CRAFT™️ Experiment: ChatGPT: Rethinking A.I. Objects (2 of 5)

THE CRAFT™️ EXPERIMENT :: WEEK 6 :: POST 2

ChatGPT: Rethinking A.I. Objects (2 of 5)

Below is an outline of the key highlights related to how “objects” can be adapted within the CRAFT framework. Wherever specific object definitions or descriptions are referenced, I’ve cited the relevant file excerpts to ground each point in the existing project material.

1. Role and Purpose of Objects in CRAFT
   • Objects as High-Level Abstractions
   – In CRAFT, objects bundle related data (variables) and behaviors (functions) into cohesive units, mirroring object‐oriented patterns to organize complex AI interactions. This hybrid approach bridges coding and conversational AI, enabling modular, reusable structures rather than ad‐hoc prompt fragments.
   • Enhancing “Code as Conversation”
   – By representing instructions or content pieces as objects, CRAFT leverages familiar Python‐style constructs to make natural‐language prompts more structured. Rather than writing free‐form instructions, authors can instantiate or manipulate objects like Workflow, ChartSpec, or ConstraintSet to communicate intent clearly to the AI.

2. Core Categories of CRAFT Objects
   • Content‐Oriented Objects
   – Prompt / PromptLibrary: Encapsulate single prompt templates with placeholders or entire libraries of reusable prompts, streamlining the process of selecting and modifying prompts without rewriting text.
   – AIRecipe / AIRecipeLibrary: Represent tested, ready‐to‐use “recipes” (structured prompt sequences) for specific tasks; libraries can be iterated as objects to discover and reuse effective patterns.
   • Workflow and Decision Objects
   – Workflow: Models a named sequence of steps (e.g., Workflow("NewBlogPost", steps=[…])), letting the AI execute multi‐step processes without re‐specifying each instruction every session.
   – AIDecisionPoint: Encapsulates conditional branching logic so AI can evaluate metrics or states and choose different paths, enabling dynamic conversation flows without embedded prose “if/then” logic.
   • Knowledge and Relationship Objects
   – KnowledgeGraphNode: Represents a node or concept in a growing knowledge graph, supporting “Cumulative Intelligence” by charting entities and their properties across sessions.
   – DefinedRelationship: Explicitly models links between entities (e.g., Relationship(subject=…, predicate=…, object=…)), improving AI’s contextual reasoning about connections (e.g., partnerships, dependencies).
   • Validation and Formatting Objects
   – ConstraintSet: Groups rules or limitations (e.g., branding guidelines) that can be attached to any instruction or object, so AI enforces consistent constraints without verbose restatement.
   – OutputSchema: Declares expected output structure (fields, types), ensuring AI replies conform to predictable, machine‐readable formats (e.g., JSON‐style reports).
   – ComparisonMatrix: Standardizes tabular comparisons (items vs. criteria), enabling entrepreneurs to ask for side‐by‐side evaluations without manually formatting tables.
   • Versioning and Iteration Objects
   – RevisionRequest: Captures targeted feedback on existing content (RevisionRequest(target_id=…, change=…, rationale=…)), enforcing a structured review loop across multi‐session drafts.
   – VersionTag: Associates version labels with specific object states (e.g., documents, workflows), facilitating “Cumulative Intelligence” by tracking iterations over time.

3. Key Benefits of Using Objects in CRAFT
   • Modularity & Reusability
   – Once an object type (e.g., ChartSpec, APIRequest, ConditionLogic) is defined, it can be instantiated and reused across different prompts or sessions, reducing boilerplate and ensuring consistency.
   • Clarity & Maintainability
   – Objects make complex instructions more declarative. For example, instead of describing chart ticks and labels in prose, a ChartSpec(type="line", x="date", y="sales") communicates exactly how to render a chart. This structure is easier to maintain or revise.
   • Scalability for Multi‐Session Projects
   – By embedding stateful constructs like KnowledgeGraphNode or Workflow, CRAFT preserves context across sessions. Subsequent AI assistants can pick up where the last one left off by re‐loading object instances rather than re‐reading free‐form notes.
   • Enhanced Business Alignment
   – Objects like BusinessLogic or ComplianceRule encode higher‐level, domain‐specific rules (e.g., “if user.loyalty_years > 1, apply 10% discount”) so that AI can audit or generate workflows compliant with organizational needs.

4. Creative Adaptation Ideas for CRAFT Objects
   • Dynamic Persona Switching via AIPersona Objects
   – Define multiple AIPersona objects (e.g., Persona("TechnicalReviewer"), Persona("MarketingCopywriter")) to let AI assume different voices or expertise levels on demand. When drafting a blog post, you might call:

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   #H->AI::Instruction: (“Write the product description”).using(Persona("MarketingCopywriter"))

   This pattern makes it easy to shift tone or role without rewriting instructions .
   • Parametric Prompt Libraries through Prompt Objects
   – Create Prompt objects with placeholders (e.g., Prompt(template="Summarize the Q2 results for {company}", variables=["company"])) and store them in a PromptLibrary. At runtime, the AI can iterate over a list of companies, filling in {company} without redefining each prompt.
   • Automated Research Pipelines with Workflow + APIRequest Objects
   – Model a multi‐step research process as a Workflow("CompetitorAnalysis", steps=[APIRequest(…), ChartSpec(…), RevisionRequest(…)]). This bundles together data‐fetch, analysis, and review steps. An entrepreneur can simply call execute(Workflow("CompetitorAnalysis")), and CRAFT orchestrates each object in sequence.
   • Adaptive Decision Paths Using AIDecisionPoint + Metric Objects
   – Embed performance or sentiment metrics (e.g., Metric("customer_satisfaction", 0.65, unit="score")) into AI flows. Then use an AIDecisionPoint(trigger=Metric(...), condition="value < 0.7", if_true=ActionTemplate("InvestigateComplaint"), if_false=Instruction("ContinueStandardFollowUp")) object to branch. This introduces simple “if‐then” logic in a declarative manner.
   • Evolving Knowledge Graphs via KnowledgeGraphNode + DefinedRelationship Objects
   – As new entities emerge in a project (e.g., KnowledgeGraphNode(id="project_alpha", type="Project", properties={…})), link them with DefinedRelationship objects (Relationship(subject="project_alpha", predicate="uses_tech", object="python")). Over time, AI can traverse this graph to surface insights or suggest related tasks, driving “Cumulative Intelligence.”
   • Contextual Output Enforcement with OutputSchema + ConstraintSet
   – When generating a financial summary, attach an OutputSchema("FinancialReport", fields={…}) plus ConstraintSet("ExecutiveBriefGuidelines", rules=[…]). This tells AI exactly how to format the output (e.g., JSON keys, section headings) while enforcing tone and length constraints.

5. Practical Steps to Define New Custom Object Types
   • Identify Repeating Patterns or Complex Tasks
   – Look for any sequence or data structure you keep rewriting in prose (e.g., a legal‐compliance check, a multi‐channel campaign). These are candidates for new object types.
   • Draft a Declarative Data Model
   – Define the object’s attributes and expected behavior. For example, a RiskAssessment object might have fields name, score_metric, thresholds, and methods like evaluate() or recommend_mitigation().
   • Embed Example Usage in CRAFT Spec
   – Add code snippets to the CRAFT specification file showing how to instantiate and use the object. For instance:

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   RiskAssessment( name="SiteBreachRisk", score_metric=Metric("vulnerability_score", value=0.4), thresholds={“high”: 0.7, “low”: 0.3} )

   Include at least one START PYTHON CODE BLOCK snippet in the spec.
   • Document Expected AI Behavior
   – Under “3.1 DATA TYPE DESCRIPTIONS,” write a concise definition and example so that any AI assistant can parse it without extra instructions. E.g.:

   RiskAssessment: Encapsulates a risk evaluation process by comparing a metric against thresholds; returns a risk level and recommended actions. – Example: RiskAssessment(…)
   • Test and Iterate
   – Use a sample chat to create instances of the new object and verify the AI responds correctly (e.g., if you call RiskAssessment(...), AI should evaluate and return a result). Refine until behavior is consistent.

6. Summary of Key Takeaways

7. Objects in CRAFT shift free‐form prompts into structured, reusable modules, reducing token usage and improving maintainability.

8. Existing Object Types (e.g., Workflow, AIDecisionPoint, KnowledgeGraphNode, ConstraintSet, RevisionRequest) already cover a broad spectrum of needs—workflow orchestration, decision logic, knowledge capture, versioning, and constraints.

9. Creative Adaptation involves layering these objects to form dynamic, multi‐session pipelines (e.g., calling a Workflow containing APIRequest, ChartSpec, and RevisionRequest objects for an end‐to‐end process).

10. Extending CRAFT with new objects means identifying recurring patterns, defining a clear schema, embedding examples in the spec, and iterating until AI consistently interprets and acts on them.

This outline should serve as a roadmap for exploring and expanding how object‐oriented concepts can power more structured, scalable AI conversations under the CRAFT framework. Each section draws on existing definitions and examples from the CRAFT specification files (especially and ) so you can see exactly where these ideas live in the current project.

