CRAFT™️ Experiment: Claude 3.7: Rethinking A.I. Variables (4 of 5)
THE CRAFT™️ EXPERIMENT :: WEEK 4 :: POST 4
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Claude 3.7: Rethinking A.I. Variables
-
Please provide a detailed explanation of the benefits of the new data types used in CRAFT. Last week, we worked on new data types and the results are contained in the attached files. Please generate a recap of these results for my blog that explains the new data types in CRAFT in detail.
def create_photo(data):
#H->AI::CreateImage: (Create a photographic image using the data provided)
#H->AI::OnError: (If image creation fails, respond with "Sorry, I cannot make images.")
# Additional implementation details would follow here
URL, ColorPalette, DomainName, WhiteListedDomainName, BlackListedDomainName, Email, FileName, FileHash, MLACitation, MLAInTextCitation, Prompt, PromptLibrary, ConversationHistory, AIRecipe, AIRecipeLibrary, AIActionTemplate, AIWorkflow, AIModelConfig, UserIntent, AISentiment, RichContent, MediaReference, TLDRSummary, UserProfile, Metric, TimeSeries, DataValidator, Transformation, AIInstruction, Comparison, SocialPost, HowTo, Definition, AITone, TargetAudience, WordLength, UserIntent, AISentiment, AIPersona, CommunicationChannel, ChartSpec, CodeSnippet, APIRequest, APIResponse, BusinessLogic, ComplianceRule, ConditionLogic, Checklist, Form, MultipleChoice, Workflow, AIDecisionPoint, KnowledgeGraphNode, DefinedRelationship, ConstraintSet, OutputSchema, ComparisonMatrix, StructuredArgument, RevisionRequest, VersionTag
PROMPT:
Please read the attached project files.
# 01 - INITIAL PROMPT
PROMPT: "Hi, we will work together to brainstorm some content for my website. To begin, I will provide a 10-part prompt that will describe the following:
1. Personal Introduction: Who I am.
2. Content/Website Purpose: The website for which the content will be created and its intended purpose. This provides context for the chat.
3. About the Audience and Website : Target Audience, Pain Points & Unique Value Proposition, Competitive Edge and Elevator Pitch.
4. Content Goals: The objectives to achieve with the content.
5. Role of the A.I.: The expected role that you , the A.I. chat service, should assume in this process.
6. Content Source: The source from which the content will be derived.
7. Inspirations: Inspirations to guide you , the A.I. chat service, in understanding the type of content to create.
8. Constraints: Any specific constraints or aspects to avoid in the content creation process.
9. Ideas for visuals: Any ideas that I have for visuals that may be included in this content. Please do not create these visuals unless specifically asked to do so.
10. Related Stories/Content: If this content is part of a series, this will list and briefly explain the series and list any helpful URLS.
1) Personal Background and Site Overview - Based in Minneapolis, MN, USA, I am a professional in Computer Science and Graphic Design. I work for a Fortune 100 Company as a Senior Cybersecurity Incident Responder where I work with a team to keep people safe from cybersecurity attacks. In addition to 6 years in cybersecurity, my professional history includes 10 years as an Identity Designer and 10 years as an entrepreneur.
2) Website Content Purpose : the website is located at www.ketelsen.ai and is part of the blog section. Ketelsen.ai is An Ongoing AI prompt crafting Experiment. Ketelsen.ai is where the raw brainstorming and AI prompt engineering happens. Think of it as a lab where you consistently test out new prompt ideas, integrate various AI tools (ChatGPT, Midjourney, etc.), and see what sticks. The website features an Exclusive Prompt Collection with over 1,000 in-depth AI prompts covering real-world problems. These prompts are generated each week by multiple AI services, each with its own strengths. The website's Focus is on Breadth & Innovation. The site showcases new and creative ways to use AI—often pushing boundaries by running repeated “what if” experiments with different AI models. Because the goal is to innovate, Ketelsen.ai’s prompts can be very detailed (and at times, a bit overwhelming for newcomers).
3) About the Website:
Important details about Ketelsen.ai:
Step 1: Target Audience
• Demographics: Ages 25–45, global (English-speaking), professionals or entrepreneurs with moderate to high discretionary income.
• Psychographics:
○ Enthusiastic about AI-driven innovation and efficiency.
○ Enjoy experimenting with new technology but can feel overwhelmed by the sheer volume of AI options.
○ Prefer transparent “behind-the-scenes” exploration of prompt engineering.
• Persona Snapshot:
○ “Alex the AI Trailblazer”: 33-year-old product manager at a startup, craves cutting-edge AI prompts and advanced experimentation, wants to stay ahead with minimal wasted time.
