a clear, evidence-backed structure.

Overall Summary of Core Categories

Content-Oriented Objects (Prompt, PromptLibrary, AIRecipe, AIActionTemplate, SocialPost, RichContent, CodeSnippet) let you package content templates into reusable, easy-to-reference pieces.
Workflow & Decision Objects (Workflow, AIDecisionPoint, APIRequest/APIResponse) transform multi-step tasks and conditional branches into single, declarative constructs.
Knowledge & Relationship Objects (KnowledgeGraphNode, DefinedRelationship) build a persistent, queryable knowledge graph that AI can traverse for richer, multi-session reasoning.
Validation & Formatting Objects (ConstraintSet, OutputSchema, ComparisonMatrix, Form, MultipleChoice) embed rules and expected structures directly into your prompts, ensuring consistent, machine-readable outputs.
Versioning & Iteration Objects (RevisionRequest, VersionTag) give you built-in version control, so you can iterate on drafts without losing track of changes.
Extended & Specialized Objects (Metric, TimeSeries, BusinessLogic, ComplianceRule, StructuredArgument, Checklist, etc.) cover niche but high-value use cases—KPIs, compliance, persuasive arguments, user input, and more.

By organizing your AI interactions around these categories, you create a system that is:

Modular: Build once, reuse everywhere.
Maintainable: Change object definitions in one place, and every prompt that references it automatically updates.
Scalable: As your prompt library and project complexity grow, you avoid “prompt spaghetti” and keep things tidy.
Context-Rich: Multi-session projects become easier because objects like KnowledgeGraphNode and VersionTag persist state.

Use this breakdown as a reference when designing new CRAFT objects, and lean on these categories to ensure your AI‐powered workflows remain consistent, efficient, and aligned with entrepreneurial needs.

1. Modularity & Reusability

Detailed Explanation
When you encapsulate related data and behavior into an object (for example, a Prompt, Workflow, or Metric), you create a self-contained unit that can be used anywhere without rewriting it. This modularity means:

Single Source of Truth: Define your prompt template once (e.g., Prompt(template="Summarize {topic}")) and instantiate it with different topics. If you later tweak the template—say, adding “Focus on key takeaways”—you update that one object definition and every invocation automatically inherits the change.
Reduced Boilerplate: Instead of copy‐pasting nearly identical instructions across dozens of chats (“Summarize the quarterly report,” “Summarize the annual review,” etc.), you create a PromptLibrary that stores them all in one place. CRAFT objects can be parameterized so you fill in only what’s unique each time.
Plug-and-Play: Suppose you build a complex multi-step Workflow("NewProductLaunch") that includes market research, competitor analysis, and a rollout plan. Next week, to launch a different product, you simply instantiate that same workflow object with new parameters. You don’t have to rebuild the sequence from scratch.

Everyday Analogy
Think of LEGO bricks:

A single brick type (e.g., a 2×4 black brick) can be used in countless builds.
If you want to change that brick’s color or shape, you replace it in the LEGO set, and all models referencing it update at once (in CRAFT, you’d modify the object definition).
Rather than gluing paper cutouts together each time you build something (free-form prompts), you click bricks together—modular, interchangeable, and easy to manage.

Short Summary

Objects let you define once and reuse everywhere.
They eliminate repetitive typing, ensure consistency, and accelerate prompt‐building.

2. Clarity & Maintainability

Detailed Explanation
When you wrap instructions in a named object, you replace long, meandering prose with a clear declaration. For example:

Free-Form Prompt:
“Please analyze the social media engagement data for the last quarter—look at likes, shares, comments, and identify any spikes or sudden drops. Then suggest three actionable recommendations to improve engagement, focusing on content types, posting times, and platform-specific tactics, making sure to write in a friendly but professional tone that matches our brand guidelines.”

Versus:

python

CopyEdit

AIInstruction( action="AnalyzeEngagement", data_source="social_media_Q2", metrics=["likes", "shares", "comments"], output_format="spike_trough_report", recommendations_count=3, tone="friendly_professional", constraints=ConstraintSet("BrandVoiceGuidelines") )

Benefits of the object version:

Unambiguous Fields: Every field has a clear label (action, data_source, metrics, etc.). No guesswork about what “analyze” entails.
Consistent Interpretation: CRAFT’s engine already understands how to process AIInstruction(action="AnalyzeEngagement", ...)—so you avoid AI “hallucinations” or misinterpretations that can happen with plain‐text.
Easier Maintenance: If you want to switch the tone to “formal_informative,” you just change the tone value. You don’t need to rewrite sentences to convey formality.
Better Documentation: Because the object’s fields are self-explanatory, anyone (or any AI assistant) reading it next week can instantly know what each parameter does.

Everyday Analogy
Compare writing instructions on a sticky note versus filling out a standardized form:

Sticky Note (Free-Form): “When you arrive, buzz me, I’ll get the files from the cabinet, check them for errors, and then email the revised version by 2 PM.” If you read that next month, you might forget which cabinet, what “check for errors” means exactly, or which email to use.
Form (Object):
- Task: “RetrieveFiles”
- Location: “Cabinet A, Drawer 2”
- Operation: “ValidateSpelling, GrammarCheck”
- Output: “SendToEmail(user@example.com) by 14:00”
  You never wonder about missing details, because the form fields force you to be explicit.

Short Summary

Objects replace ambiguous paragraphs with clear, labeled fields.
This ensures AI interprets your intent precisely and makes updates trivial.

3. Scalability & Context Persistence

Detailed Explanation
When projects or conversations grow, free-form prompts can’t reliably “remember” past context. Objects solve this by persisting state in machine-readable form:

Stateful Objects:
- A KnowledgeGraphNode("proj_alpha") created in session one is still recognized in session ten. The AI doesn’t need you to re‐explain “proj_alpha is our upcoming cybersecurity dashboard”; the object’s properties carry that knowledge forward.
- A VersionTag(target_id="proj_alpha_doc", tag="v3.0") lets you recall exactly which draft to modify later, without copying/pasting entire documents.
Large-Scale Workflows:
- Suppose you have a 15-step Workflow("AnnualReport"). As your team works on sections across multiple weeks, you don’t rewrite “Next, perform competitor analysis” each time. The AI reads the Workflow object, sees the next incomplete step, and continues seamlessly.
Parallel Projects:
- If you run several product launches simultaneously—Workflow("ProductA_Launch"), Workflow("ProductB_Launch"), Workflow("ProductC_Launch")—each object keeps its own context. AI doesn’t mix them up because each object has a unique identifier and set of parameters.

Because objects persist state across sessions, you’re effectively building a shared “project memory” rather than starting from scratch every time.

Everyday Analogy
Think of tracking multiple ongoing home renovations:

Without Objects (Free-Form): You write sticky notes like “Call plumber,” “Buy paint,” “Schedule electrician.” But after a few weeks, you lose track of which note goes with which room. If you go back to it, you have to re‐read every sticky note.
With Objects: You have a digital project management tool where each room is a “Task Object” with fields:
- Task("PaintMasterBedroom", status="InProgress", next_step="Choose color samples")
- Task("InstallBathroomFixtures", status="PendingInspection", next_step="Confirm water pressure")
  Everything is organized, each task remembers its state, and you can pick up where you left off instantly.

Short Summary

Objects hold state so AI can reconnect to ongoing projects without re-explaining everything.
They support multiple parallel workflows and multi-session collaborations seamlessly.

4. Token Efficiency & Cost Savings

Detailed Explanation
Every word you send to a paid AI model consumes tokens—and tokens translate directly into cost. By swapping free-form instructions (which might run 40–60 tokens for a single command) for concise object instantiations (often 5–10 tokens), you save money and speed up responses:

Concise Syntax:
- Free-form: “Plot a bar chart of monthly website traffic, labeling the X-axis ‘Month’ and the Y-axis ‘Visits’, coloring the bars blue, and include data points for at least the past 12 months.” (~50 tokens)
- Object-based:
```
python
```
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  ChartSpec( type="bar", x="month", y="visits", title="Monthly Website Traffic", data_source="traffic_last12months" )
  (~8 tokens for the object name + field references; exact count varies by model, but markedly lower).
Fewer Ambiguities:
- Because the object’s schema defines defaults (e.g., default bar color, default chart style), you don’t need to restate them every time.
Automated Defaults:
- Many objects have sensible defaults baked in. A simple ChartSpec(type="bar", x="month", y="visits") might default to your brand’s color palette, axis labels formatted with title case, and gridlines enabled—so you never have to specify “color: #FF6600” or “grid: true.”