Sources

You said:

Great, please expand on Role and Purpose of Objects in CRAFT

ChatGPT said:

Thought for 16 seconds

Below is a deeper look at how objects function within CRAFT—why they exist, what problems they solve, and how they reshape the way we “talk” to AI. Wherever possible, you’ll see citations back to the CRAFT specification files so you can trace the ideas directly to the source.

Why Objects Matter in CRAFT

At its core, CRAFT was invented to bridge the gap between conversational AI and software‐style rigor. Rather than treating each prompt as free‐form text, objects let us package related data and behavior into single, reusable units. This solves two big headaches:

1. Preventing Repetition & Drift
   When you rely solely on ad‐hoc prompts—paragraph after paragraph of instructions—you inevitably end up retyping versions of the same idea (e.g., “Always follow the brand voice,” “Use these formatting rules,” etc.). Over time, slight wording changes can cause the AI to drift off course. Objects bundle all the relevant rules, data, and methods into one place so you only define them once.

2. Maintaining Context Across Sessions
   Imagine you ask AI to research competitors today, then pick up again next week to write a summary. Without objects, you’d need to remind the AI of every detail (metrics, sources, past decisions). With objects like Workflow or KnowledgeGraphNode, the AI can reload a structured snapshot of the previous session’s state—no manual “recap” needed.

To borrow a household analogy, think of an object like a labeled folder in a filing cabinet, whereas a free‐form prompt is more like a loose stack of papers. Once you put all related documents in one labeled folder, anyone (or any future AI session) can grab that folder and immediately know where everything is. That’s exactly why CRAFT objects exist: to keep all the “attachments” and “instructions” neatly bundled so you don’t lose track.

Objects as High-Level Abstractions

Bundling Data + Behavior

In traditional software, an object might be a Customer class with fields (name, email) and methods (send_invoice()). CRAFT adopts this same idea for AI prompts:

• Data: Variables or parameters that an object holds (e.g., a Metric object might store name="conversion_rate", value=3.2, unit="percent").

• Behavior: Implicit conventions or instructions tied to that data (e.g., if you ask the AI to “evaluate” a Metric object, it knows to compare it against targets and return an analysis rather than treating it as free text).

By grouping related concepts (data) with the “rules” for handling them (behavior), objects become self-describing modules. In practice, when you write:

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Metric("customer_satisfaction", 0.65, unit="score")

you’re not just conveying “customer_satisfaction is 0.65.” You’re implicitly telling the AI: “Here’s a KPI; evaluate it against benchmarks, check for red flags, and format your response as a concise report.” That shared understanding saves hundreds of tokens in explanatory text—and minimizes misunderstandings.

The Transition from Text to Structure

Ordinarily, if you wanted AI to generate a chart, you might write:

“Plot a line chart showing monthly sales over the past year. X-axis is date, Y-axis is revenue. Title it ‘Monthly Sales.’ Use a clean, minimal style and label each axis clearly.”

With a ChartSpec object, you replace that entire paragraph with something like:

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ChartSpec(type="line", x="date", y="revenue", title="Monthly Sales")

The AI already “knows” how to interpret a ChartSpec to produce a chart, so you don’t have to spell out every detail. This doesn’t just cut down on tokens—it enforces consistency (every chart created via ChartSpec follows the same conventions) and makes prompts far easier to maintain when requirements change.

Enhancing “Code as Conversation”

Declaring Intent in Python-Style

One of CRAFT’s guiding philosophies is “Code as Conversation.” Rather than writing free-form instructions that the AI must interpret on the fly, you lean on Python-compatible syntax to make intent explicit. For example, instead of:

“Review this write-up and make it friendlier, with shorter sentences.”

You use:

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RevisionRequest(target_id="draft_001", change="Rewrite for a friendly tone with shorter sentences", rationale="Improve readability for lay audience")

This accomplishes two things simultaneously:

1. Clarity: The AI sees a clear RevisionRequest object with exactly three fields—target_id, change, and rationale—so there’s no ambiguity about what you want done or why.

2. Reusability: Once you define the RevisionRequest data type, you can spin up as many revision requests as needed without re-explaining the structure. Moreover, if you ever want to change how a “revision” is handled globally (for instance, adding an automatic style check), you only update the RevisionRequest handler in your CRAFT spec, not every prompt.

When AI sees that object, it knows to treat it as an instruction to update existing content identified by "draft_001". This is a drastic shift from asking the AI to parse a paragraph of text instructions and figure out what to do—objects let you speak in a defined “dialect” that AI tools already understand.

Building Multi-Step Processes Naturally

Think about a multi-step task—say, launching a new product. In a free-form chat, you might end up writing something like:

1. “First, draft a product description.”

2. “Now analyze competitors’ positioning and summarize the differences.”

3. “Then create a Go-to-Market outline.”

4. “Finally, compile everything into a single PDF deck.”

Each step would require the AI to parse and remember the previous context. With CRAFT objects, you instead define a Workflow object:

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Workflow( name="ProductLaunchPlan", steps=[ AIInstruction("Draft product description"), AIInstruction("Analyze competitors and summarize"), AIInstruction("Create Go-to-Market outline"), AIActionTemplate("Compile presentation", format="PDF") ] )

By presenting that single Workflow object, you’re telling the AI: “Here are four discrete tasks. Execute them in order, pass outputs from one to the next, and return the final deliverable.” Because CRAFT’s engine already knows how to interpret Workflow and its constituent objects, you don’t need to restate the sequence or dependencies.

From a user’s perspective, this is like giving someone a recipe card with numbered steps rather than an essay describing how to cook. Recipes are concise, unambiguous, and easy to follow; objects turn AI prompts into that same familiar pattern.

Grounding Objects in CRAFT Philosophy

CRAFT’s designers intentionally chose Python-compatible syntax because it’s both human-readable and machine-friendly. Let’s connect the dots back to the six guiding principles:

1. Structure Enables Freedom
   When you offload repetition and nuance into objects, you free up “creative headspace” to focus on innovation rather than logistics. You no longer need to remember if you asked AI to “always use Oxford commas”—you embed that rule in a ConstraintSet object once and for all.

2. Code as Conversation
   Objects are literally code structures—Python classes, data types, and methods—that the AI recognizes as “instructions” rather than free-text. By thinking in objects, you turn every AI chat into a mini-coding environment where the conversation itself is structured code.

3. Cumulative Intelligence
   Because objects can be serialized (converted into JSON or Python dictionaries) and passed between sessions, they preserve state. A KnowledgeGraphNode created in session one can be revisited in session seven without re-explaining its properties—thus steadily building a shared knowledge base.

4. Human-AI Partnership
   By shifting low-value grunt work (restating boilerplate instructions) into object definitions, humans and AI collaborate more synergistically. You define the high-level strategy; the AI executes as if running your code. That’s true partnership—humans set intent, AI carries it out.

5. Accessibility Through Familiarity
   Many entrepreneurs and content creators already know basic Python syntax. Even if you’re not a coder, objects like AIPersona("MarketingCopywriter") read like pseudo-English: “Use the AI persona that writes marketing copy.” You don’t need to invent a new prompt-language grammar; CRAFT uses Python-style names and conventions you already recognize.

6. Conservation of Tokens
   Every time you replace a multi-sentence instruction with a single object instantiation, you save tokens—and tokens are money. Instead of spending 50 tokens describing how to format a table, you write ComparisonMatrix(...), and the AI knows exactly how to render it.

Summary of “Role and Purpose of Objects in CRAFT”

1. High-Level Abstractions: Objects bundle related data and behaviors into one unit, preventing repetition and simplifying maintenance (e.g., Metric, RevisionRequest, ChartSpec).

2. Code-Like Conversation: By using Python-style syntax and predefined object types, prompts become declarative code rather than free-form text. This makes AI’s job clearer and responses more consistent (e.g., Workflow, ConstraintSet).

3. Modular & Reusable: Once an object type is defined, you can instantiate it anywhere without rewriting its schema. Changing the core logic of a workflow or rule only requires updating the object definition in the CRAFT spec—every prompt referencing that object automatically inherits the change.

4. Context Persistence: Objects like KnowledgeGraphNode and VersionTag let you hand off state from one session to the next, creating cumulative knowledge rather than isolated one-off chats.

5. Business Alignment: Complex business logic and compliance rules can be encoded as objects (BusinessLogic, ComplianceRule) so AI can automatically audit or enforce them—no need for manual checks in every prompt.

By embracing objects, CRAFT transforms AI chats from scattered instructions into a structured, code-driven workflow. You end up with a repeatable, maintainable, and efficient system—much like software development—while still leveraging the natural-language strengths of conversational AI.

Sources

You said:

Great, please expand on Core Categories of CRAFT Objects

ChatGPT said:

Thought for 9 seconds

Below is a detailed expansion of the Core Categories of CRAFT Objects, drawn directly from the framework specification. Each category bundles together related data types and behaviors, making it easier to structure AI interactions. After each section, you’ll find a brief summary to reinforce the main takeaways.