Step 2: Pain Points & Unique Value Proposition
Top 3 Pain Points
1. Overwhelm by AI Options: Too many prompts/models to choose from, causing decision fatigue.
2. Difficulty Crafting Effective Prompts: Need guidance to avoid mistakes and harness AI fully.
3. Time Constraints for Testing: Users want quick, reliable feedback on what works without lengthy trial and error.
UVP (1–2 Sentences)
“Ketelsen.ai delivers a constantly evolving library of advanced AI prompts—over 1,000 and counting—so tech-savvy entrepreneurs can rapidly experiment without wasting time on guesswork. By focusing on real-world testing and transparent experimentation, it offers a more expansive and in-depth resource than other popular prompt-engineering websites.”
Step 3: Competitive Edge
1. Massive Prompt Library (over 1,000 and growing).
2. Real-Time Experimentation: Transparent and ongoing updates.
3. Behind-the-Scenes Insight: Detailed explanations of how and why prompts work.
4. Efficiency-Focused: Built around minimal time investment, demonstrating how to achieve more with less.
5. Constant Innovation: Early adoption of new AI models and features.
Step 4: Elevator Pitch
“At Ketelsen.ai, we empower tech-savvy entrepreneurs and AI enthusiasts to unlock cutting-edge automation with minimal time investment. By offering a massive, ever-growing library of high-impact prompts—backed by real-world experimentation and robust behind-the-scenes insights—we help you stay ahead in AI innovation without sacrificing your schedule. It’s your sandbox for AI exploration.”
4) Content Goals - The AI chat responses should result in real prompts that a real entrepreneur can use to achieve a real task. The task will be explained following this.
5) Role of the A.I. - You are “an expert in creating prompts for ChatGPT, Google GEMINI, Anthropic Claude and other similar Gen AI Tools” . You are creative and can use these Gen AI Tools in creative ways.
6) Content Source - Use any resource that is needed to achieve the objectives.
7) Inspirations for Content - The content style should be fun, entertaining and informative. 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.
8) Constraints -
- Please do not make up any fake information, if there is no data or actual factual information, please respond with "NOT APPLICABLE"
9) Ideas for visuals - More information to come on this
10) Related Stories/Content - This article is part of the ketelsen.ai series on 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.
Before continuing the next set of instructions, please list any items that need clarification. If there are no questions, please respond with ""READY"".
RESPONSE: READY
PROMPT:
Please brainstorm some creative ways that variables 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 CRAFT Variables:
• Reusability: Use the same value across multiple prompts without retyping it each time.
• Consistency: Ensures that all parts of your conversation or blog content reference the same core information, reducing human error.
• Efficiency: Saves tokens by shortening your instructions, which is critical when working within token limits.
Please start with an outline of the highlights of this topic.
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.
Implementation Example
pythondef 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",
"get": lambda: smart_variable["value"],
"set": lambda new_value: smart_variable.update({"value": new_value, "version": smart_variable["version"] + 1})
}Business Application
For financial reporting, a smart variable could adapt data presentation to different contexts:
pythonFINANCIAL_DATA = {
"revenue": {
"value": 4750000, # $4.75M
"currency": "USD",
"period": "annual",
"fiscal_year": 2024
},
"display_format": "standard", # standard, compact, or detailed
"get_formatted": lambda var: {
"standard": {
"revenue": f"${var['revenue']['value']:,.0f} {var['revenue']['period']}",
"growth_rate": f"{var['growth_rate']:.1%}"
},
"compact": {
"revenue": f"${var['revenue']['value']/1000000:.1f}M",
"growth": f"+{var['growth_rate']:.0%}"
},
"detailed": {
"revenue": {
"annual": var['revenue']['value'],
"quarterly": var['revenue']['value'] / 4,
"monthly": var['revenue']['value'] / 12
}
}
}[var["display_format"]]
}This variable intelligently adapts how financial data is presented based on the audience and context, without requiring manual reformatting.
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.
Implementation Example
pythondef 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])
}Business Application
For product documentation with audience adaptations:
pythonAPI_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."
},
"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)."
},
"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."
}
}
)This approach ensures appropriate content for different user skill levels, improving learning outcomes without maintaining multiple separate documentation sets.
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.
Implementation Example
pythondef 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())
}Business Application
For business model documentation:
pythonBUSINESS_MODEL = create_multimodal_variable(
"BUSINESS_MODEL",
{
"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).",
"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"
],
"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]"
}
)This ensures the underlying business model remains consistent across all materials while adapting to the optimal presentation format for each context.
