CRAFT™️ Experiment: Gemini 2.5 Pro(Exp): Rethinking A.I. Data Types (3 of 5)

The CRAFT™️ Experiment

May 5

Written By Richard Ketelsen

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

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

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.?

Overall Assessment & Agreement:

Yes, I agree that these suggestions are highly valuable and align well with the core objectives of the CRAFT framework. They represent a thoughtful approach to making AI interactions more structured, expressive, and efficient, which is the central purpose of CRAFT.

Alignment with CRAFT Objectives:
- Structure & Reusability: Many types, like ActionTemplate, Template, UserProfile, Metric, directly promote structure and reusability, echoing the OOP principles adapted by CRAFT.
- Efficiency & Boilerplate Reduction: Types like RichContent, MediaReference, Summary, DataValidator, and Transformation effectively abstract common tasks, reducing the need for repetitive prompting or complex instructions, aligning with the goal of conserving tokens and effort.
- Expressiveness & Clarity ("Code as Conversation"): Both sets of types allow users to state their needs more clearly and concisely than typical natural language prompts. Types like Intent, Sentiment, Tone, Audience, and Length directly address the nuances of communication.
- Accessibility: The beginner-focused set (Template, Example, Question, Instruction, Email, SocialPost, Tone, Audience, etc.) strongly supports the goal of making CRAFT approachable, even for non-technical users or those new to AI.
- Practical Application: The types focus on real-world tasks (summarization, data extraction, business metrics, email/social post creation) relevant to your target audience of entrepreneurs and professionals.
- AI Understanding: The concepts underpinning most types (Intent, Entity, Sentiment, Summary, Question, Instruction) are indeed fundamental to how modern AI models process and generate information.

Evaluation of Specific Sets:

General/Advanced Data Types: These are well-suited for the core target audience ("Alex the AI Trailblazer"). They provide powerful abstractions for common AI/NLP tasks (Intent, Entity, Sentiment), data handling (Metric, TimeSeries), content manipulation (RichContent, Summary), and workflow automation (ActionTemplate, Transformation). They directly address the pain points of complexity and efficiency for experienced users.
Beginner-Friendly Data Types: This set is crucial for broadening CRAFT's appeal. They lower the barrier to entry significantly by using everyday language and focusing on common, simple tasks (Question, Instruction, Email, SocialPost). The learning/guidance types (Template, Example, Feedback) are excellent additions for onboarding. The proposed chaining syntax (e.g., Instruction(...).with(...).using(...)) is intuitive.

Potential Areas for Confusion:

While the concepts are generally clear, potential confusion could arise in implementation and usage:

For the User:
- Overlap/Granularity: Users might initially be unsure when to use Instruction vs. ActionTemplate, or Question vs. Explanation. Clear documentation defining the scope and intended use of each type will be essential.
- Syntax Complexity: While simpler than raw prompts, mastering the specific parameters and potential chaining/nesting of these types (especially the advanced ones like DialogueContext or Transformation) will have a learning curve.
- Transition: Beginners might need guidance on when and how to move from the beginner types to the more advanced ones as their needs and skills evolve.
For the AI:
- Implementation Consistency: The primary challenge isn't the AI understanding the concept (e.g., "Sentiment"), but consistently interpreting the CRAFT syntax and associated parameters (Sentiment("positive", confidence=0.87)). The exact structure, expected parameters, and behavior for each type must be rigorously defined within the CRAFT specification (CFT-FWK-SPEC-v25a1.txt).
- Distinguishing Definition vs. Usage: The AI needs to differentiate between a user defining a type (like creating an ActionTemplate) and using it (executing the action). The syntax must clearly signal this difference.
- Contextual Application: Ensuring the AI correctly incorporates context from types like UserProfile or DialogueContext across conversational turns requires careful implementation within the framework's logic.

Conclusion:

These proposed data types represent a significant enhancement to the CRAFT framework. They strongly align with its objectives and offer clear benefits for both advanced and beginner users. Addressing potential confusion will primarily involve:

Detailed Specification: Clearly defining each type, its purpose, parameters, and syntax in CFT-FWK-SPEC-v25a1.txt.
Documentation & Examples: Providing ample documentation and practical examples for users.
Refined Syntax: Ensuring the syntax is unambiguous for both the user and the AI, clearly distinguishing definition from usage.

I believe incorporating these (perhaps with some refinement based on testing) would make CRAFT substantially more powerful, usable, and aligned with its goal of structured, efficient human-AI collaboration.

Please suggest new data types that fit this objective.