1. Content-Oriented Objects

Purpose & Rationale

Content-Oriented Objects encapsulate the building blocks you’d use when working on any AI-driven content task—blog posts, social media updates, reports, or prompt recipes. Rather than embedding every detail in free-form text, these objects let you declare “I need a prompt template,” “I want to store this prompt,” or “I’m creating a reusable recipe.” In practice, this modularizes and speeds up content creation, since you don’t have to retype or rediscover your favorite prompt structures each time.

Key Object Types

1. Prompt / PromptLibrary

   • Prompt: A template with variables that can be filled in at runtime.

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     Prompt( template="Summarize the quarterly earnings for {company_name}.", variables=["company_name"] )

     By using a Prompt object, you avoid rewriting “Summarize the quarterly earnings for XYZ” over and over. Instead, you instantiate the same template with different company names.

   • PromptLibrary: A collection of Prompt objects, often organized by category (e.g., “research prompts,” “SEO prompts,” “email prompts”).

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     PromptLibrary( name="SEO_Prompts", prompts=[ Prompt(template="Generate meta description for {article_title}", variables=["article_title"]), Prompt(template="List top keywords for {topic}", variables=["topic"]) ] )

     Rather than searching through old chats or documents for that “perfect” SEO prompt, you load the library and reference it directly.

2. AIRecipe / AIRecipeLibrary

   • AIRecipe: Represents a tried-and-true sequence of prompts (a “recipe”) that accomplishes a specific task—say, “audit website accessibility.” Each recipe can reference other CRAFT objects (variables, workflows, etc.).

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     AIRecipe( name="AccessibilityAudit", steps=[ AIInstruction("List common accessibility issues for {page_url}"), AIInstruction("Generate a prioritized action plan to fix those issues"), RevisionRequest(target_id="audit_plan", change="Ensure steps follow WCAG guidelines", rationale="Compliance requirement") ] )

     Once you’ve proven that “AccessibilityAudit” works reliably, you store it as an AIRecipe and reuse it without re-engineering.

   • AIRecipeLibrary: A repository of multiple AIRecipe objects, helping entrepreneurs quickly browse and leverage best practices—everything from “write a press release” to “generate investor‐pitch bullet points.”

3. AIActionTemplate

   • Think of this as a partially completed prompt blueprint for a specific action (e.g., “email the customer”), with placeholders for parameters.

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     AIActionTemplate( name="EmailCustomer", parameters=["recipient_name", "product_issue"], template="Write an email to {recipient_name} apologizing for the {product_issue} and offering a resolution." )

     When you need to send a similar email, you simply call AIActionTemplate("EmailCustomer", recipient_name="Alex", product_issue="delivery delay") and pass it to the AI.

4. Conversational / Social Media Objects

   • SocialPost: Automatically formats a piece of content for social media—factoring in character limits, hashtags, and tone.

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     SocialPost( platform="LinkedIn", topic="AI in Cybersecurity", detail_level="concise" )

   • RichContent: Wraps formatted text, links, or multimedia references so AI knows to preserve styling (bold, italics, hyperlinks). This object helps you seamlessly create blog or article drafts without worrying about markdown syntax.

5. Reusable Snippet Objects

   • CodeSnippet: When you need the AI to analyze or modify code, you enclose it in a CodeSnippet so the AI can treat it as code rather than prose.

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     CodeSnippet( language="python", content="def calculate_roi(cost, revenue): return (revenue - cost) / cost" )

     This ensures the AI doesn’t accidentally “translate” your code into plain English or reformat it incorrectly.

Everyday Analogy

Imagine you’re a chef:

• A Prompt is like your favorite pancake recipe (with blanks for “type of flour” or “sweetener”).

• A PromptLibrary is your entire cookbook of pancake variations.

• An AIRecipe is a multi‐course dinner menu—each “step” points to a different recipe.

• And an AIActionTemplate is a partially written dinner invite that you can quickly complete with names and dates.

Using these objects, you can whip up a consistent breakfast or an entire banquet without rewriting recipes from scratch each time.

Summary

• Prompt/PromptLibrary and AIRecipe/AIRecipeLibrary bundle content templates into one place, eliminating repetition.

• AIActionTemplate streamlines recurring actions (e.g., “send email,” “generate outline”) into reusable blocks.

• SocialPost, RichContent, and CodeSnippet focus on preserving formatting and structure, so you don’t worry about markdown or code syntax.

2. Workflow & Decision Objects

Purpose & Rationale

Many entrepreneurial tasks are rarely one-and-done—they involve multiple decision points, conditional branches, and handoffs. Workflow and AIDecisionPoint objects give you a way to encode those multi-step processes declaratively. Instead of writing “First do this, then do that, if X occurs do Y,” you wrap everything in structured objects that the AI can interpret and execute.

Key Object Types

1. Workflow

   • Definition: Represents a named sequence of steps or actions, each of which can be another CRAFT object (e.g., AIInstruction, APIRequest, RevisionRequest).

   • Example:

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     Workflow( name="NewBlogPost", steps=[ AIInstruction("Research keywords for topic {topic}"), AIActionTemplate("GenerateOutline", topic="{topic}"), AIInstruction("Write draft based on outline"), RevisionRequest( target_id="draft_{topic}", change="Adjust tone to be more conversational", rationale="Target audience is non-technical entrepreneurs" ) ] )

     Once you call Workflow("NewBlogPost"), the AI knows to execute each step in order—passing outputs along the chain.

   • Why This Matters: Entrepreneurs can define a “content‐creation pipeline” once and then reuse it for multiple topics. It’s like setting up a production line—each step is clearly defined, and the AI worker knows exactly what to do next.

2. AIDecisionPoint

   • Definition: Encapsulates conditional logic within a workflow. Instead of embedding “if…then…” statements in prose, you declare a trigger metric and specify branches.

   • Example:

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     AIDecisionPoint( trigger=Metric("CustomerSatisfaction", value=site_feedback_score, unit="score"), condition="value < 0.7", if_true=AIActionTemplate("InitiateEscalation", department="Support"), if_false=Instruction("SendThankYouEmail", template="StandardThankYou") )

     When the AI processes this, it evaluates the Metric—if the score is below 0.7, it executes InitiateEscalation; otherwise, it sends a thank-you email.

   • Why This Matters: You no longer need to write out long “if X happens, do Y” instructions in paragraphs. Everything is wrapped in a single, readable object that the AI can parse directly—reducing misunderstandings and ensuring consistent branching.

3. APIRequest & APIResponse (closely tied to workflow)

   • APIRequest: When part of your workflow involves fetching data (e.g., competitor pricing, weather data, or social-media metrics), you use an APIRequest object.

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     APIRequest( endpoint="/v1/competitors", method="GET", params={"industry": "cybersecurity", "region": "US"} )

   • APIResponse: Once the AI receives that data, you wrap the response in APIResponse so it can parse status codes and payloads without misinterpreting them as free text.

Everyday Analogy

Imagine you’re assembling IKEA furniture:

• A Workflow is like the instruction booklet that says, “Step 1: Attach leg A to shelf B. Step 2: Use screw X. Step 3: Tighten until flush.”

• An AIDecisionPoint is like a note in the manual that says, “If you have Part C instead of Part D, use the alternate bracket.”

• APIRequest/APIResponse is like ordering replacement parts—you place the order and then handle the delivered items differently depending on whether they’re correct (200 OK) or need to be exchanged (404 or 500).

By encapsulating everything in these objects, you minimize fumbling around figuring out “what comes next” and reduce the chance of assembling it backward.

Summary

• Workflow bundles multi-step processes into one unit.

• AIDecisionPoint encodes conditional branching so your AI interaction can adapt dynamically.

• APIRequest/APIResponse integrate external data calls directly into your workflow, eliminating separate instructions.

3. Knowledge & Relationship Objects

Purpose & Rationale

CRAFT’s philosophy of “Cumulative Intelligence” means each session should build on the last, gradually forming a richer, shared knowledge base. Knowledge & Relationship Objects allow you to explicitly track entities and how they connect—rather than hoping the AI “remembers” something from weeks ago.

Key Object Types

1. KnowledgeGraphNode

   • Definition: Represents a distinct entity or concept (e.g., a project, a competitor, a product) with associated properties. You can link nodes to one another, forming a mini-knowledge graph.

   • Example:

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     KnowledgeGraphNode( id="proj_alpha", type="Project", properties={"status": "Ongoing", "budget": 50000}, relationships=[("managed_by", "user_jane"), ("uses_tech", "python")] )

     Now, if you later ask “Which projects use Python?” the AI can traverse that KnowledgeGraphNode instead of re-reading old chat logs.

   • Why This Matters: Entrepreneurs often juggle multiple projects, vendors, or products. By capturing each as a node, you create a self-documenting “map” of your domain—so you only define “proj_alpha” once and refer to it everywhere.

2. DefinedRelationship

   • Definition: Explicitly models the link between two entities (subjects and objects), along with an optional confidence score.

   • Example:

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     DefinedRelationship( subject=Entity("Company A", category="organization"), predicate="partnership_with", object=Entity("Company B", category="organization"), confidence=0.95 )

     The AI now knows there’s a high-confidence partnership between Company A and Company B—making it easier to ask “List all partnerships for Company A.”

   • Why This Matters: When you’re comparing competitors, suppliers, or collaborators, relationships matter. Rather than re-explaining “Company A works with Company B,” you declare it once and reference it programmatically.