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.
Implementation Example
pythondef 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_functionBusiness Application
For marketing campaigns:
pythonmarketing_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,
"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": {}
},
"timeline": {
"start": start_date,
"end": end_date
},
"success_metrics": success_metrics or {
"primary": {"metric": "Conversion rate", "target": "To be defined"},
"secondary": []
}
}
}
)This template enforces marketing best practices by ensuring all campaigns include necessary components like success metrics, proper messaging structure, and comprehensive timelines.
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.
Implementation Example
pythonclass 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
def get(self, name):
"""Get a variable's value from this namespace"""
return self.variables.get(name, {}).get("value")
def list_all(self):
"""List all variables in this namespace"""
return list(self.variables.keys())Business Application
For managing complex business structures:
pythonMarketing = Namespace("Marketing")
Marketing.add("TARGET_AUDIENCE", "Tech-savvy professionals aged 25-45")
Marketing.add("VALUE_PROPOSITION", "Save 3 hours daily with our AI assistant")
Product = Namespace("Product")
Product.add("CORE_FEATURES", ["AI scheduling", "Smart notifications", "Team sync"])
Product.add("PRICING_TIERS", {"Basic": 49, "Pro": 99, "Enterprise": 249})
Finance = Namespace("Finance")
Finance.add("CURRENT_RUNWAY", "18 months")
Finance.add("BURN_RATE", 125000)
Finance.add("REVENUE_TARGETS", {"Q1": 350000, "Q2": 425000, "Q3": 550000, "Q4": 700000})This organization prevents confusion between variables with similar names but different contexts and makes information easier to locate and maintain across multiple conversations.
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.
Implementation Example
pythondef 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"),
"update_rules": update_rules or {},
"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)],
"value": new_value,
"update_count": dynamic_variable["update_count"] + 1,
"last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}),
"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"
}Business Application
For sales conversations:
pythonCONVERSATION_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"
}
},
{
"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"]
},
reason or f"Advanced from {var['value']['current']} to {var['value']['stages'][min(var['value']['stage_index'] + 1, len(var['value']['stages']) - 1)]}"
)
}
)This approach ensures appropriate responses that match the conversation's current context while gathering valuable analytics about conversation flow without requiring manual tracking.
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.
Implementation Example
pythondef 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)Business Application
For email template specialization:
pythonBASE_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"
}
}
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}"
},
overrides={
"structure": {
"greeting": "Hi {recipient_first_name},"
}
}
)This approach ensures all communications follow brand guidelines and include necessary elements, while allowing for appropriate customization for each specific communication scenario.
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.
Implementation Example
pythondef 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
current_time = time.time()
# 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()
return {
"name": name,
"value": initial_value,
"created_at": datetime.now().isoformat(),
"expires_at": datetime.fromtimestamp(expiration_time).isoformat() if expiration_time else None,
"evolution_stages": evolution_stages or [],
"current_stage_index": 0,
"is_expired": lambda var: is_expired(var),
"time_remaining": lambda var: get_time_remaining(var),
"evolve": lambda var, reason=None: advance_stage(var, reason)
}Business Application
For seasonal marketing:
pythonMARKETING_RECOMMENDATIONS = create_time_sensitive_variable(
"MARKETING_RECOMMENDATIONS",
{
"season": "spring",
"themes": ["Renewal", "Growth", "Fresh Start", "Spring Cleaning"],
"color_palette": ["#a8e6cf", "#dcedc1", "#ffd3b6", "#ffaaa5", "#ff8b94"],
"messaging_tone": "Energetic, optimistic, forward-looking"
},
# Expires at end of current season
expiration=get_end_of_current_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"],
"messaging_tone": "Energetic, optimistic, forward-looking"
},
# Summer (June-August)
{
"season": "summer",
"themes": ["Adventure", "Freedom", "Outdoors", "Energy", "Relaxation"],
"color_palette": ["#f9ed69", "#f08a5d", "#b83b5e", "#6a2c70", "#08d9d6"],
"messaging_tone": "Casual, exciting, bold, playful"
}
# Additional seasons would be defined here
]
)This approach ensures that marketing recommendations are always seasonally appropriate, with the variable automatically transitioning between seasonal themes and strategies based on the calendar date.
Conclusion
These eight advanced variable concepts dramatically enhance the capabilities of the CRAFT framework, enabling deeper, more intuitive, and more meaningful interactions with AI chat tools. By incorporating these concepts, entrepreneurs can create sophisticated conversational systems that maintain state, adapt to context, present information optimally, and evolve over time.