The bar chart above illustrates this point:

Free-text Prompt: ~50 tokens
Object-based Prompt: ~5 tokens
Over many interactions, those saved tokens compound into significant cost reductions—especially when running iterative, multi-step workflows.

Everyday Analogy
It’s like sending a short “URL” instead of a long paragraph explaining how to find something online:

Instead of writing “Go to Google, type ‘best cyber security tools,’ click on the first result, scroll to section 3, and copy the list of tools,” you send “Open https://bit.ly/CyberToolsList.”
The latter is much shorter, quicker to process, and less likely to have typos or misunderstandings.

Short Summary

Objects drastically reduce token usage by replacing verbose text with concise, structured calls.
Over time, this translates into material cost savings and faster AI responses.

5. Business Alignment & Compliance

Detailed Explanation
When you encode business rules, brand guidelines, or compliance requirements as objects (BusinessLogic, ConstraintSet, ComplianceRule), you ensure every AI-generated output conforms to your organization’s standards:

Centralized Policies:
- Instead of reminding AI “always use our brand voice,” you attach ConstraintSet("BrandVoice") to every content‐related object. If your brand voice changes—e.g., shifting from “professional_corporate” to “friendly_innovative”—you edit the ConstraintSet, and all future outputs automatically update.
Regulatory Guardrails:
- Sensitive industries (healthcare, finance, legal) have strict rules. A ComplianceRule("HIPAA", requirement="anonymize_patient_data") object ensures AI never outputs personally identifiable patient data in medical summaries.
Auditability:
- Objects carry metadata about who created them, when, and for what purpose. This makes auditing AI outputs simple: you can query all AIInstruction objects that used ConstraintSet("FinancialGuidelines") in the last quarter.
Consistency Across Teams:
- Marketing, Legal, and Engineering can centralize their domain rules in CRAFT objects. When anyone requests a “ProductFeatureList,” AI references those shared objects—avoiding conflicting interpretations between departments.

Everyday Analogy
Consider a restaurant chain with a standardized recipe book:

Each franchise follows the exact steps for making a burger—same ingredients, same cooking time, same plating instructions.
If the headquarters changes the recipe (say, swapping cheddar for pepper jack), they update one “RecipeObject,” and every franchise automatically knows the new process.
The recipe book is the BusinessLogic; every kitchen (AI session) follows it to ensure brand consistency and compliance.

Short Summary

Business rules and compliance requirements become enforceable, reusable objects.
Companies can guarantee brand voice and regulatory adherence without repetitive instructions.

6. Collaboration & Knowledge Transfer

Detailed Explanation
CRAFT objects make it easier for multiple stakeholders—writers, analysts, executives—to share a common “language” when interacting with AI:

Shared Object Repositories:
- A central PromptLibrary or WorkflowLibrary can be version‐controlled and shared across teams. Everyone sees the same definitions and conventions.
Lower Onboarding Overhead:
- New team members don’t need to learn years of free-form prompting habits. They can browse the object spec (e.g., see that Metric("roi", value, unit="percent") means “evaluate ROI”) and start using objects right away.
Clear Handovers:
- If Alice builds a KnowledgeGraphNode("CompetitorX") with key properties and Bob picks up next week, Bob sees exactly what’s stored—no need to re-ask, “What did we learn about CompetitorX?”
Reduced Miscommunication:
- With free-form prompts, one person’s “summarize” might be another person’s “deep dive.” By standardizing on OutputSchema("ExecutiveSummary"), everyone knows “ExecutiveSummary” yields bullet points under 150 words with a “recommendations” section.

Everyday Analogy
It’s like using a shared template in a project management tool:

Instead of everyone creating tasks in their own style (some write “Call vendor,” others “Reach out to vendor for pricing”), you have a “VendorOutreach” task template with fields “VendorName,” “ContactDate,” “Purpose,” and “Status.”
Anyone can fill out that template, and downstream reports always look uniform.

Short Summary

Objects provide a common vocabulary, reducing onboarding time and miscommunication.
They enable clean handoffs and collaborative work without re‐explaining context.

Chart: Token Usage Savings

Below is a bar chart illustrating token usage for a comparable task implemented once as a free-form prompt and once as an object-based prompt. Notice the dramatic reduction in tokens (and therefore potential cost savings) when using CRAFT objects:

Overall Summary of Benefits

Modularity & Reusability
- Define once, reuse everywhere—akin to reusable LEGO bricks.
Clarity & Maintainability
- Objects replace ambiguous text with explicit fields—like filling out a standardized form instead of a sticky note.
Scalability & Context Persistence
- Objects hold state across sessions, akin to a digital project management tool that remembers every task’s status.
Token Efficiency & Cost Savings
- Swapping 50-token paragraphs for 5-token object calls dramatically cuts costs—like using short URLs instead of long explanations.
Business Alignment & Compliance
- Encode brand and regulatory rules directly into objects, ensuring uniform adherence—similar to a franchise recipe book.
Collaboration & Knowledge Transfer
- Shared object definitions act as a common vocabulary, reducing onboarding friction—like using shared task templates in project management.

By leveraging these benefits, entrepreneurs and teams can treat AI interactions as a structured, maintainable, and cost-effective extension of their workflows—rather than ad-hoc, one-off chats.

Sources

You said:

Great, please expand on Creative Adaptation Ideas for CRAFT Objects

ChatGPT said:

Below is an expanded look at Creative Adaptation Ideas for CRAFT Objects—practical ways you can combine and extend object types to solve entrepreneurial challenges, drive dynamic AI workflows, and push the boundaries of what CRAFT can do. Each adaptation includes concrete examples, benefits, and everyday analogies to illustrate how these ideas play out in real use cases.

1. Dynamic Persona Switching via AIPersona Objects

Detailed Explanation

In many entrepreneurial scenarios, you need AI to “wear different hats” depending on the task—or even switch mid‐conversation. For instance, you might want a technical deep dive from an engineer persona, followed by a high‐level summary from a marketing persona. A dedicated AIPersona object makes that switch explicit.

Object Definition:
```
python
```
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AIPersona( name="MarketingCopywriter", expertise="write engaging copy with emotional appeal", tone="conversational", prohibited_terms=["jargon", "acronyms without explanation"] )
Here, the object’s fields (expertise, tone, prohibited_terms) codify how that persona speaks.
Usage Example:
Suppose you already generated a rough product spec, and now you want marketing copy. You’d write:
```
python
```
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#H->AI::Instruction: "Create a product description for {product_name}" .using(AIPersona("MarketingCopywriter"))
Behind the scenes, CRAFT’s engine interprets .using(AIPersona("MarketingCopywriter")) and loads that persona’s style rules, so the AI knows to write in an emotionally resonant, jargon‐light way.
Switching Personas Mid‐Flow:
After the marketing copy, you need a technical review. You can chain:
```
python
```
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AIInstruction("Review the technical feasibility") .using(AIPersona("TechnicalReviewer"))
The TechnicalReviewer persona might be defined as:
```
python
```
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AIPersona( name="TechnicalReviewer", expertise="assess system architecture, APIs, and scalability", tone="concise_informative", required_formats=["UML diagrams", "BulletList"] )
Now, the AI pivots instantly—no need to restate “Write more technically.” The CRAFT engine already adjusts its output style based on that persona object.

Benefits

Consistent Tone Across Tasks: Each persona object ensures a uniform voice whenever that persona is invoked. If your marketing guidelines change, you tweak AIPersona("MarketingCopywriter") once, and every future marketing‐oriented instruction updates automatically.
Frictionless Transitions: Switching roles requires no extra explanation—just reference a different AIPersona. This enables complex dialogues (e.g., CFO persona for budgeting, HR persona for hiring) within the same chat session.
Clear Documentation & Ownership: Personas act like “role cards” in a playbook. Everyone on your team knows exactly which persona to use for sales pitches, technical write‐ups, or customer support responses.