Everyday Analogy

Imagine building a family tree:

• Each KnowledgeGraphNode is like a person’s record (name, birthdate, interests).

• A DefinedRelationship is like “Alice is Bob’s sibling” or “Charlie is Alice’s father.”
  Once that family tree is in place, you can answer questions like “Who are Alice’s cousins?” without rehashing the entire lineage in each description.

Summary

• KnowledgeGraphNode lets you capture entities and their properties once.

• DefinedRelationship maps explicit links between those entities, boosting context and reasoning.

4. Validation & Formatting Objects

Purpose & Rationale

When you hand an AI a complex task—say, “Generate a compliance report in JSON”—it’s easy for the output to wander off spec. Validation & Formatting Objects define the rules and expected structure so that, instead of policing AI outputs manually, you embed the guardrails directly in your instructions.

Key Object Types

1. ConstraintSet

   • Definition: A named, reusable collection of rules (e.g., tone, length, must-include/avoid terms). Once defined, you can attach a ConstraintSet to any instruction or object, and the AI will enforce those rules consistently.

   • Example:

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     ConstraintSet( name="BrandVoiceGuidelines", rules=[ "Tone: professional_yet_approachable", "Must include: call_to_action", "Avoid: overly technical terms" ] )

     Later, if you ask AIInstruction("Write ad copy for product X").with(ConstraintSet("BrandVoiceGuidelines")), AI knows exactly which guardrails to apply.

   • Why This Matters: Entrepreneurs often adhere to brand or regulatory guidelines. Embedding these rules into ConstraintSet ensures every AI output aligns with company policy—without rewriting guidelines every single time.

2. OutputSchema

   • Definition: Specifies the exact fields, data types, and format for AI’s response. This goes beyond “make it bullet points”—you can demand a JSON object with keys for “competitor_name,” “market_share,” “growth_rate,” etc.

   • Example:

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     OutputSchema( name="CompetitorReport", fields={ "competitor_name": "string", "key_product": "string", "estimated_revenue": Metric(unit="USD"), "summary": Summary(max_length=150) } )

     Now, AIInstruction("Generate a competitor report for {company}").with(OutputSchema("CompetitorReport")) guarantees you get a machine-parseable JSON—not a free-form paragraph.

   • Why This Matters: Whether you’re feeding AI outputs into a dashboard, a spreadsheet, or another system, you need predictable structure. OutputSchema enforces that.

3. ComparisonMatrix

   • Definition: A structured object for side-by-side comparisons. Entrepreneurs frequently evaluate options—software tools, vendors, pricing tiers. Instead of instructing AI to “layout a comparison table,” you give it a ComparisonMatrix object.

   • Example:

     python

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     ComparisonMatrix( items=["Software A", "Software B"], criteria=["Pricing", "Ease of Use", "Feature Set"], data=[ ["$50/mo", "High", True], ["$45/mo", "Medium", False] ] )

     You no longer have to tell AI “Make columns, label them X, Y, Z.” The ComparisonMatrix “knows” you want a 2×3 table with specific headers.

   • Why This Matters: It drastically reduces back-and-forth when you need a clear, consistent format for evaluations—saving time and tokens.

4. Form & MultipleChoice

   • Form: Declares a set of input fields (e.g., email, age, feedback). The AI can generate a form interface or validate entries without you writing validation logic in prose.

     python

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     Form( fields=[ {"name": "email", "type": "string", "required": True}, {"name": "age", "type": "integer", "required": False} ] )

   • MultipleChoice: Models multiple-choice questions so AI can render options and collect responses cleanly.

     python

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     MultipleChoice( question="Preferred contact method?", options=["email", "phone", "sms"] )

   • Why This Matters: When you need user input—surveys, polls, or data collection—these objects simplify the process, taking validation and formatting off your plate.

Everyday Analogy

Think of ConstraintSet as a “style guide” folder on your desktop—once you save it, every document you produce uses the same fonts, margins, and branding without retyping the instructions.
OutputSchema is like the template your accountant uses to submit quarterly figures—it has defined fields for “Revenue,” “Expenses,” “Profit,” etc., so you never have missing data.
A ComparisonMatrix is analogous to a spreadsheet you’d set up to compare two business proposals side by side, complete with rows for “Cost,” “Timeline,” and “ROI.”

Summary

• ConstraintSet ensures every AI output follows predefined rules—tone, mandatory terms, forbidden terms.

• OutputSchema guarantees structured, machine-readable responses (JSON, CSV), making downstream processing seamless.

• ComparisonMatrix provides a standardized way to generate comparison tables, saving formatting hassles.

• Form/MultipleChoice handle user input cleanly, enabling AI to validate and format responses automatically.

5. Versioning & Iteration Objects

Purpose & Rationale

Good projects evolve. As you refine strategies, iterate on drafts, or tweak workflows, you need a way to track and reference past states so you can roll back if needed or compare versions side by side. RevisionRequest and VersionTag give you that version control, directly in your AI chats.

Key Object Types

1. RevisionRequest

   • Definition: A structured instruction to modify a specific piece of content you’ve already generated. It references the content by an identifier (target_id), states what needs to change, and provides a rationale.

   • Example:

     python

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     RevisionRequest( target_id="blog_post_draft_1", change="Rewrite the introduction to be more engaging", rationale="Current version is too dry for our audience" )

     Once you send this, the AI knows exactly which draft to update and why—no need to paste paragraphs or re-explain.

   • Why This Matters: In multi-session projects, you can request revisions without recontextualizing everything. You simply say, “Revise X,” and the AI picks up the content from its stored state.

2. VersionTag

   • Definition: Assigns a version label (and an optional timestamp) to any CRAFT object or piece of content. This makes it easy to refer back to “v1.0” or “v2.1-FinalReview” without confusion.

   • Example:

     python

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     VersionTag( target_id="business_plan_doc", tag="v2.1-FinalReview", timestamp="2025-05-05T12:30:00Z" )

     Later, if you need to compare “v2.0” and “v2.1,” the AI can fetch both states and show you the diffs—much like Git but in natural language.

   • Why This Matters: Entrepreneurs working on evolving documents—business plans, pitch decks, brand guidelines—can keep a clean history and avoid accidentally building on outdated content.

Everyday Analogy

This is analogous to version control in software (Git tags) or the “Track Changes” feature in Word:

• A RevisionRequest is like inserting a comment in Word that says, “Rewrite paragraph 2 to sound friendlier.”

• A VersionTag is like saving the document as “BusinessPlan_v2.1” so you always know which iteration you’re looking at.

Summary

• RevisionRequest organizes feedback and iteration loops, letting AI update content directly by reference.

• VersionTag provides explicit labels for object states, facilitating side-by-side comparisons and rollbacks.

6. Extended & Specialized Objects

While the above four categories cover a majority of common use cases, CRAFT also includes specialized objects that handle more nuanced tasks. Below are a few noteworthy examples:

1. Metric & TimeSeries

   • Metric: Stores a named value (e.g., “conversion_rate”) with units, trends, and context—standardizing how you track KPIs.

     python

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

     Whenever you ask AI to “evaluate our KPIs,” you pass a list of Metric objects rather than embedding numbers in text.

   • TimeSeries: Encapsulates a sequence of data points over time, which AI can use to generate trend analyses or forecasts.

     python

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     TimeSeries( data=[{"date": "2025-04-01", "value": 120}, {"date": "2025-05-01", "value": 135}], metric="sales" )

     This object tells AI, “Here’s your sales history—plot it, analyze seasonality, or forecast next month.”

2. BusinessLogic & ComplianceRule

   • BusinessLogic: Encodes company-specific decision rules (e.g., “if loyalty_years > 1, apply 10% discount”).

     python

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     BusinessLogic( name="DiscountEligibility", condition="user.loyalty_years > 1", action="apply_discount(10%)" )

     Rather than describing your discount policy in a paragraph, you give AI a formal rule to follow.

   • ComplianceRule: Captures regulatory guidelines (GDPR, HIPAA, etc.) so AI can flag or handle potential compliance issues.

     python

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     ComplianceRule( regulation="GDPR", requirement="user_consent_required", action="log_consent()" )

     This ensures that whenever AI processes personal data, it automatically enforces “log_consent()” if needed—no manual reminders.

3. Checklist, Form, MultipleChoice (overview)

   • Checklist: Turns a to-do list into a structured object that AI can mark “complete” or “pending,” then remind you if any items are left.

   • Form / MultipleChoice: As discussed earlier, handles user input with built-in validation and formatting.

4. StructuredArgument

   • Definition: Packages a claim supported by evidence and reasoning. This is particularly useful for building persuasive business cases or investor decks.

     python

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     StructuredArgument( claim="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." )

     AI can process this to produce a well-structured, logical argument rather than a meandering essay.

Everyday Analogy

Think of these specialized objects as the “power tools” in your entrepreneur’s toolkit:

• Metric and TimeSeries are like your digital dashboard, updating you on KPIs in real time.

• BusinessLogic and ComplianceRule are your company’s policy manuals—AI simply follows the instructions without you having to repeat them.

• StructuredArgument is like your “elevator pitch” template—concise, evidence-backed, and ready for investors.

