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

THE CRAFT™️ EXPERIMENT :: WEEK 3 :: POST 2

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ChatGPT 04-mini-high: Rethinking A.I. Data Types

• Initial Prompt Given to the A.I.

  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:

  Based on analysis of modern AI engineering needs, I am considering new specialized data types. These data types will make CRAFT code more expressive and easier to understand while reducing boilerplate. They are concepts that any A.I. can understand without defining them (for example: users do not need to explain to a A.I. Chat what a prompt is because the A.I. knows this term and what a prompt is and what it cannot be). Here are some ideas that I have:

  Communication & Context Data Types

  Intent: Represents a user's goal or purpose in an interaction

  Example: Intent("book_flight", confidence=0.92)

  Value: Helps AI understand what users want to accomplish without explicit instructions

  Entity: Named pieces of information extracted from text

  Example: Entity("destination", "Paris", category="location")

  Value: Identifies key information points without complex parsing code

  Sentiment: Emotional tone in text or speech

  Example: Sentiment("positive", confidence=0.87)

  Value: Enables emotional intelligence in AI responses

  DialogueContext: Maintains conversation state with history and reference points

  Example: DialogueContext(turns=5, current_topic="booking_details")

  Value: Simplifies building conversational flows

  Content & Media Data Types

  RichContent: Text with formatting, links, and media

  Example: RichContent("Check this important [resource](https://example.com)")

  Value: Represents formatted content without HTML/Markdown boilerplate

  MediaReference: Standardized reference to image, audio, or video

  Example: MediaReference("image", url="https://example.com/img.jpg", alt="Product photo")

  Value: Consistent handling of media across different AI platforms

  Summary: Condensed version of longer content

  Example: Summary("Long meeting transcript...", max_length=100)

  Value: Standardizes content summarization without repetitive prompting

  Business & Analytics Data Types

  UserProfile: Collection of user preferences and behaviors

  Example: UserProfile(industry="healthcare", role="manager", interests=["AI", "automation"])

  Value: Provides context for personalized AI interactions

  Metric: Named value with unit and context

  Example: Metric("conversion_rate", 3.2, unit="percent", trend="increasing")

  Value: Standardized business KPI representation

  TimeSeries: Temporal sequence of data points

  Example: TimeSeries([{date: "2025-04-01", value: 120}, ...], metric="sales")

  Value: Simplifies trend analysis and forecasting

  Annotation: Comments or notes attached to content

  Example: Annotation("This paragraph needs revision", severity="suggestion")

  Value: Standardizes feedback and collaboration

  Functional Data Types

  ActionTemplate: Reusable pattern for AI actions

  Example: ActionTemplate("email_customer", parameters=["name", "order_id"])

  Value: Creates standardized workflows for common business processes

  DataValidator: Rules for validating input

  Example: DataValidator(type="email", required=true, format="corporate")

  Value: Ensures data quality without complex validation code

  Transformation: Data conversion specification

  Example: Transformation("csv_to_table", options={"headers": true})

  Value: Standardizes data processing steps

  Why These Align With CRAFT Objectives

  These data types align with your objectives because they:

  Reduce boilerplate: Replace complex code patterns with simple, declarative structures

  Increase expressiveness: Clearly communicate intent without lengthy documentation

  Are universally understood by AI: Represent concepts that all modern AI systems understand innately

  Are accessible to non-technical users: Use business-friendly terminology rather than technical jargon

  Enable practical applications: Focus on common business scenarios that entrepreneurs encounter daily

  Each type provides a clear, practical benefit that would be immediately recognizable to your target users, making the CRAFT framework more intuitive and productive for AI-assisted tasks in business contexts.

  Beginner-Friendly Data Types for CRAFT Framework

  For AI beginners with less than a year of experience, we need data types that are immediately intuitive, self-explanatory, and relate to familiar concepts. Here are data types that would make CRAFT more accessible to newcomers:

  Learning & Guidance Data Types

  Template: Ready-to-use prompt structures with placeholders

  Example: Template("product_description", slots=["product_name", "features", "target_audience"])

  Value: Helps beginners follow proven patterns without reinventing prompts

  Example: Sample inputs and outputs for reference

  Example: Example(input="Summarize climate change", output="Climate change refers to...")

  Value: Shows concrete results to help beginners understand what's possible

  Feedback: Guidance on improving prompts

  Example: Feedback("Add more specific details about your target audience")

  Value: Provides built-in coaching to accelerate learning

  Simplified Task Data Types

  Question: Simple query for information

  Example: Question("What are the benefits of solar energy?")

  Value: Clarifies that the AI should focus on providing information

  Instruction: Clear direction for the AI to follow

  Example: Instruction("Write a professional email to a client about a project delay")

  Value: Makes it explicit when users want the AI to create something

  Comparison: Request to evaluate alternatives

  Example: Comparison("electric vs. gas vehicles", aspects=["cost", "environment", "convenience"])

  Value: Structures a common task in a format AI can consistently understand

  Everyday Content Data Types

  Email: Email structure with essential components

  Example: Email(subject="Meeting Follow-up", recipient="team", tone="professional")

  Value: Simplifies a common business task with natural parameters

  SocialPost: Content formatted for social media

  Example: SocialPost(platform="LinkedIn", topic="industry trend", hashtags=3)

  Value: Handles platform-specific considerations automatically

  Note: Quick, informal content for personal use

  Example: Note("Remember to check quarterly numbers before the meeting")

  Value: Distinguishes casual content from formal documents

  Learning Support Data Types

  Explanation: Request for concept clarification

  Example: Explanation("machine learning", level="beginner", examples=true)

  Value: Gets consistent educational content with appropriate depth

  HowTo: Step-by-step instructions for a process

  Example: HowTo("create a budget spreadsheet", detail_level="medium")

  Value: Ensures AI provides actionable, sequential steps

  Definition: Simple meaning of a term

  Example: Definition("prompt engineering", context="AI")

  Value: Quick access to clear explanations without overwhelming detail

  Communication Helper Data Types

  Tone: Writing style specification

  Example: Tone("friendly", "professional", "concise")

  Value: Simplifies one of the most common adjustments beginners make

  Audience: Target reader description

  Example: Audience("marketing professionals", knowledge_level="intermediate")

  Value: Helps AI tailor content appropriately without complex instructions

  Length: Content size specification

  Example: Length("paragraph", count=3) or Length("words", count=200)

  Value: Ensures consistent output format without technical parameters

  Why These Are Beginner-Friendly

  These data types are designed specifically for AI beginners because they:

  Use everyday language: No technical jargon or complex concepts

  Match how beginners think: Align with natural questions and requests

  Provide structure: Guide users toward successful interactions

  Focus on common tasks: Address the needs beginners have most frequently

  Reduce complexity: Hide advanced parameters behind simple interfaces

  Build transferable skills: Teach concepts that apply across all AI systems

  For a beginner, being able to say Instruction("Write a blog post").with(Topic("sustainable fashion")).using(Tone("casual", "informative")) is much more intuitive than crafting complex prompts from scratch.

  These types create a structured yet flexible way for newcomers to create effective AI interactions while gradually learning deeper prompt engineering concepts.

  Do you agree with these suggestions? Please evaluate these data types given the objectives of the CRAFT frameowrk.

  Are any of these data types likely to be confusing for either the user or the A.I.?

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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