Everyday Analogy

Imagine a customer support center where agents change headsets to switch functions:

Putting on the “Sales” headset routes you to pitch training, pricing scripts, and upsell tactics.
Hanging that up and grabbing the “Technical Support” headset gives you access to troubleshooting guides, code snippets, and escalation paths.
CRAFT’s AIPersona works the same way: assign a persona object, and AI “puts on” the corresponding role.

2. Parametric Prompt Libraries through Prompt Objects

Detailed Explanation

Rather than scavenging through old chats to find that one perfect prompt, you can build a parametric library of Prompt objects. These objects serve as blueprints, complete with named placeholders, so you only supply the specifics at runtime.

Object Definition:
```
python
```
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Prompt( name="QuarterlyEarningsSummary", template="Summarize the {quarter} earnings report for {company} focusing on {key_focus}.", variables=["quarter", "company", "key_focus"], default_format="BulletList", recommended_length="short" )
Key fields:
- template: The text pattern with placeholders ({quarter}, {company}, {key_focus}).
- variables: Names of placeholders so CRAFT knows what to ask you if any are missing.
- default_format & recommended_length: Implicit instructions baked into the object so you don’t have to restate “use bullets” every time.
Usage Example:
If you need a summary for Q3 earnings of “Acme Corp,” you do:
```
python
```
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PromptLibrary("FinancialSummaries").get("QuarterlyEarningsSummary") .fill(quart er="Q3", company="Acme Corp", key_focus="expenses_vs_revenue") .execute()
The fill() method populates placeholders and passes the prompt to AI. Because default_format="BulletList", AI outputs a concise bulleted summary.
Batch Processing:
Suppose you manage multiple portfolio companies and want to generate a one‐page summary for each. You can loop:
```
python
```
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for company in ["Acme Corp", "BetaTech", "GammaSoft"]: PromptLibrary("FinancialSummaries").get("QuarterlyEarningsSummary") .fill(quarter="Q3", company=company, key_focus="net_income") .execute()
CRAFT automatically instantiates three prompt objects, and AI produces three separate summaries—no retyping, no copy‐pasting.

Benefits

Scalable Content Generation: Easily scale from one‐off prompts to hundreds of reports by looping over a list of parameters.
Error Reduction: Because your variables are defined up front, missing or misspelled placeholders trigger a CRAFT‐level validation error rather than a half‐baked prompt to AI.
Rapid Iteration: If you decide you want paragraphs instead of bullet lists, change default_format="Paragraph" in the Prompt object. All future executions reflect that change.

Everyday Analogy

Think of mail merge in Microsoft Word:

You design a single letter template with fields for FirstName, LastName, Address.
You import a CSV, and Word generates personalized letters automatically.
Parametric prompts replicate that concept: one template drives many customized outputs.

3. Automated Research Pipelines with Workflow + APIRequest Objects

Detailed Explanation

Entrepreneurs often need to pull data from external sources—competitor pricing, customer reviews, or social trends—and then analyze it. By combining Workflow and APIRequest objects, you can orchestrate an end‐to‐end research pipeline:

Step 1 – Data Collection (APIRequest):
```
python
```
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APIRequest( name="FetchCompetitorData", endpoint="/v1/competitor_pricing", method="GET", params={"industry": "cybersecurity", "region": "US"} )
This object instructs CRAFT to fetch competitor pricing data from your chosen API.
Step 2 – Preliminary Analysis (AIInstruction):
```
python
```
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AIInstruction( action="AnalyzeJSON", data_source="FetchCompetitorData.response", output_format="ComparisonMatrix", criteria=["Price", "FeatureSet", "CustomerRating"] )
Here, FetchCompetitorData.response references the JSON payload returned by the previous step. CRAFT’s engine interprets that, parses JSON, and outputs a ComparisonMatrix between competitors.
Step 3 – Recommendation Generation (AIInstruction with OutputSchema):
```
python
```
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AIInstruction( action="GenerateRecommendations", input="ComparisonMatrix", output_format="JSON", schema=OutputSchema( name="CompetitorInsights", fields={ "competitor": "string", "strengths": "list[string]", "weaknesses": "list[string]", "recommendation": "string" } ) )
Now the AI takes the comparison table and produces a structured JSON object with insights for each competitor.
Packaging as a Workflow:
```
python
```
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Workflow( name="CompetitorAnalysisPipeline", steps=[ APIRequest("FetchCompetitorData", ...), AIInstruction("AnalyzeJSON", ...), AIInstruction("GenerateRecommendations", ...) ] )
With this one Workflow, you’ve defined an automated pipeline: fetch data → analyze → generate recommendations. When you call Workflow("CompetitorAnalysisPipeline"), CRAFT executes each step in sequence, passing outputs transparently.

Benefits

No Manual Data Handling: You don’t copy‐paste JSON or manually instruct AI to parse it. The APIRequest and AIInstruction objects handle parsing and passing of results.
End‐to‐End Reusability: Save the entire pipeline in your WorkflowLibrary. Next quarter, you update API params or change the recommendation schema—your code remains in one place.
Error Handling & Conditional Branching: You can attach an AIDecisionPoint after the APIRequest to check response.status_code != 200 and branch accordingly, triggering an alert rather than proceeding.

Everyday Analogy

Imagine an assembly line in a factory:

Station 1 automatically fetches raw materials (APIRequest).
Station 2 transforms materials into subcomponents (AIInstruction analyzing JSON).
Station 3 packages the finished product (AIInstruction generating recommendations).
The Workflow is the conveyor belt that links them—everything happens automatically once you press “Start.”

4. Adaptive Decision Paths Using AIDecisionPoint + Metric Objects

Detailed Explanation

Some business processes require dynamic branching—for instance, if a customer NPS score falls below a threshold, you trigger an escalation. AIDecisionPoint objects let you encode these “if‐then‐else” conditions without descriptive paragraphs:

Step 1 – Collect a Metric:
```
python
```
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Metric( name="CustomerNPS", value=latest_nps_score, unit="score", threshold={"good": 8, "neutral": 7, "poor": 6} )
This object captures the latest NPS result and defines benchmark thresholds.
Step 2 – Define Decision Logic:
```
python
```
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AIDecisionPoint( name="FollowUpAction", trigger=Metric("CustomerNPS", value=latest_nps_score, unit="score"), condition="value < threshold['poor']", if_true=AIActionTemplate("InitiateSupportEscalation", team="CustomerSuccess"), if_false=AIActionTemplate("SendSurveyThankYou", template="StandardThanks") )
The condition checks if value (e.g., 5) is below threshold['poor'] (6). If so, it invokes the “InitiateSupportEscalation” template; otherwise, it sends a thank‐you email.
Step 3 – Embed in a Workflow:
```
python
```
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Workflow( name="PostSurveyWorkflow", steps=[ AIInstruction("CollectNPSScore", customer_id="{cid}"), AIDecisionPoint("FollowUpAction", trigger=Metric(...), ...), AIInstruction("LogOutcome", data_source="FollowUpAction.result") ] )
With this Workflow, once you collect a survey score, CRAFT automatically chooses the next action.

Benefits

Declarative Branching: No multi‐sentence “If NPS is < 6, do X; else do Y.” Everything is in one structured object.
Reusable Decision Templates: If you run multiple surveys (CSAT, CES, NPS), you can parameterize the same AIDecisionPoint logic with different Metric names and thresholds.
Clear Audit Trail: Because each branch is a named object, you can query “Which customers triggered an escalation in the last month?”—AI can filter logs based on the AIDecisionPoint name.

Everyday Analogy

Think of a “choose your own adventure” book:

At each page, you read a prompt: “If you go into the forest, turn to page 32; if you enter the castle, turn to page 45.”
Each decision point is an AIDecisionPoint object—concise, unambiguous, and easy to follow. The book “knows” how to direct you based on your choice without reprinting the entire storyline.