Okay, building on your initial ideas and the core objectives of CRAFT (structure, efficiency, reusability, accessibility, practical application, etc. ), here are some suggestions for new data types that could further enhance the framework:

Workflow & Process Management Data Types

Workflow: Represents a defined sequence of steps or actions involving multiple CRAFT data types or instructions.
- Example: Workflow("NewBlogPost", steps=[Instruction("Research keywords for topic X"), ActionTemplate("GenerateOutline", topic="X"), Instruction("Write draft based on outline"), RevisionRequest("Review for tone and clarity")])
- Value: Formalizes multi-step processes, making complex tasks repeatable and manageable within the chat context. Aligns with structured workflows and efficiency.
DecisionPoint: Represents a conditional branch within a workflow or conversation.
- Example: DecisionPoint(trigger=Metric("CustomerSatisfaction", value=...), condition="value < 0.7", if_true=ActionTemplate("EscalateToManager"), if_false=Instruction("Send standard follow-up"))
- Value: Introduces basic logic into CRAFT interactions without complex code, allowing for more dynamic and responsive conversational flows. Supports structuring complex interactions.

Knowledge Representation & Relationships Data Types

KnowledgeGraphNode: Represents a distinct entity or concept within a potentially larger knowledge structure being built or referenced.
- Example: KnowledgeGraphNode(id="proj_alpha", type="Project", properties={"status": "Ongoing", "budget": 50000}, relationships=[("managed_by", "user_jane"), ("uses_tech", "tech_python")])
- Value: Facilitates building and referencing structured knowledge within the AI's context, supporting CRAFT's goal of "Cumulative Intelligence" and enabling more sophisticated reasoning.
Relationship: Explicitly defines a connection between two entities, data points, or CRAFT objects.
- Example: Relationship(subject=Entity("Company A", category="organization"), predicate="partnership_with", object=Entity("Company B", category="organization"), confidence=0.95)
- Value: Makes connections between pieces of information explicit, improving the AI's ability to understand context and draw inferences.

AI Configuration & Control Data Types

Persona: Defines a specific AI role, personality, or expertise level that can be applied temporarily or to specific tasks, potentially overriding the default session persona.
- Example: Persona("Critical Editor", focus=["Clarity", "Conciseness"], constraints=["Avoid jargon", "Challenge weak arguments"]) --> followed by Instruction("Review this draft").using(Persona("Critical Editor"))
- Value: Allows for more granular control over AI behavior for specific tasks, enhancing the "Human-AI Partnership" by tailoring the AI's contribution more precisely.
ConstraintSet: A named, reusable collection of rules or limitations to be applied during generation or analysis.
- Example: ConstraintSet("BrandVoiceGuidelines", rules=["Tone: professional_yet_approachable", "Must include: call_to_action", "Avoid: overly technical terms"]) --> used later with Instruction("Write ad copy").with(ConstraintSet("BrandVoiceGuidelines"))
- Value: Improves consistency and efficiency by standardizing the application of complex requirements across multiple prompts or sessions. Reduces boilerplate instructions.
OutputSchema: Specifies the desired structure, format, and data types for the AI's output, going beyond simple length or tone.
- Example: OutputSchema("CompetitorReport", fields={"competitor_name": "string", "key_product": "string", "estimated_revenue": Metric(unit="USD"), "summary": Summary(max_length=150)})
- Value: Ensures the AI generates information in a predictable, structured format suitable for direct use or further processing, enhancing reliability and structure.

Structured Data & Analysis Data Types

ComparisonMatrix: A structured format for comparing multiple items across a set of defined criteria.
- Example: ComparisonMatrix(items=["Software A", "Software B"], criteria=["Pricing", "Ease of Use", "Feature X"], data=[["$50/mo", "High", True], ["$45/mo", "Medium", False]])
- Value: Standardizes the output for comparison tasks, making it easier for users (especially entrepreneurs) to evaluate options. More structured than the beginner Comparison type.
Argument: Represents a structured claim supported by evidence and reasoning.
- Example: Argument(claim="We should invest in Solar Panels", evidence=[Metric("Energy Savings", value=15, unit="percent"), MediaReference("CaseStudy.pdf")], reasoning="Long-term cost reduction and environmental benefits outweigh initial investment.")
- Value: Useful for structuring persuasive content, documenting decisions, or analyzing proposals within a business context. Promotes clarity and structured thinking.

Collaboration & Versioning Data Types

RevisionRequest: A formal instruction to modify a specific piece of previously generated content, potentially linking to it by an ID.
- Example: RevisionRequest(target_id="blog_post_draft_1", change="Rewrite the introduction to be more engaging", rationale="Current version is too dry")
- Value: Structures the feedback and iteration loop within CRAFT, making collaboration more organized, especially in multi-session projects handled via Handoffs .
VersionTag: Associates a label or identifier with a specific state of a CRAFT object or generated content.
- Example: VersionTag(target_id="business_plan_doc", tag="v2.1-FinalReview", timestamp="2025-05-05T12:30:00Z")
- Value: Introduces basic versioning concepts, helping to track changes and manage different iterations of work within the CRAFT project context, contributing to "Cumulative Intelligence".

These suggestions aim to deepen the capabilities of CRAFT by introducing more sophisticated ways to manage workflows, represent knowledge, control AI behavior, structure data, and facilitate collaboration, all while adhering to the framework's core principles.

CRAFT™️The CRAFT™️ Experimentweek3