Summary

• Metric/TimeSeries turn raw numbers into standardized data objects for trend analysis.

• BusinessLogic/ComplianceRule codify company rules and regulations, so AI enforces them automatically.

• StructuredArgument ensures persuasive content follows a clear, evidence-backed structure.

Overall Summary of Core Categories

1. Content-Oriented Objects (Prompt, PromptLibrary, AIRecipe, AIActionTemplate, SocialPost, RichContent, CodeSnippet) let you package content templates into reusable, easy-to-reference pieces.

2. Workflow & Decision Objects (Workflow, AIDecisionPoint, APIRequest/APIResponse) transform multi-step tasks and conditional branches into single, declarative constructs.

3. Knowledge & Relationship Objects (KnowledgeGraphNode, DefinedRelationship) build a persistent, queryable knowledge graph that AI can traverse for richer, multi-session reasoning.

4. Validation & Formatting Objects (ConstraintSet, OutputSchema, ComparisonMatrix, Form, MultipleChoice) embed rules and expected structures directly into your prompts, ensuring consistent, machine-readable outputs.

5. Versioning & Iteration Objects (RevisionRequest, VersionTag) give you built-in version control, so you can iterate on drafts without losing track of changes.

6. Extended & Specialized Objects (Metric, TimeSeries, BusinessLogic, ComplianceRule, StructuredArgument, Checklist, etc.) cover niche but high-value use cases—KPIs, compliance, persuasive arguments, user input, and more.

By organizing your AI interactions around these categories, you create a system that is:

• Modular: Build once, reuse everywhere.

• Maintainable: Change object definitions in one place, and every prompt that references it automatically updates.

• Scalable: As your prompt library and project complexity grow, you avoid “prompt spaghetti” and keep things tidy.

• Context-Rich: Multi-session projects become easier because objects like KnowledgeGraphNode and VersionTag persist state.

Use this breakdown as a reference when designing new CRAFT objects, and lean on these categories to ensure your AI‐powered workflows remain consistent, efficient, and aligned with entrepreneurial needs.

Sources

You said:

Great, please expand on Key Benefits of Using Objects in CRAFT

ChatGPT said:

Thought for 9 seconds

Token Usage: Free-text vs Object-based Prompts

Below is an in-depth exploration of the Key Benefits of Using Objects in CRAFT, with detailed explanations, relatable analogies, and a summary at the end. Afterward, you’ll see a bar chart comparing token usage between free-form prompts and object-based prompts—illustrating one of the biggest practical upsides.

1. Modularity & Reusability

Detailed Explanation
When you encapsulate related data and behavior into an object (for example, a Prompt, Workflow, or Metric), you create a self-contained unit that can be used anywhere without rewriting it. This modularity means:

• Single Source of Truth: Define your prompt template once (e.g., Prompt(template="Summarize {topic}")) and instantiate it with different topics. If you later tweak the template—say, adding “Focus on key takeaways”—you update that one object definition and every invocation automatically inherits the change.

• Reduced Boilerplate: Instead of copy‐pasting nearly identical instructions across dozens of chats (“Summarize the quarterly report,” “Summarize the annual review,” etc.), you create a PromptLibrary that stores them all in one place. CRAFT objects can be parameterized so you fill in only what’s unique each time.

• Plug-and-Play: Suppose you build a complex multi-step Workflow("NewProductLaunch") that includes market research, competitor analysis, and a rollout plan. Next week, to launch a different product, you simply instantiate that same workflow object with new parameters. You don’t have to rebuild the sequence from scratch.

Everyday Analogy
Think of LEGO bricks:

• A single brick type (e.g., a 2×4 black brick) can be used in countless builds.

• If you want to change that brick’s color or shape, you replace it in the LEGO set, and all models referencing it update at once (in CRAFT, you’d modify the object definition).

• Rather than gluing paper cutouts together each time you build something (free-form prompts), you click bricks together—modular, interchangeable, and easy to manage.

Short Summary

• Objects let you define once and reuse everywhere.

• They eliminate repetitive typing, ensure consistency, and accelerate prompt‐building.

2. Clarity & Maintainability

Detailed Explanation
When you wrap instructions in a named object, you replace long, meandering prose with a clear declaration. For example:

Free-Form Prompt:
“Please analyze the social media engagement data for the last quarter—look at likes, shares, comments, and identify any spikes or sudden drops. Then suggest three actionable recommendations to improve engagement, focusing on content types, posting times, and platform-specific tactics, making sure to write in a friendly but professional tone that matches our brand guidelines.”

Versus:

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AIInstruction( action="AnalyzeEngagement", data_source="social_media_Q2", metrics=["likes", "shares", "comments"], output_format="spike_trough_report", recommendations_count=3, tone="friendly_professional", constraints=ConstraintSet("BrandVoiceGuidelines") )

Benefits of the object version:

1. Unambiguous Fields: Every field has a clear label (action, data_source, metrics, etc.). No guesswork about what “analyze” entails.

2. Consistent Interpretation: CRAFT’s engine already understands how to process AIInstruction(action="AnalyzeEngagement", ...)—so you avoid AI “hallucinations” or misinterpretations that can happen with plain‐text.

3. Easier Maintenance: If you want to switch the tone to “formal_informative,” you just change the tone value. You don’t need to rewrite sentences to convey formality.

4. Better Documentation: Because the object’s fields are self-explanatory, anyone (or any AI assistant) reading it next week can instantly know what each parameter does.

Everyday Analogy
Compare writing instructions on a sticky note versus filling out a standardized form:

• Sticky Note (Free-Form): “When you arrive, buzz me, I’ll get the files from the cabinet, check them for errors, and then email the revised version by 2 PM.” If you read that next month, you might forget which cabinet, what “check for errors” means exactly, or which email to use.

• Form (Object):

  • Task: “RetrieveFiles”

  • Location: “Cabinet A, Drawer 2”

  • Operation: “ValidateSpelling, GrammarCheck”

  • Output: “SendToEmail(user@example.com) by 14:00”
    You never wonder about missing details, because the form fields force you to be explicit.

Short Summary

• Objects replace ambiguous paragraphs with clear, labeled fields.

• This ensures AI interprets your intent precisely and makes updates trivial.

3. Scalability & Context Persistence

Detailed Explanation
When projects or conversations grow, free-form prompts can’t reliably “remember” past context. Objects solve this by persisting state in machine-readable form:

• Stateful Objects:

  • A KnowledgeGraphNode("proj_alpha") created in session one is still recognized in session ten. The AI doesn’t need you to re‐explain “proj_alpha is our upcoming cybersecurity dashboard”; the object’s properties carry that knowledge forward.

  • A VersionTag(target_id="proj_alpha_doc", tag="v3.0") lets you recall exactly which draft to modify later, without copying/pasting entire documents.

• Large-Scale Workflows:

  • Suppose you have a 15-step Workflow("AnnualReport"). As your team works on sections across multiple weeks, you don’t rewrite “Next, perform competitor analysis” each time. The AI reads the Workflow object, sees the next incomplete step, and continues seamlessly.

• Parallel Projects:

  • If you run several product launches simultaneously—Workflow("ProductA_Launch"), Workflow("ProductB_Launch"), Workflow("ProductC_Launch")—each object keeps its own context. AI doesn’t mix them up because each object has a unique identifier and set of parameters.

Because objects persist state across sessions, you’re effectively building a shared “project memory” rather than starting from scratch every time.

Everyday Analogy
Think of tracking multiple ongoing home renovations:

• Without Objects (Free-Form): You write sticky notes like “Call plumber,” “Buy paint,” “Schedule electrician.” But after a few weeks, you lose track of which note goes with which room. If you go back to it, you have to re‐read every sticky note.

• With Objects: You have a digital project management tool where each room is a “Task Object” with fields:

  • Task("PaintMasterBedroom", status="InProgress", next_step="Choose color samples")

  • Task("InstallBathroomFixtures", status="PendingInspection", next_step="Confirm water pressure")
    Everything is organized, each task remembers its state, and you can pick up where you left off instantly.

Short Summary

• Objects hold state so AI can reconnect to ongoing projects without re-explaining everything.

• They support multiple parallel workflows and multi-session collaborations seamlessly.

4. Token Efficiency & Cost Savings

Detailed Explanation
Every word you send to a paid AI model consumes tokens—and tokens translate directly into cost. By swapping free-form instructions (which might run 40–60 tokens for a single command) for concise object instantiations (often 5–10 tokens), you save money and speed up responses:

• Concise Syntax:

  • Free-form: “Plot a bar chart of monthly website traffic, labeling the X-axis ‘Month’ and the Y-axis ‘Visits’, coloring the bars blue, and include data points for at least the past 12 months.” (~50 tokens)

  • Object-based:

    python

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    ChartSpec( type="bar", x="month", y="visits", title="Monthly Website Traffic", data_source="traffic_last12months" )

    (~8 tokens for the object name + field references; exact count varies by model, but markedly lower).

• Fewer Ambiguities:

  • Because the object’s schema defines defaults (e.g., default bar color, default chart style), you don’t need to restate them every time.