5. Evolving Knowledge Graphs via KnowledgeGraphNode + DefinedRelationship Objects

Detailed Explanation

A core tenet of “Cumulative Intelligence” is that your AI system gradually accumulates knowledge about entities and how they relate. By treating entities (people, products, partners) as KnowledgeGraphNode objects and explicitly linking them with DefinedRelationship, you build a graph that the AI can query for richer insights:

Creating Knowledge Nodes:
```
python
```
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KnowledgeGraphNode( id="Vendor_XYZ", type="Vendor", properties={ "industry": "CloudHosting", "year_founded": 2010, "reputation_score": 4.5 } ) KnowledgeGraphNode( id="Project_Alpha", type="InternalProject", properties={"start_date": "2025-01-01", "budget": 100000} )
Linking with DefinedRelationship:
```
python
```
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DefinedRelationship( subject="Vendor_XYZ", predicate="supplies_to", object="Project_Alpha", confidence=0.92 )
Now the AI “knows” Vendor_XYZ supplies resources to Project_Alpha.
Querying the Graph:
Later, you can ask:
```
python
```
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AIInstruction("ListAllVendorsForProject", input="Project_Alpha")
Because CRAFT stored DefinedRelationship, AI traverses the graph and returns “Vendor_XYZ.” You can then chain:
```
python
```
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AIInstruction("EvaluateVendor", input="Vendor_XYZ", criteria=["cost", "reliability"])
And so on.
Expanding Over Time:
Each new vendor, partner, or product is a new KnowledgeGraphNode. Relationships (partnership_with, competitor_of, uses_tech) grow organically. Over months, you end up with a rich, semantically linked map of your ecosystem.

Benefits

Persistent, Structured Memory: Rather than hoping AI “remembers” that Vendor_XYZ is linked to Project_Alpha, the relationship is stored as a CRAFT object.
Rich Contextual Reasoning: When making strategic decisions, AI can pull from the graph. For example, “Identify all internal projects that use Python and list their vendors.”
Easier Onboarding: New team members can inspect the knowledge graph to see “Which vendors do we currently engage?” or “What projects use AWS?”—instead of digging through chat transcripts.

Everyday Analogy

Think of a mind‐map or a concept map:

You start with one bubble labeled “Company.” You draw lines to “Product A,” “Product B.” Then each product connects to its “Vendors,” “Features,” “Stakeholders.”
Over time, the map fills out—an instant visual of how everything is connected. CRAFT’s KnowledgeGraphNode and DefinedRelationship create that same structure behind the scenes, but in a machine-readable form.

6. Contextual Output Enforcement with OutputSchema + ConstraintSet

Detailed Explanation

When your AI outputs need to follow strict formatting or content rules—whether it’s for regulatory compliance, brand consistency, or downstream machine parsing—you combine OutputSchema (for structural enforcement) with ConstraintSet (for stylistic and policy enforcement).

Defining an Output Schema:
```
python
```
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OutputSchema( name="InvestorPitchDeck", fields={ "title_slide": {"title": "string", "subtitle": "string"}, "problem_statement": "paragraph", "solution_overview": "paragraph", "market_analysis": { "market_size": "number", "growth_rate": "number", "key_trends": ["string"] }, "financial_projections": "table" } )
This schema spells out exactly which sections appear and what type of data goes in each.
Attaching a Constraint Set:
```
python
```
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ConstraintSet( name="PitchDeckBrandGuidelines", rules=[ "Use company logo in top-left corner of each slide", "Limit slides to 10 max", "Tone: Inspirational yet data-driven", "Font family: Arial or Helvetica" ] )
Prompting AI:
```
python
```
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AIInstruction( action="GenerateInvestorPitchDeck", input="CompanyOverviewData", output_schema=OutputSchema("InvestorPitchDeck"), constraints=ConstraintSet("PitchDeckBrandGuidelines") )
Because you’ve provided both structure and style constraints, AI produces a 10‐slide outline (or actual slide content) that matches your brand and is guaranteed to follow the schema—each JSON object or slide section appears exactly as specified.
Automated Validation:
Later, if someone inadvertently passes malformed data (e.g., a numeric value for a field expecting text), CRAFT raises a validation error before AI even generates content. This pre‐emptively catches mistakes instead of discovering them in post hoc reviews.

Benefits

Guaranteed Consistency: Every output across multiple sessions or team members adheres to the same structure and brand rules.
Machine-Parseable Results: Systems downstream (e.g., BI dashboards, partner integrations) can ingest AI outputs without additional parsing code—because they conform to OutputSchema.
Reduced Manual QA: Editors no longer need to check slide counts, logo placement, or tone. CRAFT enforces these rules automatically.

Everyday Analogy

Imagine a fillable PDF form with strict fields and checkboxes (e.g., a government tax form). You can’t write “lots of words” in a numeric field, and you can’t skip mandatory sections. CRAFT’s OutputSchema and ConstraintSet are that PDF: they force compliance before content exists.

Summary of Creative Adaptations

AIPersona Objects:
- Switch AI roles seamlessly (e.g., “MarketingCopywriter” vs. “TechnicalReviewer”) by invoking different persona objects.
Parametric Prompt Libraries:
- Store prompt templates with placeholders in a PromptLibrary for batch content generation and rapid iteration.
Automated Research Pipelines:
- Chain APIRequest, AIInstruction, and OutputSchema in a Workflow so AI automatically fetches, analyzes, and summarizes external data.
Adaptive Decision Paths:
- Use AIDecisionPoint with Metric objects to branch logic “if metric < threshold, do X; else do Y” without free‐form conditionals.
Evolving Knowledge Graphs:
- Build KnowledgeGraphNode and DefinedRelationship objects to form a persistent, queryable map of your domain—enabling cumulative intelligence.
Contextual Output Enforcement:
- Combine OutputSchema and ConstraintSet to guarantee that AI outputs follow strict formatting and brand/regulatory rules.

By creatively adapting and composing these CRAFT objects, you turn AI prompts into dynamic, robust workflows—moving from one‐off instructions to a self‐documenting, reusable system that scales as your business grows.

Sources

You said:

Great, please expand on Practical Steps to Define New Custom Object Types

ChatGPT said:

Thought for 10 seconds

CRAFT Object Definition Steps

Now interactive!

StepDescriptionExampleBenefit1. Identify Recurring PatternsLook for tasks or data structures you repeatedly describe in prose.Noticing repeated 'analyze financial data' instructions becomes a 'FinancialAnalyzer' object.Minimizes rewriting the same instructions and reduces errors.2. Draft a Declarative Data ModelDefine the object's attributes and intended behaviors in a structured format.Create FinancialAnalyzer(name, data_source, metrics) with fields and methods.Ensures clarity, making it easier to maintain and update schemas.3. Embed Examples in SpecAdd code snippets in the CRAFT spec showing how to instantiate and use the object.In CRAFT spec: ``FinancialAnalyzer(name="Q1Report", data_source="sales_data", metrics=["revenue", "profit"])``Demonstrates usage, reducing ambiguity for future users.

Below is a detailed expansion of the Practical Steps to Define New Custom Object Types. Each step includes explanations, examples, and analogies to help you apply these ideas in real-world CRAFT workflows. At the end, you’ll find a summary table that recaps all steps, examples, and benefits for quick reference.

1. Identify Recurring Patterns or Complex Tasks

Detailed Explanation

Start by reviewing the prompts and processes you’ve used so far. Notice any instructions or data structures you describe repeatedly in free-form text. Those are prime candidates for conversion into objects. Common patterns include:

Repeated Task Descriptions:
If you frequently ask AI to “analyze financial data,” “generate a summary of product features,” or “compare vendor offerings,” that’s a sign you could define a FinancialAnalyzer, FeatureSummarizer, or VendorComparer object. Instead of retyping the instructions each time, bundle the logic into one object.
Complex Data Structures:
Whenever you manually format a JSON schema, comparison table, or chart specification in prose, think about creating an OutputSchema, ComparisonMatrix, or ChartSpec object. By identifying these repeating structures, you reduce the chance that slight wording changes will introduce inconsistencies.
Conditional Logic Blocks:
If you find yourself embedding “if…then…” logic in paragraphs—e.g., “If revenue growth is below 5%, alert the manager; otherwise, continue with the marketing plan”—it’s time to formalize that into an AIDecisionPoint or Metric object. This encapsulation makes branching explicit and machine‐readable.

Example

Scenario: You frequently ask AI to "analyze financial data from a spreadsheet, identify key metrics (revenue, profit, expenses), and provide recommendations for cost savings."
Recurring Pattern: That entire instruction block appears in multiple contexts—quarterly reviews, budget planning, investor updates.
Custom Object Concept: Create a FinancialAnalyzer object that takes parameters like data_source, metrics, and recommendation_count.

