CRAFT™️ Experiment: ChatGPT 04-mini-high: Rethinking A.I. Functions (2 of 5)

The CRAFT™️ Experiment

May 19

Written By Richard Ketelsen

THE CRAFT™️ EXPERIMENT :: WEEK 5 :: POST 2

ChatGPT 04-mini-high: Rethinking A.I. Functions (2 of 5)

Please brainstorm some creative ways that functions can be adapted to the CRAFT framework. Please refer to the attached files for information about the current state of this project. For reference, here are some benefits of regular functions:
In programming, a function is a reusable block of code that performs a specific task. It's like a mini-program within a larger program, designed to be called (or invoked) when needed. Functions help organize code, making it easier to read, understand, and maintain. They also promote code reusability, as you can use the same function multiple times with different inputs
Key Concepts:
• Input: Functions can accept data (called arguments or parameters) as input, allowing them to perform different tasks based on the data they receive.
• Output: Functions can return a value (the output) as a result of their execution.
• Reusability: A function can be called multiple times from different parts of a program, saving you from rewriting the same code repeatedly.
• Organization: Functions help break down complex programs into smaller, manageable units.
Abstraction: Functions hide the implementation details of a task, allowing you to focus on what the task does rather than how it does it.
How can these known concepts be adapted to CRAFT which is built on six 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.
6. CONSERVATION OF TOKENS: Limiting tokens in an A.I. chat saves money and time.
   It also cuts down on the amount of vertical scrolling needed in a chat.
Please start with an outline of the highlights of this topic.

Mastering Function Adaptation in the CRAFT Framework

In the fast‑moving world of AI prompt engineering, the CRAFT™ framework brings structure to natural‑language conversations—unlocking both clarity and creative freedom. In this post, we’ll explore ten innovative ways to adapt the familiar programming concept of functions to CRAFT. You’ll learn how to modularize prompts, inject dynamic context, enforce structured outputs, chain multi‑step workflows, maintain state, enable live streaming, refine iteratively, organize with namespaces, wrap behavior with decorators, and stay well under your token budget.

Each pattern is grounded in CRAFT’s six guiding principles:

Structure Enables Freedom

Code as Conversation

Cumulative Intelligence

Human‑AI Partnership

Accessibility Through Familiarity

Conservation of Tokens

Ready to transform your AI chats into a modular, maintainable, and cost‑effective toolkit? Let’s dig in.

==========

1. CRAFT Function Definitions

A func block is your fundamental building block—a named, reusable prompt template. By defining:

func summarize(text, style="concise"):

"""Summarize `text` in two sentences."""

prompt: "Summarize the following in two sentences ({style}):\n\n{text}"

return summary• Benefits: modularity, maintainability, token savings (call by name, not by full prompt), easier updates across sessions

• Example Use: my_summary = summarize(article_text, "detailed")

• Principles: Structure Enables Freedom, Conservation of Tokens

==========

2. Parameterization as Context Injection

Turn hard‑coded details into named slots:

func translate(text, to_language):

prompt: "Translate the following into {to_language}:\n\n{text}"

return translated_text• Benefits: dynamic customization, self‑documenting calls, token efficiency (only inject small values), separation of concerns

• Example Use: translate(report, "Spanish")

• Principles: Accessibility Through Familiarity, Conservation of Tokens

==========

3. Return Templates for Structured Replies

Enforce predictable output shapes:

func generate_blog_summary(text):

prompt: "Provide a JSON with `title`, `bullet_points` (3 items), and `read_time` (minutes).\n\nArticle: {text}"

return {"title": string, "bullet_points": [string,string,string], "read_time": integer}• Benefits: consistency, easier post‑processing, token savings, built‑in validation

• Example Use: meta = generate_blog_summary(long_article) → meta.title, meta.bullet_points

• Principles: Code as Conversation, Cumulative Intelligence

==========

4. Composable (Higher‑Order) Prompt Chains

Build pipelines by composing functions:

func compose(f1, f2):

prompt: |

Run `{f1}` then feed its output into `{f2}`.

return composed• Benefits: extreme reusability, separation of concerns, token‑efficient orchestration, scalable workflows

• Example Use:

summ_trans = compose(summarize, translate)

result = summ_trans(article, "French")• Principles: Structure Enables Freedom, Human‑AI Partnership

==========

5. Stateful Functions for Cumulative Intelligence

Carry context forward via a persistent state object:

# state = {"history":[], "vars":{}}

func set_variable(state, name, value):

state.vars[name] = value

return {"state": state}• Benefits: builds on prior turns, reduces repetition, enables dynamic workflows and error recovery

• Example Use:

sv = set_variable(state, "language", "German")• Principles: Cumulative Intelligence, Conservation of Tokens

==========

6. Async & Streaming Functions

Leverage streaming or long‑running jobs:

func progressive_summarize(text, steps=3) async stream:

prompt: |

Summarize `{text}` in {steps} progressive passes, yielding each pass.

return summary_stream• Benefits: real‑time feedback, interactive clarifications, early error detection, supports large tasks