• Automated Defaults:

  • Many objects have sensible defaults baked in. A simple ChartSpec(type="bar", x="month", y="visits") might default to your brand’s color palette, axis labels formatted with title case, and gridlines enabled—so you never have to specify “color: #FF6600” or “grid: true.”

The bar chart above illustrates this point:

• Free-text Prompt: ~50 tokens

• Object-based Prompt: ~5 tokens
  Over many interactions, those saved tokens compound into significant cost reductions—especially when running iterative, multi-step workflows.

Everyday Analogy
It’s like sending a short “URL” instead of a long paragraph explaining how to find something online:

• Instead of writing “Go to Google, type ‘best cyber security tools,’ click on the first result, scroll to section 3, and copy the list of tools,” you send “Open https://bit.ly/CyberToolsList.”

• The latter is much shorter, quicker to process, and less likely to have typos or misunderstandings.

Short Summary

• Objects drastically reduce token usage by replacing verbose text with concise, structured calls.

• Over time, this translates into material cost savings and faster AI responses.

5. Business Alignment & Compliance

Detailed Explanation
When you encode business rules, brand guidelines, or compliance requirements as objects (BusinessLogic, ConstraintSet, ComplianceRule), you ensure every AI-generated output conforms to your organization’s standards:

• Centralized Policies:

  • Instead of reminding AI “always use our brand voice,” you attach ConstraintSet("BrandVoice") to every content‐related object. If your brand voice changes—e.g., shifting from “professional_corporate” to “friendly_innovative”—you edit the ConstraintSet, and all future outputs automatically update.

• Regulatory Guardrails:

  • Sensitive industries (healthcare, finance, legal) have strict rules. A ComplianceRule("HIPAA", requirement="anonymize_patient_data") object ensures AI never outputs personally identifiable patient data in medical summaries.

• Auditability:

  • Objects carry metadata about who created them, when, and for what purpose. This makes auditing AI outputs simple: you can query all AIInstruction objects that used ConstraintSet("FinancialGuidelines") in the last quarter.

• Consistency Across Teams:

  • Marketing, Legal, and Engineering can centralize their domain rules in CRAFT objects. When anyone requests a “ProductFeatureList,” AI references those shared objects—avoiding conflicting interpretations between departments.

Everyday Analogy
Consider a restaurant chain with a standardized recipe book:

• Each franchise follows the exact steps for making a burger—same ingredients, same cooking time, same plating instructions.

• If the headquarters changes the recipe (say, swapping cheddar for pepper jack), they update one “RecipeObject,” and every franchise automatically knows the new process.

• The recipe book is the BusinessLogic; every kitchen (AI session) follows it to ensure brand consistency and compliance.

Short Summary

• Business rules and compliance requirements become enforceable, reusable objects.

• Companies can guarantee brand voice and regulatory adherence without repetitive instructions.

6. Collaboration & Knowledge Transfer

Detailed Explanation
CRAFT objects make it easier for multiple stakeholders—writers, analysts, executives—to share a common “language” when interacting with AI:

• Shared Object Repositories:

  • A central PromptLibrary or WorkflowLibrary can be version‐controlled and shared across teams. Everyone sees the same definitions and conventions.

• Lower Onboarding Overhead:

  • New team members don’t need to learn years of free-form prompting habits. They can browse the object spec (e.g., see that Metric("roi", value, unit="percent") means “evaluate ROI”) and start using objects right away.

• Clear Handovers:

  • If Alice builds a KnowledgeGraphNode("CompetitorX") with key properties and Bob picks up next week, Bob sees exactly what’s stored—no need to re-ask, “What did we learn about CompetitorX?”

• Reduced Miscommunication:

  • With free-form prompts, one person’s “summarize” might be another person’s “deep dive.” By standardizing on OutputSchema("ExecutiveSummary"), everyone knows “ExecutiveSummary” yields bullet points under 150 words with a “recommendations” section.

Everyday Analogy
It’s like using a shared template in a project management tool:

• Instead of everyone creating tasks in their own style (some write “Call vendor,” others “Reach out to vendor for pricing”), you have a “VendorOutreach” task template with fields “VendorName,” “ContactDate,” “Purpose,” and “Status.”

• Anyone can fill out that template, and downstream reports always look uniform.

Short Summary

• Objects provide a common vocabulary, reducing onboarding time and miscommunication.

• They enable clean handoffs and collaborative work without re‐explaining context.

Chart: Token Usage Savings

Below is a bar chart illustrating token usage for a comparable task implemented once as a free-form prompt and once as an object-based prompt. Notice the dramatic reduction in tokens (and therefore potential cost savings) when using CRAFT objects:

Overall Summary of Benefits

1. Modularity & Reusability

   • Define once, reuse everywhere—akin to reusable LEGO bricks.

2. Clarity & Maintainability

   • Objects replace ambiguous text with explicit fields—like filling out a standardized form instead of a sticky note.

3. Scalability & Context Persistence

   • Objects hold state across sessions, akin to a digital project management tool that remembers every task’s status.

4. Token Efficiency & Cost Savings

   • Swapping 50-token paragraphs for 5-token object calls dramatically cuts costs—like using short URLs instead of long explanations.

5. Business Alignment & Compliance

   • Encode brand and regulatory rules directly into objects, ensuring uniform adherence—similar to a franchise recipe book.

6. Collaboration & Knowledge Transfer

   • Shared object definitions act as a common vocabulary, reducing onboarding friction—like using shared task templates in project management.

By leveraging these benefits, entrepreneurs and teams can treat AI interactions as a structured, maintainable, and cost-effective extension of their workflows—rather than ad-hoc, one-off chats.

Sources

You said:

Great, please expand on Creative Adaptation Ideas for CRAFT Objects

ChatGPT said:

Below is an expanded look at Creative Adaptation Ideas for CRAFT Objects—practical ways you can combine and extend object types to solve entrepreneurial challenges, drive dynamic AI workflows, and push the boundaries of what CRAFT can do. Each adaptation includes concrete examples, benefits, and everyday analogies to illustrate how these ideas play out in real use cases.

1. Dynamic Persona Switching via AIPersona Objects

Detailed Explanation

In many entrepreneurial scenarios, you need AI to “wear different hats” depending on the task—or even switch mid‐conversation. For instance, you might want a technical deep dive from an engineer persona, followed by a high‐level summary from a marketing persona. A dedicated AIPersona object makes that switch explicit.

• Object Definition:

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  AIPersona( name="MarketingCopywriter", expertise="write engaging copy with emotional appeal", tone="conversational", prohibited_terms=["jargon", "acronyms without explanation"] )

  Here, the object’s fields (expertise, tone, prohibited_terms) codify how that persona speaks.

• Usage Example:
  Suppose you already generated a rough product spec, and now you want marketing copy. You’d write:

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  #H->AI::Instruction: "Create a product description for {product_name}" .using(AIPersona("MarketingCopywriter"))

  Behind the scenes, CRAFT’s engine interprets .using(AIPersona("MarketingCopywriter")) and loads that persona’s style rules, so the AI knows to write in an emotionally resonant, jargon‐light way.

• Switching Personas Mid‐Flow:
  After the marketing copy, you need a technical review. You can chain:

  python

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  AIInstruction("Review the technical feasibility") .using(AIPersona("TechnicalReviewer"))

  The TechnicalReviewer persona might be defined as:

  python

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  AIPersona( name="TechnicalReviewer", expertise="assess system architecture, APIs, and scalability", tone="concise_informative", required_formats=["UML diagrams", "BulletList"] )

  Now, the AI pivots instantly—no need to restate “Write more technically.” The CRAFT engine already adjusts its output style based on that persona object.

Benefits

1. Consistent Tone Across Tasks: Each persona object ensures a uniform voice whenever that persona is invoked. If your marketing guidelines change, you tweak AIPersona("MarketingCopywriter") once, and every future marketing‐oriented instruction updates automatically.

2. Frictionless Transitions: Switching roles requires no extra explanation—just reference a different AIPersona. This enables complex dialogues (e.g., CFO persona for budgeting, HR persona for hiring) within the same chat session.

3. Clear Documentation & Ownership: Personas act like “role cards” in a playbook. Everyone on your team knows exactly which persona to use for sales pitches, technical write‐ups, or customer support responses.

Everyday Analogy

Imagine a customer support center where agents change headsets to switch functions:

• Putting on the “Sales” headset routes you to pitch training, pricing scripts, and upsell tactics.

• Hanging that up and grabbing the “Technical Support” headset gives you access to troubleshooting guides, code snippets, and escalation paths.
  CRAFT’s AIPersona works the same way: assign a persona object, and AI “puts on” the corresponding role.

2. Parametric Prompt Libraries through Prompt Objects

Detailed Explanation

Rather than scavenging through old chats to find that one perfect prompt, you can build a parametric library of Prompt objects. These objects serve as blueprints, complete with named placeholders, so you only supply the specifics at runtime.

• Object Definition:

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  Prompt( name="QuarterlyEarningsSummary", template="Summarize the {quarter} earnings report for {company} focusing on {key_focus}.", variables=["quarter", "company", "key_focus"], default_format="BulletList", recommended_length="short" )

  Key fields:

  • template: The text pattern with placeholders ({quarter}, {company}, {key_focus}).