Everyday Analogy
Consider a baker who bakes different types of bread. If they manually mix ingredients each time, they risk inconsistencies in kneading time, baking temperature, or ingredient proportions. By creating a “BreadRecipe” template (object), they ensure each loaf follows the same standardized process—no guesswork, no mistakes.

2. Draft a Declarative Data Model

Detailed Explanation

Once you’ve identified a pattern or complex task, define a clear, declarative schema for the new object. This is essentially your data model: a list of attributes (fields) and, where relevant, behaviors or methods. Key considerations:

List All Necessary Attributes
- Name: A unique identifier for the object (e.g., FinancialAnalyzer).
- Parameters/Fields: Logical fields you’ll need at runtime. For FinancialAnalyzer, these might include:
  - data_source: Where the financial data lives (spreadsheet name, SQL query, etc.)
  - metrics: A list of strings (["revenue", "profit", "expenses"])
  - recommendation_count: How many cost-saving recommendations to generate
- Optional Fields: Defaults or toggles (e.g., include_graphs=True, currency="USD").
Define Behaviors (if applicable)
Although CRAFT objects are mostly declarative, you may specify that certain objects trigger built-in AI behaviors. For example, FinancialAnalyzer might implicitly instruct AI to:
- Parse the dataset
- Compute each metric trend
- Identify top three cost‐saving opportunities
  Document that in your data model so AI “knows” what to do when it sees a FinancialAnalyzer object.
Select Meaningful Field Names
Use intuitive, self‐documenting names. data_source is clearer than ds, metrics_to_check is better than m. This helps future users (and future AI sessions) understand the object at a glance.
Consider Data Types & Validation
If a field expects a list of strings, make sure your spec notes that. If another field must be an integer greater than 0, document that constraint. Doing so enables CRAFT’s validation system to catch missing or malformed inputs before AI processes them.

Example

python

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# Declarative Data Model for a new custom object class FinancialAnalyzer: """ Attributes: - name (string): Unique label for this analysis instance. - data_source (string): Identifier for the financial dataset (e.g., 'Q1_Sales.csv'). - metrics (list[string]): Metrics to compute (e.g., ['revenue', 'profit', 'expenses']). - recommendation_count (integer): Number of cost-saving recommendations to return. - include_graphs (boolean, default=True): Whether to include trend graphs in the output. - currency (string, default='USD'): Currency for all financial values. Behavior: - Compute each metric’s trend (month-over-month % change). - Identify the top N cost-saving opportunities. - Format output as a structured JSON with sections: 'metric_trends', 'recommendations'. """

Everyday Analogy
Creating a data model is like drafting a blueprint before building a house: you outline each room’s dimensions, the materials needed, and how rooms connect. Once the blueprint is clear, construction is straightforward, and any builder knows exactly what to do.

3. Embed Examples in the CRAFT Spec

Detailed Explanation

Documentation alone isn’t enough; you need concrete examples to demonstrate how to instantiate and use the new object. In your CRAFT spec file (e.g., CFT-FWK-SPEC-v0625d8-Objects.txt), add a “Usage Examples” section under your object’s definition. This typically includes:

One or More Code Snippets
Show real‐world instantiation with representative values. For instance:
```
python
```
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FinancialAnalyzer( name="Q1_Earnings_Review", data_source="Q1_Sales.csv", metrics=["revenue", "profit", "expenses"], recommendation_count=3, include_graphs=True, currency="USD" )
Comments Explaining Each Field
Inline comments or trailing comments clarify why certain values are chosen:
```
python
```
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FinancialAnalyzer( name="Q1_Earnings_Review", # Label for easy reference data_source="Q1_Sales.csv", # CSV file with columns: month, revenue, cost, expense metrics=["revenue", "profit", "expenses"], # Compute these metrics recommendation_count=3, # Return top 3 cost-saving ideas include_graphs=True, # Include charts for visual clarity currency="USD" # Use US dollars for all monetary calculations )
Example Outputs (Optional but Helpful)
If possible, show a sample AI response to that instantiation. Even a short excerpt like:
```
json
```
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{ "metric_trends": { "revenue": { "Jan": 12000, "Feb": 13000, "Mar": 12500, ... }, ... }, "recommendations": [ "Negotiate lower rates with supplier X to save 5%", "Consolidate spending on marketing channels A and B", "Reduce overhead by optimizing inventory levels" ] }
gives future users a concrete idea of what to expect.

Why This Matters

Reduces Ambiguity: AI and team members see exactly how to call the object, preventing guesswork.
Accelerates Adoption: Developers essentially have copy-paste-ready code. They don’t need to infer field names or decide on formatting defaults.
Facilitates Validation: The examples serve as test cases for your validation logic (CRAFT can check if actual instantiations match these examples).

Everyday Analogy
When you buy furniture that requires assembly, the instruction manual usually includes a fully assembled photograph plus step‐by-step diagrams. Without those images, you’d fuss over how pieces fit. Example code snippets in the spec serve the same purpose: they show the “assembled” object so you know what the end state looks like.

4. Document Expected AI Behavior

Detailed Explanation

Beyond showing how to instantiate an object, you must document how AI should interpret it—i.e., the “semantics” or behavior tied to that data model. This goes under a “Behavior” or “Description” section in your spec. Include:

Intent Description
A concise, plain‐English summary of what the object represents (e.g., “FinancialAnalyzer: Encapsulates a financial dataset analysis, computing trends and generating cost-saving recommendations.”).
Field Semantics
For each field, explain:
- Data Format: “data_source should be a string pointing to a CSV or database table name.”
- Units or Accepted Values: “currency must be an ISO currency code (e.g., ‘USD’, ‘EUR’).”
- Behavioral Defaults: “If include_graphs=True, AI generates a simple line chart for each metric; if False, omits charts entirely.”
Implicit Methods
If the object triggers a multi‐step AI process, briefly describe each step. For example:
- Step 1: Parse the data_source to load raw data.
- Step 2: Compute monthly revenue, profit, and expense trends.
- Step 3: Identify top N anomalies or cost spikes.
- Step 4: Formulate recommendations based on patterns (e.g., “Supplier costs rose 15% in February—suggest renegotiating contract”).
- Step 5: Format final output as a JSON object with two keys: metric_trends and recommendations.
Error Handling
Document what happens if inputs are missing, malformed, or out of range. For instance:
- “If metrics is empty or contains unknown metric names, AI responds with an error object: { 'error': 'Unknown metric: X' }.”
- “If data parsing fails, AI returns: { 'error': 'Unable to load data from Q1_Sales.csv' }.”

Citations

Everyday Analogy
Think of this like API documentation:

The “endpoint” is your object name.
Each “parameter” has a description, accepted values, and default behavior.
You also show example requests and responses. Without such documentation, developers misuse APIs and encounter unexpected behaviors.

5. Test and Iterate

Detailed Explanation

Defining an object in spec is only half the battle—ensuring that AI consistently recognizes and executes it as intended requires testing. Follow these sub‐steps:

Create Sample Chat Scenarios
- Basic Instantiation: Start with a simple call, e.g.:
```
python
```
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  FinancialAnalyzer( name="TestAnalysis", data_source="SampleData.csv", metrics=["revenue", "profit"], recommendation_count=2 )
  Confirm that AI processes it, computes trends, and returns two recommendations in the expected JSON format.
- Edge Cases: Test missing fields or invalid values:
```
python
```
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  FinancialAnalyzer( name="EdgeCase1", data_source="", # missing data_source metrics=[], recommendation_count=0 )
  AI should respond with structured errors (e.g., { 'error': 'data_source cannot be empty' }).
Refine Prompt Handlers
- If you notice AI ignoring certain fields (e.g., it doesn’t include graphs when include_graphs=True), adjust the underlying prompt templates or behavior description in the spec.
- If AI misinterprets a field’s purpose (e.g., thinking recommendation_count refers to chapters instead of bullet points), update the object’s description and examples.
Gather Feedback from Stakeholders
- Have colleagues or team members use the object in their own CRAFT sessions. Note any confusion or misalignment.
- For instance, a marketer might expect “recommendations” to be marketing‐focused, while a finance lead expects cost‐cutting suggestions. You may need to add a field like recommendation_focus (e.g., “marketing” or “cost”).
Versioning and Updates
- Once the object behaves as intended, tag it with a VersionTag (e.g., v1.0). If you later tweak its behavior (perhaps adding a new field or changing default logic), increment to v1.1 or v2.0.
- Document the changes in a changelog section of your spec so that anyone referencing the object knows exactly how its behavior evolved.
Automate Regression Testing (Optional)
- For advanced setups, write a small testing harness that imports your CRAFT spec and runs a suite of example instantiations to verify outputs. If AI model updates cause different behavior, you catch it early.

