CRAFT™️ Experiment: Rethinking A.I. Functions (1 of 5) Recap of Last Week
THE CRAFT™️ EXPERIMENT :: WEEK 5 :: POST 1
Rethinking A.I. Functions (1 of 5) Recap of Last Week
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Please brainstorm some creative ways that functions can be adapted to the CRAFT framework. Please refer to the attached files for information about the current state of this project. For reference, here are some benefits of regular functions:
In programming, a function is a reusable block of code that performs a specific task. It's like a mini-program within a larger program, designed to be called (or invoked) when needed. Functions help organize code, making it easier to read, understand, and maintain. They also promote code reusability, as you can use the same function multiple times with different inputs
Key Concepts:
• Input: Functions can accept data (called arguments or parameters) as input, allowing them to perform different tasks based on the data they receive.
• Output: Functions can return a value (the output) as a result of their execution.
• Reusability: A function can be called multiple times from different parts of a program, saving you from rewriting the same code repeatedly.
• Organization: Functions help break down complex programs into smaller, manageable units.
Abstraction: Functions hide the implementation details of a task, allowing you to focus on what the task does rather than how it does it.
How can these known concepts be adapted to CRAFT which is built on six key philosophical principles:
1. STRUCTURE ENABLES FREEDOM: By providing a clear framework for AI interaction,
CRAFT paradoxically creates more creative freedom by eliminating cognitive
overhead and repetition.
2. CODE AS CONVERSATION: Programming concepts like variables, functions, and
objects can dramatically improve the clarity and efficiency of natural
language exchanges with AI.
3. CUMULATIVE INTELLIGENCE: AI interactions should build upon each other,
creating an evolving knowledge base rather than isolated conversations.
4. HUMAN-AI PARTNERSHIP: The framework facilitates true collaboration where
both human creativity and AI capabilities are leveraged optimally.
5. ACCESSIBILITY THROUGH FAMILIARITY: Using Python-like syntax creates an
approachable system for both technical and non-technical users.
6. CONSERVATION OF TOKENS: Limiting tokens in an A.I. chat saves money and time.
It also cuts down on the amount of vertical scrolling needed in a chat.
Please start with an outline of the highlights of this topic.
Supercharging Your AI Interactions: Advanced Variable Strategies in CRAFT Framework
Artificial Intelligence is rapidly transforming how entrepreneurs and innovators approach their work. But to truly harness its power, we need ways to communicate with AI that are both intuitive and deeply effective. That's where the CRAFT™️ (Configurable Reusable AI Framework Technology) from Ketelsen.ai comes in—an innovative approach dedicated to adapting principles of object-oriented programming to AI chats for more meaningful interactions.
At the heart of CRAFT™️ lies the variable—a powerful concept borrowed from programming but reimagined for AI conversations. While variables might seem simple on the surface, they form the foundation of a sophisticated framework designed to revolutionize how we interact with AI assistants.
The CRAFT Framework Philosophy
Before diving into advanced variable concepts, let's revisit the six philosophical principles that guide the CRAFT framework:
STRUCTURE ENABLES FREEDOM: By providing a clear framework for AI interaction, CRAFT paradoxically creates more creative freedom by eliminating cognitive overhead and repetition.
CODE AS CONVERSATION: Programming concepts like variables, functions, and objects can dramatically improve the clarity and efficiency of natural language exchanges with AI.
CUMULATIVE INTELLIGENCE: AI interactions should build upon each other, creating an evolving knowledge base rather than isolated conversations.
HUMAN-AI PARTNERSHIP: The framework facilitates true collaboration where both human creativity and AI capabilities are leveraged optimally.
ACCESSIBILITY THROUGH FAMILIARITY: Using Python-like syntax creates an approachable system for both technical and non-technical users.
CONSERVATION OF TOKENS: Limiting tokens in an A.I. chat saves money and time. It also cuts down on the amount of vertical scrolling needed in a chat.
