CRAFT™️ Experiment: Recap: Rethinking A.I. Functions (1 of 5)

THE CRAFT™️ EXPERIMENT :: WEEK 6 :: POST 1

Recap of Last Week: Rethinking A.I. Functions (1 of 5)

Mastering Function Adaptation in the CRAFT Framework: 10 Game-Changing Patterns for AI Conversations

Welcome to another deep dive into CRAFT™️ (Configurable Reusable AI Framework Technology), where we're revolutionizing how humans and AI communicate by adapting object-oriented programming principles to conversational AI. This week, we explored one of programming's most fundamental concepts—functions—and discovered 10 innovative ways to transform them into powerful tools for AI interaction.

Why Functions Matter in AI Conversations

In traditional programming, functions are the workhorses that make code modular, maintainable, and reusable. But what happens when we apply these same principles to AI conversations? Magic. By reimagining functions for the CRAFT framework, we unlock new levels of efficiency, clarity, and intelligence in our AI interactions.

Each adaptation we'll explore aligns with CRAFT's six guiding principles, creating a synergy that transforms chaotic AI chats into structured, evolving dialogues that build knowledge over time.

The 10 Function Patterns That Will Transform Your AI Workflow

1. CRAFT Function Definitions: Your Prompt Templates on Steroids

Think of CRAFT functions as reusable prompt templates that accept parameters and return structured responses. Instead of rewriting similar prompts repeatedly, you define once and call many times.

Example:

python

func summarize(text, style="concise"):
    """Summarize `text` in two sentences."""
    prompt: "Summarize the following in two sentences ({style}):\n\n{text}"
    return summary

Key Benefits:

• Modularity and maintainability

• Token savings through reuse

• Consistent output quality

• Easy updates across all uses

2. Parameterization as Context Injection: Dynamic Prompts Made Simple

Transform static prompts into dynamic, context-aware instructions by injecting parameters. This pattern turns hard-coded details into flexible, reusable components.

Example:

python

func translate(text, to_language):
    prompt: "Translate the following into {to_language}:\n\n{text}"
    return translated_text

Key Benefits:

• Dynamic customization without rewriting

• Self-documenting function calls

• Token efficiency through targeted injection

• Clear separation of concerns

3. Return Templates for Structured Replies: Predictable AI Outputs

Define exactly what shape your AI responses should take. No more parsing free-form text—get structured, predictable outputs every time.

Example:

python

func generate_blog_summary(text):
    prompt: "Provide a JSON with `title`, `bullet_points` (3 items), and `read_time`"
    return {"title": string, "bullet_points": [string,string,string], "read_time": integer}

Key Benefits:

• Consistency across responses

• Easier post-processing

• Built-in validation

• Reduced token waste on formatting

4. Composable Prompt Chains: Building Complex Workflows Simply

Create sophisticated AI workflows by chaining simple functions together. Each function's output becomes the next function's input, creating powerful pipelines.

Example:

python

func compose(f1, f2):
    prompt: "Run `{f1}` then feed its output into `{f2}`."
    return composed

summ_trans = compose(summarize, translate)
result = summ_trans(article, "French")

Key Benefits:

• Extreme reusability

• Clear workflow visualization

• Token-efficient orchestration

• Scalable complexity management

5. Stateful Functions: Memory That Spans Sessions

Give your AI conversations true memory by maintaining state across function calls. This pattern enables genuine cumulative intelligence.

Example:

python

func set_variable(state, name, value):
    state.vars[name] = value
    return {"state": state}

Key Benefits:

• Builds on previous interactions

• Reduces repetitive context-setting

• Enables dynamic workflows

• Supports error recovery

6. Async & Streaming Functions: Real-Time AI Collaboration

Enable interactive, streaming responses that allow for mid-course corrections and real-time feedback. Perfect for long-running or complex tasks.

Example:

python

func progressive_summarize(text, steps=3) async stream:
    prompt: "Summarize `{text}` in {steps} progressive passes, yielding each"
    return summary_stream

Key Benefits:

• Real-time user feedback integration

• Early error detection

• Interactive refinement

• Better handling of large tasks

7. Recursion & Iterative Refinement: Polish Until Perfect

Automatically improve outputs through multiple refinement passes. The AI critiques and enhances its own work until quality thresholds are met.

Example:

python

func refine(text, passes=3, target_score=8.0):
    prompt: "Rate readability (0-10) and improve if below {target_score}"
    if score < target_score and passes > 1:
        return refine(improved_text, passes-1, target_score)
    return improved_text

Key Benefits:

• Automated quality improvement

• Consistent output standards

• Token-efficient iteration

• Self-correcting workflows

8. Namespaces & Modules: Organizing Your AI Toolkit

Group related functions into logical modules, preventing naming conflicts and improving discoverability. Think of it as creating specialized AI toolkits.

Example:

python

module nlp:
    import summarize, translate
module data:
    import tabulate, extract_entities

# Usage: nlp.summarize(report)

Key Benefits:

• Prevents naming collisions

• Improves function discovery

• Scales elegantly

• Creates logical organization

9. Decorators & Hooks: Cross-Cutting Concerns Made Easy

Add validation, logging, retry logic, or token budgeting to any function without cluttering its core logic. These wrappers handle the "administrative" tasks.

Example:

python

@enforce_budget(150)
func outline(topic):
    prompt: "Create a 300-word outline for: {topic}"
    return outline_struct

Key Benefits:

• Clean separation of concerns

• Reusable validation logic

• Automatic retry handling

• Token budget enforcement

10. Token-Budgeted Signatures: Maximum Impact, Minimum Tokens

Design function interfaces that respect token limits from the ground up. Every parameter is justified, every default is strategic.

Example:

python

# Before: func translate(text, to_language, tone)
# After:  func translate(txt, lang="en")

Key Benefits:

• Predictable token costs

• Faster API responses

• Cleaner interfaces

• Fewer truncation errors

Bringing It All Together: The CRAFT Advantage

These 10 patterns don't exist in isolation—they work together to create a comprehensive system for AI interaction. By combining stateful functions with composable chains, adding decorators for validation, and organizing everything into logical modules, you create an AI interaction framework that is:

• Efficient: Minimal tokens, maximum impact

• Scalable: Grows with your needs

• Maintainable: Easy to update and improve

• Intelligent: Builds knowledge over time

• Accessible: Intuitive for both beginners and experts

Your Next Steps

Ready to revolutionize your AI workflows? Here's how to get started:

1. Pick One Pattern: Start with the pattern that addresses your biggest pain point

2. Implement a Simple Version: Create your first CRAFT function

3. Test and Iterate: See how it performs in real conversations

4. Combine Patterns: Layer in additional patterns as you grow comfortable

5. Share Your Results: Join the CRAFT community and share your innovations

The Bottom Line

By adapting programming's most powerful abstraction—the function—to AI conversations, CRAFT transforms chaotic prompt engineering into a systematic, scalable practice. Whether you're building a simple chatbot or orchestrating complex AI workflows, these 10 patterns give you the tools to work smarter, not harder.

The future of AI interaction isn't about writing better prompts—it's about building better systems. And with CRAFT's function patterns, that future is here today.

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