Everyday Analogy
This process resembles software unit testing:

You write test cases for both “happy path” and “edge cases.”
You iterate on your code (object definition) until tests pass reliably.
You tag releases (versioning) so downstream users know which version they’re on.

Summary Table of Practical Steps

Below is a summary table that captures each practical step, a brief description, a concrete example, and the primary benefit. Use this as a quick reference when defining new CRAFT objects.

Overall Takeaways

Spot Repetition: Look for any chunk of free-form text or multi-step instruction you send repeatedly—those are prime candidates for objects.
Design with Clarity: A well-structured data model (fields and behaviors) prevents misunderstandings and makes maintenance a breeze.
Show, Don’t Tell: Always include usage examples in your CRAFT spec so human and AI readers understand exactly how to instantiate and interpret the object.
Spell Out AI Behavior: Document both “what” the object means and “how” AI should act when it sees the object—this bridges the gap between your intent and AI’s execution.
Validate & Iterate: Test in real chat scenarios, gather feedback, and version your objects. Iteration ensures reliable, predictable AI behavior over time.

By following these steps, you transform free‐wheeling prompt fragments into robust, maintainable, and reusable building blocks—extending CRAFT’s power and enabling consistent, efficient AI collaboration.

Creative Object Adaptations for CRAFT™️: An Innovation Outline

The Big Picture: Why Objects Matter in AI Conversations

Objects transform chaotic AI interactions into elegant, reusable systems—turning your prompts from one-time commands into scalable business assets.

I. Foundation: Understanding CRAFT Objects Today

Current State: Objects bundle related variables and functions into cohesive units
Core Benefits: Organization, scalability, and modular complexity management
The Gap: While CRAFT has 60+ data types, object implementation remains underexplored

II. Game-Changing Object Categories for Entrepreneurs

A. Business Intelligence Objects

MarketAnalysis Object: Combines competitor data, trend analysis, and opportunity scoring
CustomerJourney Object: Maps touchpoints, pain points, and conversion triggers
RevenueModel Object: Bundles pricing strategies, cost structures, and profitability projections

B. Content Creation Powerhouses

ContentCampaign Object: Integrates tone, audience, channels, and performance metrics
BrandVoice Object: Encapsulates personality, vocabulary, storytelling patterns
ViralFormula Object: Combines hook patterns, emotional triggers, and distribution strategies

C. Workflow Automation Objects

DecisionTree Object: Structures complex business logic with branching scenarios
ProcessOptimizer Object: Identifies bottlenecks and suggests improvements
AITeam Object: Orchestrates multiple AI personas working on different aspects of a project

III. Revolutionary Implementation Concepts

A. Living Objects (Self-Evolving)

Objects that learn from each interaction and update their parameters
Example: CustomerPersona Object that refines itself based on actual customer interactions

B. Composite Objects (Russian Doll Architecture)

Objects containing other objects for sophisticated hierarchies
Example: BusinessPlan Object containing multiple Department Objects, each with Team Objects

C. Conditional Objects (Smart Adaptation)

Objects that change behavior based on context or results
Example: CrisisResponse Object that escalates communication tone based on severity metrics

IV. Practical Magic: Real-World Applications

A. The Startup Launcher Object

Combines market validation, MVP design, funding strategy, and launch timeline
One object call replaces dozens of separate prompts

B. The Content Factory Object

Generates blog posts, social media, email campaigns from a single brief
Maintains consistency across all channels automatically

C. The AI Consultant Object

Provides expert analysis by combining industry knowledge, best practices, and personalized recommendations
Acts as a 24/7 strategic advisor

V. The Token Economy Revolution

Traditional Approach: 500+ tokens per complex request
Object Approach: 50 tokens to invoke a pre-built object
ROI: 90% token reduction = massive cost savings at scale

VI. Future Vision: Where Objects Take CRAFT

A. Object Marketplaces

Entrepreneurs sharing/selling custom objects
Industry-specific object libraries

B. Object Inheritance

Child objects inheriting and extending parent capabilities
Version control for object evolution

C. Cross-Platform Objects

Objects that work across ChatGPT, Claude, Gemini
Universal prompt compatibility

VII. The Entrepreneurial Edge

Speed: Launch products 10x faster with pre-built objects
Consistency: Maintain quality across all AI interactions
Scale: Handle complexity without cognitive overload
Innovation: Focus on strategy while objects handle execution

VIII. Call to Action: Your Object Journey

Start with one simple object for your most repetitive task
Build a personal object library over 30 days
Share successes with the CRAFT community
Transform from AI user to AI architect

The Bottom Line: Objects aren't just a feature—they're your competitive advantage in the AI-powered economy. While others fumble with prompts, you'll be orchestrating symphonies of automated intelligence.

The current CRAFT framework brilliantly uses object-like "Data Types" (e.g., SocialPost, Workflow) to structure information. The next evolution is to introduce true "Project Objects"—dynamic entities that combine data, functions, and even their own interactive logic. This moves beyond simple data containers to create active partners in our workflow, fully embracing the CRAFT philosophies of "Code as Conversation" and "Human-AI Partnership".

1. The Stateful Object: The "Project Brain" for Session Continuity

Concept: Create objects that manage their own state and can be passed between chat sessions using the HANDOFF_SNAPSHOT system. This provides a massive leap for the "Cumulative Intelligence" principle.
Example Idea: The ProjectTracker Object.
- An object that encapsulates a project's key metrics, tasks, and status.
- Instead of just describing progress in a handoff, we pass the object itself. The next AI assistant can immediately interact with it (e.g., MyProject.get_status() or MyProject.calculate_burn_rate()).

2. The Interactive Object: The "AI Guide" That Prompts Back

Concept: Design objects that, upon creation or modification, can proactively prompt the user for the next logical step. This turns a passive object into an active guide, streamlining complex workflows.
Example Idea: The AB_Test Object.
- A user defines an A/B test: HomepageTest = AB_Test(name="Homepage CTA Button", goal="Increase Signups").
- The AI, guided by the object's internal logic, would respond: #AI->H::Note: (AB_Test object created. To proceed, please provide the 'Control' and 'Variant' using the .add_variant() method.) This embodies the "Human-AI Partnership" by having the AI guide the user through a structured process.

3. The Generator Object: The "Content Factory"

Concept: Develop objects that act as specialized factories for content generation. These objects would hold core strategic information (like audience and tone) and use it to execute a variety of creative tasks consistently.
Example Idea: The ContentCampaign Object.
- Initialize an object with high-level strategy: NewCampaign = ContentCampaign(audience=TargetAudience("marketing professionals"), tone=AITone("friendly", "professional"), topic="The Future of AI in Marketing").
- Execute methods that use this built-in context: NewCampaign.generate_blog_titles(count=10), NewCampaign.draft_linkedin_posts(count=5), NewCampaign.create_email_subject_lines().

4. The Persona Object 2.0: The "Virtual Team Member"

Concept: Evolve the existing AIPersona data type into a full-fledged TeamMember object. This allows for the creation of a virtual team of AI specialists that can be assigned specific roles and tasks within a single chat.
Example Idea: The TeamMember Object.
- Instantiate a virtual team:
  - CriticalEditor = TeamMember(persona="Expert Editor", skills=["Conciseness", "Clarity"])
  - CreativeDirector = TeamMember(persona="Visionary Brainstormer", skills=["Innovative Ideas", "Visual Concepts"])
- Delegate tasks directly: CriticalEditor.review(my_blog_draft) or CreativeDirector.suggest_visuals_for(my_blog_draft). This makes collaboration more structured and powerful.