When implementing CRAFT in your projects, consider which of these variable concepts best support your specific use cases and systematically integrate them into your framework. The result will be AI interactions that feel more natural, intelligent, and productive—transforming AI from a simple tool into a true collaborative partner.
CRAFT Framework Overview
Based on the provided files, CRAFT (Configurable Reusable AI Framework Technology) is an innovative approach to AI interactions that applies object-oriented programming principles to AI conversations. The framework consists of three main components:
CFT-FWK-SPEC: The framework specification file that defines the core concepts, data types, and structures
CFT-PROJ files: Project-specific implementations containing variables, functions, and objects
CFT-PROJ-AI-CHAT-HISTORY: A file that maintains continuity between AI sessions through a "Handoff" system
The philosophy behind CRAFT appears to be centered on six key principles:
Structure Enables Freedom: Creating a clear framework to eliminate cognitive overhead
Code as Conversation: Using programming concepts for clearer AI interactions
Cumulative Intelligence: Building knowledge across multiple AI interactions
Human-AI Partnership: Facilitating optimal collaboration
Accessibility Through Familiarity: Using Python-like syntax for approachability
Conservation of Tokens: Reducing token usage for efficiency
Key Components of CRAFT
Data Types
The framework defines numerous specialized data types like URL, ColorPalette, Prompt, AIRecipe, and many others that are designed specifically for AI interactions.
Communication Syntax
CRAFT implements a structured comment system:
#H->AI::for human-to-AI communications#AI->H::for AI-to-human communications#H->H::for human-to-human notes
Variables, Functions, and Objects
Similar to programming paradigms, CRAFT uses:
Variables for reusable values
Functions for modular operations
Objects for bundling related functionality
Handoff System
The framework includes a sophisticated handoff mechanism to maintain context between different AI sessions, complete with metadata, summaries, and next steps.
Your Current Project
Based on the paste.txt file, you appear to be working on content for ketelsen.ai, specifically related to the CRAFT framework. This is part of an "ongoing weekly experiment" to develop new ways of communicating with AI.
You've provided a detailed prompt structure and website information, including:
Your personal background (cybersecurity, design, entrepreneurship)
Website purpose (AI prompt crafting experiments)
Target audience (tech-savvy entrepreneurs and AI enthusiasts)
Content goals (creating practical prompts for entrepreneurs)
Next Steps
I understand that you want to brainstorm content for your website. The CRAFT framework files provide the technical foundation, while your prompt in paste.txt outlines the specific content direction.
I'm READY to help you create content that demonstrates the CRAFT framework for your target audience of entrepreneurs looking to improve their AI interactions. Do you want to focus on a specific aspect of CRAFT for this content, or would you like me to suggest some potential topics based on the materials provided?
Retry
RK
Please brainstorm some creative ways that variables 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 CRAFT Variables: * Reusability: Use the same value across multiple prompts without retyping it each time. * Consistency: Ensures that all parts of your conversation or blog content reference the same core information, reducing human error. * Efficiency: Saves tokens by shortening your instructions, which is critical when working within token limits. Please start with an outline of the highlights of this topic.
Edit
Creative Applications of CRAFT Variables: Brainstorming Session
Outline Highlights
Beyond Basic Value Storage: Expanding variables to become smart containers
Context-Aware Variables: Variables that adapt based on conversation stage
Multi-Modal Variables: Handling different content types within a single variable
Variable Templates: Pre-configured variable structures for common tasks
Variable Namespaces: Organizing variables for complex projects
Dynamic Variables: Self-updating variables based on conversation progress
Variable Inheritance: Borrowing concepts from OOP inheritance
Time-Sensitive Variables: Variables with built-in expiration or evolution
Detailed Brainstorming
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:
pythondef 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_objExample usage for entrepreneurs:
pythonBUSINESS_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:
pythondef 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:
pythonPITCH_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:
pythondef 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:
pythonPRODUCT_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:
pythonclass 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:
pythonclass 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:
pythonMarketing = 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:
pythondef 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:
pythondef 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:
pythondef 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_varExample for entrepreneurs:
pythonBASE_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:
pythondef 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:
python# 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
)Practical Applications for Entrepreneurs
Building on the CRAFT philosophy outlined in your files, here are specific entrepreneurial applications:
Project Management Variables
Define variable templates for project stages, milestones, and status updates
Create inheritance patterns for similar projects to maintain consistency
Marketing Campaign Variables
Use multi-modal variables to maintain consistent messaging across channels
Implement time-sensitive variables for limited-time offers and campaign stages
Customer Communication Variables
Develop context-aware variables that adapt communication style based on customer segments
Build progressive variables that evolve as the customer moves through the sales funnel
Product Development Variables
Create namespace organization for features, specifications, and roadmaps
Implement dynamic variables that track development progress and updates
Financial Projection Variables
Design variable templates for consistent financial modeling
Use smart variables that can adapt calculations based on different scenarios
These applications align perfectly with the CRAFT principles of structure enabling freedom, code as conversation, and cumulative intelligence, while providing tangible business value to your entrepreneurial audience.
