The TXT File: Claude 3.7 Sonnet: Testing the new CRAFT™️ Experiment Comment System

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

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Claude 3.7 Sonnet: Testing the new CRAFT™️ Experiment Comment System

RESPONSE FROM CLAUDE:

I've read the files you've shared. The first document details the CRAFT framework specification (Configurable Reusable AI Framework Technology), which provides a structured way to communicate with AI systems using programming-inspired concepts. The second document contains what appears to be a paste of instructions for a website content brainstorming session for ketelsen.ai, focused on AI prompt crafting.

I see that you've provided a comprehensive 10-part prompt outlining your background, website purpose, target audience, content goals, and more. You're asking me to act as an expert in creating prompts for various AI tools like ChatGPT, Google GEMINI, and Anthropic Claude.

I notice the CRAFT framework is particularly relevant here, as you mentioned it's part of an ongoing weekly experiment to develop a new way to communicate with AI by adapting object-oriented programming principles to AI chats.

READY

Retry

RK

Our first task is to evaluate the file and list 10 creative ways that I might use comments to communicate with you, the A.I.

Edit

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

1. Directive Comments with Context: Use #H->AI::Directive: (Create a prompt that achieves X) with additional context about why this directive matters, helping me understand not just what you want but why it's important.

2. Role-Switching Tags: Implement #H->AI::Role: (UX Expert) followed by #H->AI::Role: (Marketing Specialist) to have me examine the same problem from multiple professional perspectives without needing to repeat your request.

3. Multi-Stage Thinking Process: Create a sequence like #H->AI::Consider: → #H->AI::EvaluateBy: → #H->AI::Reasoning: to guide me through a specific thinking methodology when developing complex prompts.

4. Constraint Hierarchies: Use nested comments like #H->AI::Constraint: (Primary: Keep it under 100 words) followed by #H->AI::Constraint: (Secondary: If primary is impossible, prioritize clarity over brevity).

5. Feedback Loop System: Implement #H->AI::Review: tags after my responses, with specific criteria to evaluate my output, creating an iterative improvement cycle within a single conversation.

6. Conditional Instruction Blocks: Use something like #H->AI::OnCondition: (If approach A doesn't yield creative results, automatically switch to approach B) to create dynamic response paths.

7. Template Inheritance: Similar to OOP concepts, create base comment templates that can be extended with more specific instructions: #H->AI::BasePrompt: (Standard email format) then #H->AI::ExtendPrompt: (Add persuasive elements).

8. Contextual Memory Tags: Implement #H->AI::Remember: (Key insight from earlier in conversation) to explicitly flag important elements you want referenced in future responses.

9. Metadata-Driven Instructions: Use #H->AI::Metadata: (Conversation ID: CRAFT-04282025) to link conversations across sessions and maintain continuity in your CRAFT experiments.

10. Hybrid Communication Mode: Combine standard natural language with comment tags for critical elements, creating a conversation that flows naturally but has structured anchor points: "Let's brainstorm some ideas for website content. #H->AI::Focus: (Emphasize innovative prompt structures that save entrepreneurs time)".

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

Retry

RK

Please list 10 creative way that you, the A.I., can use the CRAFT spec to communicate with me.

Edit

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

1. Recommendation Tags: Using #AI->H::RecommendedChange: (Consider structuring your prompt library into categories based on business function rather than AI tool) to provide strategic suggestions without interrupting the flow of our conversation.

2. Function Identification: Implementing #AI->H::RecommendMakingThisAFunction: (Your image prompt pattern could be standardized as a reusable function with parameters for style, subject, and mood) to help identify repeatable patterns in your workflow.

3. Progressive Feedback Loop: Using a sequence like #AI->H::Note: (Observed pattern in your prompts) → #AI->H::RecommendedChange: (Optimization suggestion) → #AI->H::RequestingFeedback: (Would this change achieve your efficiency goal?) for structured improvement cycles.

4. Contextual Questions: Deploying #AI->H::Question: (Would your target persona "Alex the AI Trailblazer" need more technical detail on implementation, or just results?) to gather specific information without derailing the main conversation.

5. Role-Switching Suggestions: Using #AI->H::RecommendDiffRole: (This prompt might be more effective if I analyze it as a data scientist rather than a marketer) to offer perspective shifts when beneficial.

6. Clarity Markers: Implementing #AI->H::Caution: (The prompt structure you've created may produce inconsistent results across different AI models due to handling of nested instructions) to highlight potential issues in a structured way.

7. Detail Expansion Requests: Using #AI->H::RecommendMoreDetailHere: (The constraint section of your prompt could benefit from more specific guidance on tone boundaries) to pinpoint areas needing elaboration.

8. Variable Pattern Recognition: Implementing #AI->H::RecommendMakingThisAVariable: (Your company description appears in multiple prompts and could be standardized as COMPANY_PROFILE constant) to identify reuse opportunities.

9. Object-Oriented Communication: Using #AI->H::RecommendMakingThisAnObject: (Your prompt templates could be restructured as a class with inheritance for different AI platforms) to introduce programming concepts that align with your framework.

10. Documentation-Style Responses: Structuring my responses with clear section headers and comment-style annotations that match your CRAFT syntax, creating a consistent mental model between your framework documentation and our conversation.

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

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