1. The Stateful Object: The "Project Brain" for Session Continuity

The single biggest challenge in complex, multi-session AI projects is context decay. Each new chat is a fresh start, and continuity relies on summarizing the past. The Stateful Object solves this by creating a "Project Brain"—a living object that encapsulates the project's entire state and travels from one session to the next.

This concept directly supports two core CRAFT philosophies:

Cumulative Intelligence: The project's knowledge base isn't just described; it's contained within an object that actively grows and evolves.
Human-AI Partnership: The object becomes a shared, single source of truth that both the user and the AI can query, update, and rely on.

Proposed Implementation: The ProjectTracker Object

To make this concrete, we can define a new PROJECT_OBJECTS class in the CRAFTALPHA project file.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class ProjectTracker:
    def __init__(self, name, budget=0, status="Planning"):
        self.name = name
        self.status = status
        self.budget = budget
        self.hours_logged = 0.0
        self.milestones = {} # Using a dictionary for milestones
        self.key_decisions = []

    def log_hours(self, hours, description):
        """Adds billable hours and a description of the work."""
        self.hours_logged += hours
        # H->AI::Context: (Log this activity for reporting)
        print(f"Logged {hours} hours for: {description}")

    def add_milestone(self, name, due_date):
        """Adds a new project milestone with a due date."""
        if name not in self.milestones:
            self.milestones[name] = {"status": "Not Started", "due_date": due_date}
            print(f"Milestone '{name}' added.")
        else:
            print(f"Milestone '{name}' already exists.")

    def complete_milestone(self, name):
        """Marks a milestone as complete."""
        if name in self.milestones:
            self.milestones[name]["status"] = "Completed"
            print(f"Milestone '{name}' marked as complete.")
        else:
            print(f"Milestone '{name}' not found.")

    def generate_report(self):
        """Generates a summary report of the current project state."""
        # H->AI::Directive: (Format this output clearly for a business summary)
        report = f"""
        --- Project Status Report: {self.name} ---
        Status: {self.status}
        Budget: ${self.budget:,.2f}
        Total Hours Logged: {self.hours_logged}

        Key Decisions:
        - {"- ".join(self.key_decisions) or "None"}

        Milestones:
        """
        for name, data in self.milestones.items():
            report += f"  - {name} (Due: {data['due_date']}) - Status: {data['status']}\n"

        print(report)

### END PYTHON CODE BLOCK ###

This is how the ProjectTracker object makes the HANDOFF_SNAPSHOT system radically more effective.

Session 1: Project Kick-off

User: Let's start a new project. MyKetelsenAI_Project = ProjectTracker(name="Q3 Blog Content Strategy", budget=5000)
AI: #AI->H::Note: (ProjectTracker object 'MyKetelsenAI_Project' created with status 'Planning'.)
User: MyKetelsenAI_Project.add_milestone(name="Outline 5 Main Articles", due_date="2025-06-10")
AI: Milestone 'Outline 5 Main Articles' added.

(...work continues until the token limit is near...)

AI: We're approaching this chat's token limit. Let's create a transition HANDOFF_SNAPSHOT.
HANDOFF_SNAPSHOT Contents:
META.SESSION_ID: CFT-PROJ-0004 ... CURRENT_STATE: The active artifact is the MyKetelsenAI_Project object. Its current state is: {name: "Q3 Blog Content Strategy", status: "Planning", budget: 5000, hours_logged: 0, milestones: {"Outline 5 Main Articles": {"status": "Not Started", "due_date": "2025-06-10"}}, key_decisions: []} ...

Session 2: Seamless Continuation

The next AI assistant reviews the handoff, reinstantiates the MyKetelsenAI_Project object with its data, and is immediately ready.

User: Welcome back. Can you give me a status update on our project? MyKetelsenAI_Project.generate_report()
AI: Of course. Here is the current report:
--- Project Status Report: Q3 Blog Content Strategy ---
Status: Planning
Budget: $5,000.00
Total Hours Logged: 0.0

Key Decisions:
- None

Milestones:
  - Outline 5 Main Articles (Due: 2025-06-10) - Status: Not Started

This workflow completely eliminates the need for manual summaries or the risk of forgetting key details between sessions. The object is the project's memory, creating a truly seamless and efficient process.

2. The Interactive Object: The "AI Guide" That Prompts Back

This concept moves an object from being a passive data container to an active participant in the workflow. An Interactive Object contains internal logic that allows it to guide the user, prompting them for the next logical action. This fundamentally enhances the "Human-AI Partnership" by creating a conversational loop where the AI, guided by the object, helps you build and execute complex tasks step-by-step.

It directly addresses the user pain point of "Difficulty Crafting Effective Prompts" by reducing the need to remember a complex sequence of commands. The object itself shows you the way.

Proposed Implementation: The AB_Test Object

A/B testing is a perfect use case for entrepreneurs. It has a clear, multi-step process that an Interactive Object can streamline. Here is how it could be defined in the PROJECT_OBJECTS section.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class AB_Test:
    def __init__(self, name, hypothesis, goal):
        self.name = name
        self.hypothesis = hypothesis
        self.goal = goal
        self.variants = {}
        self.status = "Defining"
        # H->AI::Directive: (The following is an instruction to the AI, triggered by the object's creation.)
        # This is the "prompt back" feature.
        print(f"""#AI->H::Note: (AB_Test object '{self.name}' created.
# To proceed, please define your 'control' and at least one 'test' variant using the .add_variant() method.)""")

    def add_variant(self, name, description):
        """Adds a variant (e.g., 'control' or 'test1') to the experiment."""
        self.variants[name] = {"description": description}
        print(f"Variant '{name}' added: {description}")
        # H->AI::Consider: (Check if the test is ready to start and guide the user.)
        if "control" in self.variants and len(self.variants) > 1:
            print(f"""#AI->H::RecommendedChange: (All necessary variants are now defined.
# You can begin the experiment by calling the .start_test() method.)""")
        else:
            print("#AI->H::Note: (Please add a 'control' and at least one 'test' variant to proceed.)")

    def start_test(self):
        """Begins the A/B test, changing its status to Active."""
        if "control" in self.variants and len(self.variants) > 1:
            self.status = "Active"
            print(f"A/B Test '{self.name}' is now Active.")
        else:
            print("#AI->H::Caution: (Cannot start test. Please ensure a 'control' and at least one 'test' variant are defined.)")
            
### END PYTHON CODE BLOCK ###

The Guided Workflow in Action

Notice how the conversation feels less like giving commands and more like a guided consultation.

Session: Designing an Experiment

User: Let's set up a test for our website's call-to-action button. HomepageCTA = AB_Test(name="Homepage CTA Efficacy", hypothesis="A more direct call-to-action will increase sign-ups.", goal="Increase user sign-ups by 5%.")
AI: (Triggered by the object's creation) #AI->H::Note: (AB_Test object 'Homepage CTA Efficacy' created. # To proceed, please define your 'control' and at least one 'test' variant using the .add_variant() method.)
User: Great. Let's add the control. HomepageCTA.add_variant(name="control", description="The current 'Learn More' button.")
AI: (Triggered by the object's internal logic) Variant 'control' added: The current 'Learn More' button. #AI->H::Note: (Please add a 'control' and at least one 'test' variant to proceed.)
User: OK, here's the test variant. HomepageCTA.add_variant(name="test1", description="A new 'Start Your Free Trial' button.")
AI: (The object now sees all conditions are met and prompts for the next logical step) Variant 'test1' added: A new 'Start Your Free Trial' button. #AI->H::RecommendedChange: (All necessary variants are now defined. # You can begin the experiment by calling the .start_test() method.)

This interaction perfectly embodies the "Structure Enables Freedom" principle. The object provides a rigid, best-practice structure for setting up the test, freeing the user to focus their creative energy on the substance of the test—the hypothesis and the variants—rather than on the procedural syntax of the framework.

3. The Generator Object: The "Content Factory"

The Generator Object is a strategic engine designed to solve one of the biggest challenges in content creation: maintaining a consistent voice, tone, and message across multiple formats. This object acts as a central "factory" where you define the high-level strategy (the what and why) once. Then, you can command it to produce a variety of specific content pieces (the how), all of which automatically align with the core strategy.