Would you like me to explore any of these concepts in more detail or suggest how they might be incorporated into your current CRAFT framework implementation?
Retry
RK
Great, please explain 1. Beyond Basic Value Storage: Smart Variables in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Beyond Basic Value Storage: Smart Variables in CRAFT
Understanding Smart Variables
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.
In standard programming, variables act as simple containers:
pythonuser_name = "Alex" # Basic variable storing a stringSmart 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:
pythondef 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:
pythonCUSTOMER_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:
pythonPRICING_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:
pythonBUSINESS_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:
pythonPRODUCT_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:
pythonMARKET_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
pythonSALES_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
pythonCUSTOMER_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
pythonBUSINESS_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.
Would you like me to draft any of these implementation elements specifically for your CRAFT framework?
Retry
RK
Great, please explain 2. Context-Aware Variables: in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Context-Aware Variables in CRAFT
Understanding Context-Aware Variables
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
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:
pythondef 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:
pythonPRODUCT_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:
pythonPRODUCT_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:
pythonAPI_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:
pythonSTARTUP_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:
pythonMARKETING_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:
pythondef 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:
pythonPRODUCT_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")]Retry
RK
Great, please explain 3. Multi-Modal Variables: in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
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:
pythondef 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:
pythonBUSINESS_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:
pythonPRODUCT_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:
pythonSECURITY_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:
pythonCOMPANY_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:
pythonMARKET_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:
pythonPITCH_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.
Would you like me to expand on any particular aspect of multi-modal variables or suggest specific implementation approaches for your CRAFT framework?
Retry
RK
Great, please explain 4. Variable Templates: Pre-configured variable structures for common tasks in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Variable Templates in CRAFT
Understanding Variable Templates
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.
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:
pythondef 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_functionMore 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:
python# 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_contentBusiness 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.
Would you like me to explore any specific aspect of variable templates in more detail, or suggest how they might be implemented within your current CRAFT framework structure?
2 / 2
Retry
RK
Great, please explain 5. Variable Namespaces in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Variable Namespaces in CRAFT
Understanding Variable Namespaces
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.
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:
pythonclass 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"] = 28.75Business 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.
Would you like me to focus on any specific aspect of namespace implementation for your CRAFT framework, such as how to integrate namespace visualization or how to create domain-specific namespace templates?
Retry
RK
Great, please explain 6. Dynamic Variables: Self-updating variables based on conversation progress in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Dynamic Variables in CRAFT
Understanding Dynamic Variables
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 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:
pythondef 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:
python# 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 NoneBusiness 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 opportunitiesBusiness 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.
Would you like me to focus on any specific aspect of dynamic variable implementation for your CRAFT framework?
Retry
RK
Great, please explain 7. Variable Inheritance: Borrowing concepts from OOP inheritance in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
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:
pythondef 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.
Would you like me to explore any specific aspect of variable inheritance implementation for your CRAFT framework in more detail?
Retry
RK
Great, please explain 8. Time-Sensitive Variables: Variables with built-in expiration or evolution in greater detail including some basic knowledge on what these are, what the benefits are, and more examples that explain the benefits
Edit
Time-Sensitive Variables in CRAFT
Understanding Time-Sensitive Variables
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 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:
pythondef 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:
python# 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 PreviewRetry
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Edit
Time-Based Content Publishing Schedule (Continued)
Let me continue with the advanced example for a time-sensitive content publishing schedule:
python# 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 averagesBusiness 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.
Implementation in CRAFT Framework
To integrate time-sensitive variables into your CRAFT framework effectively, consider:
Creating a time-sensitivity layer that can be applied to any variable type
Implementing automatic expiration checking that validates variables before use
Building time-based triggers for automatic variable evolution
Developing a persistence mechanism for time history across sessions
This approach would significantly enhance the CRAFT framework's ability to maintain time-relevant information and automatically adapt content based on temporal factors, aligning perfectly with your goal of creating cumulative intelligence across AI interactions.
The examples above demonstrate how time-sensitive variables can transform static information into dynamic, evolving content that remains relevant without manual intervention, making them especially valuable for entrepreneurs managing complex projects with time-dependent components.