• Example Use: call progressive_summarize(report,5) and reply “next” to continue

• Principles: Human‑AI Partnership, Conservation of Tokens

==========

7. Recursion & Iterative Refinement

Polish outputs over multiple passes:

func refine(text, passes=3, target_score=8.0):

prompt: "Rate readability (0‑10) and return improved text.\n\n{text}"

if score < target_score and passes>1:

return refine(improved_text, passes-1, target_score)

else:

return improved_text• Benefits: layered reasoning, quality control, token‑efficient looping

• Example Use: final = refine(draft, 2, 7.5)

• Principles: Cumulative Intelligence, Conservation of Tokens

==========

8. Namespaces & Modules for Organization

Group functions logically:

module nlp:

import summarize, translate

module data:

import tabulate, extract_entities• Benefits: avoids name collisions, improves discoverability, scales your function library, implicit imports save tokens

• Example Use: nlp.summarize(report)

• Principles: Structure Enables Freedom, Code as Conversation

==========

9. Decorators & Hooks

Wrap cross‑cutting concerns around functions:

decorator enforce_budget(max):

hook on_enter:

if estimate_tokens(...)>max: raise Error

return func

@enforce_budget(150)

func outline(topic): …• Benefits: clean validation, logging, retries, caching—without cluttering core prompts

• Example Use: outline = outline("AI trends") triggers budget check before call

• Principles: Structure Enables Freedom, Conservation of Tokens

==========

10. Token‑Budgeted Signatures

Trim signatures to essentials:

# Before: func translate(text, to_language, tone)

# After: func translate(txt, lang="en")• Benefits: predictable cost, faster calls, cleaner UX, fewer truncation errors

• Example Use: translate(txt, "fr") vs. verbose param lists

• Principles: Conservation of Tokens, Human‑AI Partnership

==========

Conclusion & Next Steps

By adopting these ten patterns, you’ll turn your prompt library into a modular, maintainable, and cost‑effective toolkit—ready for limitless AI experimentation. Here’s how to begin:

Draft your first func or refactor an existing prompt.

Parameterize and template its return values.

Organize it into a module.

Add any needed decorators or state management.

Test in a real ChatGPT session, inspect tokens, and iterate.

Now you have a clear roadmap: build your CRAFT library one function at a time, and watch your AI collaborations become faster, smoother, and more powerful. Happy crafting!

RESEARCH:

Please brainstorm some creative ways that functions can be adapted to the CRAFT framework. Please refer to the attached files for information about the current state of this project. For reference, here are some benefits of regular functions: In programming, a function is a reusable block of code that performs a specific task. It's like a mini-program within a larger program, designed to be called (or invoked) when needed. Functions help organize code, making it easier to read, understand, and maintain. They also promote code reusability, as you can use the same function multiple times with different inputs Key Concepts: • Input: Functions can accept data (called arguments or parameters) as input, allowing them to perform different tasks based on the data they receive. • Output: Functions can return a value (the output) as a result of their execution. • Reusability: A function can be called multiple times from different parts of a program, saving you from rewriting the same code repeatedly. • Organization: Functions help break down complex programs into smaller, manageable units. Abstraction: Functions hide the implementation details of a task, allowing you to focus on what the task does rather than how it does it. How can these known concepts be adapted to CRAFT which is built on six 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. 6. CONSERVATION OF TOKENS: Limiting tokens in an A.I. chat saves money and time. It also cuts down on the amount of vertical scrolling needed in a chat. Please start with an outline of the highlights of this topic.

ChatGPT said:

Thought for 9 seconds

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
CopyEdit
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
CopyEdit
func summarize(text, style="concise"):
Docstring (Optional)
- Triple-quoted natural-language description
craft
CopyEdit
"""Summarize `text` in two sentences using the given style."""
Prompt Body
- The meat of your instruction, with placeholders matching parameter names
craft
CopyEdit
prompt: "Please summarize the following text in two sentences ({style}):\n\n{text}"
Return Clause
- Declares the output variable(s) you expect back
craft
CopyEdit
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

CopyEdit

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
CopyEdit
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
CopyEdit
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
CopyEdit
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
CopyEdit
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
CopyEdit
func summarize(text, style="concise"): …
Calling summarize(long_doc) uses the concise style automatically.

Examples

1. Translation Function

craft

CopyEdit

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

CopyEdit

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

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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.

craft

CopyEdit

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:

craft

CopyEdit

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
craft
CopyEdit
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:
craft
CopyEdit
summarizer = summarize translator = translate formatter = format_markdown pipeline = compose(compose(summarizer, translator), formatter) result = pipeline(article, "Italian")

Examples

A. Summarize → Translate → Style-Guide Check

craft

CopyEdit

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

craft

CopyEdit

# 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

craft

CopyEdit

# 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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

# 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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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:

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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:

craft

CopyEdit

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:
craft
CopyEdit
# ==================================================== # 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:
craft
CopyEdit
summary = nlp.summarize(article_text, style="detailed") entities = data.extract_entities(article_text)
Nested Modules for Granularity
You can even nest modules, e.g.:
craft
CopyEdit
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

craft

CopyEdit

# 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

craft

CopyEdit

# 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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

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

craft

CopyEdit

# 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

craft

CopyEdit

# 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

craft

CopyEdit

# 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!

CRAFT™️The CRAFT™️ Experimentweek5