Core Benefits of CRAFT Variables
Variables in CRAFT aren't just placeholders—they're the structural atoms of the entire ecosystem. They offer three fundamental benefits that transform how we work with AI:
Reusability: Use the same value across multiple prompts without retyping it each time. Declare once, reference everywhere. No more copy-paste typos or scattered updates.
Consistency: Ensures that all parts of your conversation reference the same core information, reducing human error. Create a "single source of truth" that keeps your entire project aligned.
Efficiency: Saves tokens by shortening your instructions, which is critical when working within token limits. Swap out a short variable name for a lengthy phrase, and watch your token count (and costs!) shrink.
Now, let's explore advanced variable concepts that can take your CRAFT framework implementation to the next level.
8 Advanced Variable Concepts for Next-Level AI Interaction
1. Smart Variables: Beyond Basic Value Storage
What Are Smart Variables?
Smart variables represent an evolution from traditional static variables. 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:
def create_smart_variable(initial_value, metadata=None):
"""Creates a basic smart variable with state tracking."""
return {
"value": initial_value,
"metadata": metadata or {},
"history": [initial_value],
"version": 1,
"updated_at": "2025-05-11",
"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:
FINANCIAL_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:
def create_context_variable(name, context_map, default_context=None):
"""Creates a variable that returns different values based on context."""
return {
"name": name,
"contexts": list(context_map.keys()),
"values": context_map,
"default_context": default_context or list(context_map.keys())[0],
"get": lambda ctx=None: context_map.get(ctx or default_context or list(context_map.keys())[0])
}Business Application: For product documentation with audience adaptations:
API_DOCUMENTATION = create_context_variable(
"API_DOCUMENTATION",
{
"beginner": {
"overview": "Our API lets you connect your app to our service. Think of it as a bridge between different software systems.",
"authentication": "First, you'll need an API key. This is like a special password that lets our system know who you are."
},
"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:
def create_multimodal_variable(name, content_modes):
"""Creates a variable that can output different formats of the same content."""
return {
"name": name,
"modes": list(content_modes.keys()),
"content": content_modes,
"get": lambda mode=None: content_modes.get(mode or "default", content_modes.get("default")),
"available_modes": lambda: list(content_modes.keys())
}Business Application: For business model documentation:
BUSINESS_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:
def create_variable_template(template_name, structure_generator):
"""Creates a reusable template for generating variables with consistent structure."""
def template_function(*args, **kwargs):
return structure_generator(*args, **kwargs)
template_function.__name__ = template_name
return template_functionBusiness Application: For marketing campaigns:
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,
"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:
class Namespace:
def __init__(self, name, description=None):
self.name = name
self.description = description or f"Namespace for {name}-related variables"
self.variables = {}
def add(self, name, value, description=None):
"""Add a variable to this namespace"""
self.variables[name] = {
"value": value,
"description": description or f"Variable '{name}' in {self.name} namespace"
}
return self
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:
Marketing = 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:
def create_dynamic_variable(name, initial_value, update_rules=None):
"""Creates a variable that can update itself based on defined rules or external triggers."""
from datetime import datetime
return {
"name": name,
"value": initial_value,
"previous_values": [],
"update_count": 0,
"last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"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:
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"
}
},
{
"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:
def inherit(parent_variable, extensions=None, overrides=None):
"""Create a new variable that inherits from a parent variable."""
# Start with a deep copy of the parent
if isinstance(parent_variable, dict):
result = copy.deepcopy(parent_variable)
# Apply extensions (add new properties)
if extensions and isinstance(extensions, dict):
for key, value in extensions.items():
if key not in result:
result[key] = value
elif isinstance(result[key], dict) and isinstance(value, dict):
result[key] = {**result[key], **value}
elif isinstance(result[key], list) and isinstance(value, list):
result[key] = result[key] + value
else:
result[key] = value
# Apply overrides (replace existing properties)
if overrides and isinstance(overrides, dict):
for key, value in overrides.items():
result[key] = value
return result
elif isinstance(parent_variable, list):
return copy.deepcopy(parent_variable) + (extensions or [])
elif isinstance(parent_variable, str):
return parent_variable + "\n\n" + (extensions or "")
else:
# For primitive types, just override with extensions if provided
return extensions if extensions is not None else copy.deepcopy(parent_variable)Business Application: For email template specialization:
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"
}
}
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:
def create_time_sensitive_variable(name, initial_value, expiration=None, evolution_stages=None):
"""Creates a variable with time-based behaviors like expiration and evolution."""
import time
from datetime import datetime
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:
MARKETING_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.