  • variables: Names of placeholders so CRAFT knows what to ask you if any are missing.

  • default_format & recommended_length: Implicit instructions baked into the object so you don’t have to restate “use bullets” every time.

• Usage Example:
  If you need a summary for Q3 earnings of “Acme Corp,” you do:

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  PromptLibrary("FinancialSummaries").get("QuarterlyEarningsSummary") .fill(quart er="Q3", company="Acme Corp", key_focus="expenses_vs_revenue") .execute()

  The fill() method populates placeholders and passes the prompt to AI. Because default_format="BulletList", AI outputs a concise bulleted summary.

• Batch Processing:
  Suppose you manage multiple portfolio companies and want to generate a one‐page summary for each. You can loop:

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  for company in ["Acme Corp", "BetaTech", "GammaSoft"]: PromptLibrary("FinancialSummaries").get("QuarterlyEarningsSummary") .fill(quarter="Q3", company=company, key_focus="net_income") .execute()

  CRAFT automatically instantiates three prompt objects, and AI produces three separate summaries—no retyping, no copy‐pasting.

Benefits

1. Scalable Content Generation: Easily scale from one‐off prompts to hundreds of reports by looping over a list of parameters.

2. Error Reduction: Because your variables are defined up front, missing or misspelled placeholders trigger a CRAFT‐level validation error rather than a half‐baked prompt to AI.

3. Rapid Iteration: If you decide you want paragraphs instead of bullet lists, change default_format="Paragraph" in the Prompt object. All future executions reflect that change.

Everyday Analogy

Think of mail merge in Microsoft Word:

• You design a single letter template with fields for FirstName, LastName, Address.

• You import a CSV, and Word generates personalized letters automatically.
  Parametric prompts replicate that concept: one template drives many customized outputs.

3. Automated Research Pipelines with Workflow + APIRequest Objects

Detailed Explanation

Entrepreneurs often need to pull data from external sources—competitor pricing, customer reviews, or social trends—and then analyze it. By combining Workflow and APIRequest objects, you can orchestrate an end‐to‐end research pipeline:

• Step 1 – Data Collection (APIRequest):

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  APIRequest( name="FetchCompetitorData", endpoint="/v1/competitor_pricing", method="GET", params={"industry": "cybersecurity", "region": "US"} )

  This object instructs CRAFT to fetch competitor pricing data from your chosen API.

• Step 2 – Preliminary Analysis (AIInstruction):

  python

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  AIInstruction( action="AnalyzeJSON", data_source="FetchCompetitorData.response", output_format="ComparisonMatrix", criteria=["Price", "FeatureSet", "CustomerRating"] )

  Here, FetchCompetitorData.response references the JSON payload returned by the previous step. CRAFT’s engine interprets that, parses JSON, and outputs a ComparisonMatrix between competitors.

• Step 3 – Recommendation Generation (AIInstruction with OutputSchema):

  python

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  AIInstruction( action="GenerateRecommendations", input="ComparisonMatrix", output_format="JSON", schema=OutputSchema( name="CompetitorInsights", fields={ "competitor": "string", "strengths": "list[string]", "weaknesses": "list[string]", "recommendation": "string" } ) )

  Now the AI takes the comparison table and produces a structured JSON object with insights for each competitor.

• Packaging as a Workflow:

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  Workflow( name="CompetitorAnalysisPipeline", steps=[ APIRequest("FetchCompetitorData", ...), AIInstruction("AnalyzeJSON", ...), AIInstruction("GenerateRecommendations", ...) ] )

  With this one Workflow, you’ve defined an automated pipeline: fetch data → analyze → generate recommendations. When you call Workflow("CompetitorAnalysisPipeline"), CRAFT executes each step in sequence, passing outputs transparently.

Benefits

1. No Manual Data Handling: You don’t copy‐paste JSON or manually instruct AI to parse it. The APIRequest and AIInstruction objects handle parsing and passing of results.

2. End‐to‐End Reusability: Save the entire pipeline in your WorkflowLibrary. Next quarter, you update API params or change the recommendation schema—your code remains in one place.

3. Error Handling & Conditional Branching: You can attach an AIDecisionPoint after the APIRequest to check response.status_code != 200 and branch accordingly, triggering an alert rather than proceeding.

Everyday Analogy

Imagine an assembly line in a factory:

• Station 1 automatically fetches raw materials (APIRequest).

• Station 2 transforms materials into subcomponents (AIInstruction analyzing JSON).

• Station 3 packages the finished product (AIInstruction generating recommendations).
  The Workflow is the conveyor belt that links them—everything happens automatically once you press “Start.”

4. Adaptive Decision Paths Using AIDecisionPoint + Metric Objects

Detailed Explanation

Some business processes require dynamic branching—for instance, if a customer NPS score falls below a threshold, you trigger an escalation. AIDecisionPoint objects let you encode these “if‐then‐else” conditions without descriptive paragraphs:

• Step 1 – Collect a Metric:

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  Metric( name="CustomerNPS", value=latest_nps_score, unit="score", threshold={"good": 8, "neutral": 7, "poor": 6} )

  This object captures the latest NPS result and defines benchmark thresholds.

• Step 2 – Define Decision Logic:

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  AIDecisionPoint( name="FollowUpAction", trigger=Metric("CustomerNPS", value=latest_nps_score, unit="score"), condition="value < threshold['poor']", if_true=AIActionTemplate("InitiateSupportEscalation", team="CustomerSuccess"), if_false=AIActionTemplate("SendSurveyThankYou", template="StandardThanks") )

  The condition checks if value (e.g., 5) is below threshold['poor'] (6). If so, it invokes the “InitiateSupportEscalation” template; otherwise, it sends a thank‐you email.

• Step 3 – Embed in a Workflow:

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  Workflow( name="PostSurveyWorkflow", steps=[ AIInstruction("CollectNPSScore", customer_id="{cid}"), AIDecisionPoint("FollowUpAction", trigger=Metric(...), ...), AIInstruction("LogOutcome", data_source="FollowUpAction.result") ] )

  With this Workflow, once you collect a survey score, CRAFT automatically chooses the next action.

Benefits

1. Declarative Branching: No multi‐sentence “If NPS is < 6, do X; else do Y.” Everything is in one structured object.

2. Reusable Decision Templates: If you run multiple surveys (CSAT, CES, NPS), you can parameterize the same AIDecisionPoint logic with different Metric names and thresholds.

3. Clear Audit Trail: Because each branch is a named object, you can query “Which customers triggered an escalation in the last month?”—AI can filter logs based on the AIDecisionPoint name.

Everyday Analogy

Think of a “choose your own adventure” book:

• At each page, you read a prompt: “If you go into the forest, turn to page 32; if you enter the castle, turn to page 45.”

• Each decision point is an AIDecisionPoint object—concise, unambiguous, and easy to follow. The book “knows” how to direct you based on your choice without reprinting the entire storyline.

5. Evolving Knowledge Graphs via KnowledgeGraphNode + DefinedRelationship Objects

Detailed Explanation

A core tenet of “Cumulative Intelligence” is that your AI system gradually accumulates knowledge about entities and how they relate. By treating entities (people, products, partners) as KnowledgeGraphNode objects and explicitly linking them with DefinedRelationship, you build a graph that the AI can query for richer insights:

• Creating Knowledge Nodes:

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  KnowledgeGraphNode( id="Vendor_XYZ", type="Vendor", properties={ "industry": "CloudHosting", "year_founded": 2010, "reputation_score": 4.5 } ) KnowledgeGraphNode( id="Project_Alpha", type="InternalProject", properties={"start_date": "2025-01-01", "budget": 100000} )

• Linking with DefinedRelationship:

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  DefinedRelationship( subject="Vendor_XYZ", predicate="supplies_to", object="Project_Alpha", confidence=0.92 )

  Now the AI “knows” Vendor_XYZ supplies resources to Project_Alpha.

• Querying the Graph:
  Later, you can ask:

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  AIInstruction("ListAllVendorsForProject", input="Project_Alpha")

  Because CRAFT stored DefinedRelationship, AI traverses the graph and returns “Vendor_XYZ.” You can then chain:

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  AIInstruction("EvaluateVendor", input="Vendor_XYZ", criteria=["cost", "reliability"])

  And so on.

• Expanding Over Time:
  Each new vendor, partner, or product is a new KnowledgeGraphNode. Relationships (partnership_with, competitor_of, uses_tech) grow organically. Over months, you end up with a rich, semantically linked map of your ecosystem.

Benefits

1. Persistent, Structured Memory: Rather than hoping AI “remembers” that Vendor_XYZ is linked to Project_Alpha, the relationship is stored as a CRAFT object.

2. Rich Contextual Reasoning: When making strategic decisions, AI can pull from the graph. For example, “Identify all internal projects that use Python and list their vendors.”

3. Easier Onboarding: New team members can inspect the knowledge graph to see “Which vendors do we currently engage?” or “What projects use AWS?”—instead of digging through chat transcripts.

Everyday Analogy

Think of a mind‐map or a concept map:

• You start with one bubble labeled “Company.” You draw lines to “Product A,” “Product B.” Then each product connects to its “Vendors,” “Features,” “Stakeholders.”