This model is a powerful implementation of several CRAFT principles:

Structure Enables Freedom: By defining the strategic structure upfront, you are freed from repetitive setup and can focus on the creative output.
Automation: It automates complex instructions into a single, reusable call.
Conservation of Tokens: It dramatically shortens instructions, replacing long, descriptive prompts with concise method calls.

Proposed Implementation: The ContentCampaign Object

A content campaign is the perfect use case. It requires a unified strategy across blog posts, social media, and emails. This object would be defined in the PROJECT_OBJECTS section and would intelligently use other CRAFT Data Types.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class ContentCampaign:
    def __init__(self, name, topic, core_message, target_audience, tone, keywords=[]):
        self.name = name
        self.topic = topic
        self.core_message = core_message
        self.target_audience = target_audience # Expects a TargetAudience object [cite: 87]
        self.tone = tone # Expects an AITone object [cite: 86]
        self.keywords = keywords

        print(f"#AI->H::Note: (ContentCampaign '{self.name}' is configured. You can now generate content.)")

    def generate_blog_titles(self, count=5):
        """Generates blog titles based on the campaign's topic and keywords."""
        # H->AI::Directive: (Generate {count} blog titles for {self.target_audience.description}
        # about {self.topic}, using a {self.tone.description} tone. Include these keywords: {self.keywords})
        print(f"Generating {count} blog titles...")
        # AI would generate titles here

    def draft_social_post(self, platform):
        """Drafts a social media post, adapting it to the specified platform."""
        # H->AI::Focus: (Use the SocialPost data type to create a post for {platform}[cite: 83].
        # The content should convey the '{self.core_message}' to a {self.target_audience.description} audience.
        # Use a {self.tone.description} tone and incorporate these hashtags: {self.keywords})
        print(f"Drafting a {platform} post about '{self.topic}'...")
        # AI would generate a social post here

    def create_email_subject_lines(self, count=3):
        """Generates compelling email subject lines for the target audience."""
        # H->AI::Directive: (Create {count} email subject lines for a campaign about '{self.topic}'.
        # They must capture the attention of a {self.target_audience.description} and reflect a {self.tone.description} tone.)
        print(f"Generating {count} email subject lines...")
        # AI would generate subject lines here
### END PYTHON CODE BLOCK ###

The "Content Factory" Workflow in Action

This workflow transforms a series of disconnected tasks into a streamlined production line.

Step 1: Configure the Factory (One-Time Setup)

User: Let's build a campaign to promote our new AI-powered analytics feature.

Q3_Analytics_Push = ContentCampaign(
    name="Q3 AI Analytics Launch",
    topic="Using AI to uncover business insights",
    core_message="Our new tool saves you time by finding hidden trends in your data automatically.",
    target_audience=TargetAudience("Tech-savvy entrepreneurs", knowledge_level="intermediate")[cite: 87],
    tone=AITone("informative", "authoritative", "concise")[cite: 86],
    keywords=["AI analytics", "business intelligence", "data insights", "SaaS"]
)

AI: #AI->H::Note: (ContentCampaign 'Q3 AI Analytics Launch' is configured. You can now generate content.)

Step 2: Run the Production Line (Rapid Content Generation)

User: Give me some blog titles. Q3_Analytics_Push.generate_blog_titles(count=3)
AI: Generating 3 blog titles... 1. "Stop Digging: How AI Analytics Surfaces Key Business Insights Automatically" 2. "Beyond Dashboards: 3 Ways AI is Revolutionizing Business Intelligence" 3. "Is Your Data Lying? Using AI to Uncover the Truth"
User: Now draft a post for LinkedIn. Q3_Analytics_Push.draft_social_post(platform="LinkedIn")
AI: Drafting a LinkedIn post about 'Using AI to uncover business insights'... (AI generates a professional post, referencing the core message and using the specified keywords as hashtags)
User: Perfect. I need subject lines for the announcement email. Q3_Analytics_Push.create_email_subject_lines(count=2)
AI: Generating 2 email subject lines... 1. "Your data has a story. Let AI tell it." 2. "The new, faster way to find business insights is here."

With the Generator Object, creating a dozen coordinated content pieces is no longer a dozen separate, detailed prompts. It's one strategic setup followed by a dozen simple, efficient commands.

4. The Persona Object 2.0: The "Virtual Team Member"

This concept elevates the existing AIPersona Data Type into a fully functional TeamMember object. Instead of applying a single, temporary persona to the AI, you can now instantiate a persistent team of virtual specialists. Each "team member" has a distinct role, skillset, and can be delegated tasks directly. This transforms the "Human-AI Partnership" from a guiding principle into a tangible, interactive workflow.

It allows a user to orchestrate a collaboration between multiple AI specialists within a single chat, getting nuanced, multi-faceted feedback on their work without having to constantly redefine roles and context.

Proposed Implementation: The TeamMember Object

This object would be defined in the PROJECT_OBJECTS section. It is designed to contain another CRAFT Data Type (AIPersona), demonstrating how objects can be composed for greater power.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class TeamMember:
    def __init__(self, name, persona):
        self.name = name
        self.persona = persona # Expects an AIPersona object
        self.task_log = []
        
        print(f"#AI->H::Note: (Virtual team member '{self.name}', the {self.persona.role}, has joined the project.)")

    def review(self, content):
        """Reviews content from the perspective of this team member's persona."""
        self.task_log.append(f"Reviewed content: {content[:50]}...")
        # H->AI::Directive: (Assume the role of '{self.name}'. Adopt the following persona: {self.persona}.
        # Now, critically review the following content and provide actionable feedback based on your specific focus and constraints.)
        # H->AI::Context: (The content to review is: {content})
        print(f"'{self.name}' is reviewing the content...")
        # AI would generate persona-specific feedback here

    def ideate(self, topic):
        """Brainstorms ideas on a topic from this team member's unique perspective."""
        self.task_log.append(f"Ideated on: {topic}")
        # H->AI::Directive: (Assume the role of '{self.name}' with the persona {self.persona}.
        # Brainstorm a list of creative ideas about the following topic: {topic}.
        # Your ideas should reflect your persona's unique focus.)
        print(f"'{self.name}' is brainstorming ideas about '{topic}'...")
        # AI would generate persona-specific ideas here
### END PYTHON CODE BLOCK ###

The "Virtual Team" Workflow in Action

This workflow demonstrates how to leverage a team of specialists to develop an idea from a rough draft to a polished piece.

Step 1: Assemble the Team

User: Let's build our content team for this project.
Editor = TeamMember(
    name="Casey",
    persona=AIPersona(role="Critical Editor", focus=["Clarity", "Conciseness"], constraints=["Avoid jargon", "Challenge weak arguments"])
)
Marketer = TeamMember(
    name="Riley",
    persona=AIPersona(role="Growth Marketer", focus=["Audience Engagement", "SEO Keywords"], constraints=["Must have a clear call-to-action"])
)
AI: #AI->H::Note: (Virtual team member 'Casey', the Critical Editor, has joined the project.) #AI->H::Note: (Virtual team member 'Riley', the Growth Marketer, has joined the project.)

Step 2: Multi-faceted Review of a Blog Draft

User: Riley, take a look at this draft first. I want to make sure it's engaging. Marketer.review("Our new product is a synergy of forward-thinking paradigms...")
AI (as Riley, the Marketer): "Casey' is reviewing the content... Feedback: The hook is a bit dry. Let's open with a question that speaks to the customer's pain point. For SEO, we need to include 'AI analytics' and 'SaaS' in the first paragraph. Also, the call-to-action is buried at the end—let's move it up.
User: Great feedback. I've updated the draft. Now for the hard part. Casey, your turn. Editor.review("Updated draft text...")
AI (as Casey, the Editor): 'Casey' is reviewing the content... Feedback: "Synergy of forward-thinking paradigms" is meaningless jargon. Let's cut it. Be direct. What does the product *do*? I've flagged three sentences that are over 25 words long; we need to shorten them for clarity. The argument in paragraph two is weak; it needs a specific data point to be convincing.

This layered feedback process—leveraging different AI specialists for different tasks—is a far more sophisticated and effective way to collaborate than using a single, generalist AI. It truly brings the concept of a Human-AI Partnership to life.

CRAFT™️The CRAFT™️ Experimentweek43

Richard Ketelsen