Best Practices for Variable Implementation
To get the most out of your CRAFT variables, consider these best practices:
Use Descriptive Names: Choose variable names that clearly communicate purpose over cryptic abbreviations (e.g.,
marketing_budgetnotmb).Create a Variable Documentation Block: Centralize your variable definitions in a
PROJECT_VARIABLESsection that serves as a legend for your project.Apply Consistent Naming Patterns:
- Global constants in ALL_CAPS (e.g.,
PROJECT_NAME,API_VERSION) - Workflow variables in snake_case (e.g.,
current_step,user_feedback) - Parameterized builders with verb_noun style (e.g.,
build_prompt,format_date)
- Global constants in ALL_CAPS (e.g.,
Use Namespacing: Apply namespacing to prevent collisions as your library grows (e.g.,
Marketing.campaign_namevs.Analytics.sales_target).Prune Regularly: Remove unused variables to prevent bloat and confusion.
Group Related Variables: Organize variables by function or domain for easier maintenance.
Add Comments: Document complex variables, especially those with specific formats or requirements.
Why This Matters for Entrepreneurs & Innovators
For the tech-savvy entrepreneur—like "Alex the AI Trailblazer" in Ketelsen.ai's audience profile—these advanced variable strategies directly address key pain points:
Combat AI Overwhelm: Instead of juggling countless prompts and models, these variables offer structured, reusable, and adaptable ways to guide your AI.
Craft More Effective Prompts: By embedding logic, context, and fine-grained control into variables, you're inherently creating more potent and precise instructions.
Slash Testing Time: Reusable, templated, and conditional variables mean less trial-and-error and more reliable, consistent outputs.
These strategies empower you to build deeper, more nuanced, and significantly more efficient partnerships with your AI tools. You can automate more complex tasks, personalize AI behavior to an unprecedented degree, and manage your creative and operational workflows with greater ease and precision.
Integration Within the CRAFT Framework Ecosystem
The true power of variables emerges when they're fully integrated into the CRAFT framework:
Inter-Session Variable Persistence: The HANDOFF_SNAPSHOT mechanism captures variable values at the close of each session and reloads them in the next, preserving cumulative context without manual re-establishment.
Modular Variable Libraries: Organize variables into centralized libraries that can be updated in one place, with changes propagating automatically across all references.
Adaptive Rewriting and Contextual Updates: Variables can be updated mid-session in response to new inputs or changing project directions, ensuring each phase of the conversation reflects the most current state.
API-Driven Variable Updates: Connect variables to external APIs to update in real-time with live data, creating a continuously updated conversation that aligns with real-world project progress.
Future Directions
As CRAFT continues to evolve, we see several exciting possibilities for variable technology:
Machine Learning-Supported Variables: Analyzing historical conversation data could refine variable values automatically, creating a self-optimizing system that enhances user satisfaction.
Integrated Security and Access Controls: For sensitive data, securing variables with encryption and access controls ensures that only authorized users can modify or view specific information.
Visual Variable Dashboards: Translating variable states into interactive dashboards would allow stakeholders to monitor changes in real-time, democratizing data management while enhancing oversight.
Conclusion
By reimagining variables as dynamic, interconnected components, the CRAFT framework elevates AI interaction from isolated sessions to a continuous, evolving narrative of innovation and efficiency. These advanced variable concepts dramatically enhance the capabilities of the framework, enabling deeper, more intuitive, and more meaningful interactions with AI chat tools.
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.
The CRAFT™️ framework is an evolving journey into the future of human-AI collaboration. What creative ways can you envision using variables to enhance your AI interactions? Dive into the experiments at ketelsen.ai and let's CRAFT™️ the future together!