• Over time, the map fills out—an instant visual of how everything is connected. CRAFT’s KnowledgeGraphNode and DefinedRelationship create that same structure behind the scenes, but in a machine-readable form.

6. Contextual Output Enforcement with OutputSchema + ConstraintSet

Detailed Explanation

When your AI outputs need to follow strict formatting or content rules—whether it’s for regulatory compliance, brand consistency, or downstream machine parsing—you combine OutputSchema (for structural enforcement) with ConstraintSet (for stylistic and policy enforcement).

• Defining an Output Schema:

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  OutputSchema( name="InvestorPitchDeck", fields={ "title_slide": {"title": "string", "subtitle": "string"}, "problem_statement": "paragraph", "solution_overview": "paragraph", "market_analysis": { "market_size": "number", "growth_rate": "number", "key_trends": ["string"] }, "financial_projections": "table" } )

  This schema spells out exactly which sections appear and what type of data goes in each.

• Attaching a Constraint Set:

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  ConstraintSet( name="PitchDeckBrandGuidelines", rules=[ "Use company logo in top-left corner of each slide", "Limit slides to 10 max", "Tone: Inspirational yet data-driven", "Font family: Arial or Helvetica" ] )

• Prompting AI:

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  AIInstruction( action="GenerateInvestorPitchDeck", input="CompanyOverviewData", output_schema=OutputSchema("InvestorPitchDeck"), constraints=ConstraintSet("PitchDeckBrandGuidelines") )

  Because you’ve provided both structure and style constraints, AI produces a 10‐slide outline (or actual slide content) that matches your brand and is guaranteed to follow the schema—each JSON object or slide section appears exactly as specified.

• Automated Validation:
  Later, if someone inadvertently passes malformed data (e.g., a numeric value for a field expecting text), CRAFT raises a validation error before AI even generates content. This pre‐emptively catches mistakes instead of discovering them in post hoc reviews.

Benefits

1. Guaranteed Consistency: Every output across multiple sessions or team members adheres to the same structure and brand rules.

2. Machine-Parseable Results: Systems downstream (e.g., BI dashboards, partner integrations) can ingest AI outputs without additional parsing code—because they conform to OutputSchema.

3. Reduced Manual QA: Editors no longer need to check slide counts, logo placement, or tone. CRAFT enforces these rules automatically.

Everyday Analogy

Imagine a fillable PDF form with strict fields and checkboxes (e.g., a government tax form). You can’t write “lots of words” in a numeric field, and you can’t skip mandatory sections. CRAFT’s OutputSchema and ConstraintSet are that PDF: they force compliance before content exists.

Summary of Creative Adaptations

1. AIPersona Objects:

   • Switch AI roles seamlessly (e.g., “MarketingCopywriter” vs. “TechnicalReviewer”) by invoking different persona objects.

2. Parametric Prompt Libraries:

   • Store prompt templates with placeholders in a PromptLibrary for batch content generation and rapid iteration.

3. Automated Research Pipelines:

   • Chain APIRequest, AIInstruction, and OutputSchema in a Workflow so AI automatically fetches, analyzes, and summarizes external data.

4. Adaptive Decision Paths:

   • Use AIDecisionPoint with Metric objects to branch logic “if metric < threshold, do X; else do Y” without free‐form conditionals.

5. Evolving Knowledge Graphs:

   • Build KnowledgeGraphNode and DefinedRelationship objects to form a persistent, queryable map of your domain—enabling cumulative intelligence.

6. Contextual Output Enforcement:

   • Combine OutputSchema and ConstraintSet to guarantee that AI outputs follow strict formatting and brand/regulatory rules.

By creatively adapting and composing these CRAFT objects, you turn AI prompts into dynamic, robust workflows—moving from one‐off instructions to a self‐documenting, reusable system that scales as your business grows.

Sources

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CRAFT Object Definition Steps

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StepDescriptionExampleBenefit1. Identify Recurring PatternsLook for tasks or data structures you repeatedly describe in prose.Noticing repeated 'analyze financial data' instructions becomes a 'FinancialAnalyzer' object.Minimizes rewriting the same instructions and reduces errors.2. Draft a Declarative Data ModelDefine the object's attributes and intended behaviors in a structured format.Create FinancialAnalyzer(name, data_source, metrics) with fields and methods.Ensures clarity, making it easier to maintain and update schemas.3. Embed Examples in SpecAdd code snippets in the CRAFT spec showing how to instantiate and use the object.In CRAFT spec: ``FinancialAnalyzer(name="Q1Report", data_source="sales_data", metrics=["revenue", "profit"])``Demonstrates usage, reducing ambiguity for future users.

Below is a detailed expansion of the Practical Steps to Define New Custom Object Types. Each step includes explanations, examples, and analogies to help you apply these ideas in real-world CRAFT workflows. At the end, you’ll find a summary table that recaps all steps, examples, and benefits for quick reference.

1. Identify Recurring Patterns or Complex Tasks

Detailed Explanation

Start by reviewing the prompts and processes you’ve used so far. Notice any instructions or data structures you describe repeatedly in free-form text. Those are prime candidates for conversion into objects. Common patterns include:

• Repeated Task Descriptions:
  If you frequently ask AI to “analyze financial data,” “generate a summary of product features,” or “compare vendor offerings,” that’s a sign you could define a FinancialAnalyzer, FeatureSummarizer, or VendorComparer object. Instead of retyping the instructions each time, bundle the logic into one object.

• Complex Data Structures:
  Whenever you manually format a JSON schema, comparison table, or chart specification in prose, think about creating an OutputSchema, ComparisonMatrix, or ChartSpec object. By identifying these repeating structures, you reduce the chance that slight wording changes will introduce inconsistencies.

• Conditional Logic Blocks:
  If you find yourself embedding “if…then…” logic in paragraphs—e.g., “If revenue growth is below 5%, alert the manager; otherwise, continue with the marketing plan”—it’s time to formalize that into an AIDecisionPoint or Metric object. This encapsulation makes branching explicit and machine‐readable.

Example

• Scenario: You frequently ask AI to "analyze financial data from a spreadsheet, identify key metrics (revenue, profit, expenses), and provide recommendations for cost savings."

• Recurring Pattern: That entire instruction block appears in multiple contexts—quarterly reviews, budget planning, investor updates.

• Custom Object Concept: Create a FinancialAnalyzer object that takes parameters like data_source, metrics, and recommendation_count.

Everyday Analogy
Consider a baker who bakes different types of bread. If they manually mix ingredients each time, they risk inconsistencies in kneading time, baking temperature, or ingredient proportions. By creating a “BreadRecipe” template (object), they ensure each loaf follows the same standardized process—no guesswork, no mistakes.

2. Draft a Declarative Data Model

Detailed Explanation

Once you’ve identified a pattern or complex task, define a clear, declarative schema for the new object. This is essentially your data model: a list of attributes (fields) and, where relevant, behaviors or methods. Key considerations:

1. List All Necessary Attributes

   • Name: A unique identifier for the object (e.g., FinancialAnalyzer).

   • Parameters/Fields: Logical fields you’ll need at runtime. For FinancialAnalyzer, these might include:

     • data_source: Where the financial data lives (spreadsheet name, SQL query, etc.)

     • metrics: A list of strings (["revenue", "profit", "expenses"])

     • recommendation_count: How many cost-saving recommendations to generate

   • Optional Fields: Defaults or toggles (e.g., include_graphs=True, currency="USD").

2. Define Behaviors (if applicable)
   Although CRAFT objects are mostly declarative, you may specify that certain objects trigger built-in AI behaviors. For example, FinancialAnalyzer might implicitly instruct AI to:

   • Parse the dataset

   • Compute each metric trend

   • Identify top three cost‐saving opportunities
     Document that in your data model so AI “knows” what to do when it sees a FinancialAnalyzer object.

3. Select Meaningful Field Names
   Use intuitive, self‐documenting names. data_source is clearer than ds, metrics_to_check is better than m. This helps future users (and future AI sessions) understand the object at a glance.

4. Consider Data Types & Validation
   If a field expects a list of strings, make sure your spec notes that. If another field must be an integer greater than 0, document that constraint. Doing so enables CRAFT’s validation system to catch missing or malformed inputs before AI processes them.

Example

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# Declarative Data Model for a new custom object class FinancialAnalyzer: """ Attributes: - name (string): Unique label for this analysis instance. - data_source (string): Identifier for the financial dataset (e.g., 'Q1_Sales.csv'). - metrics (list[string]): Metrics to compute (e.g., ['revenue', 'profit', 'expenses']). - recommendation_count (integer): Number of cost-saving recommendations to return. - include_graphs (boolean, default=True): Whether to include trend graphs in the output. - currency (string, default='USD'): Currency for all financial values. Behavior: - Compute each metric’s trend (month-over-month % change). - Identify the top N cost-saving opportunities. - Format output as a structured JSON with sections: 'metric_trends', 'recommendations'. """

Everyday Analogy
Creating a data model is like drafting a blueprint before building a house: you outline each room’s dimensions, the materials needed, and how rooms connect. Once the blueprint is clear, construction is straightforward, and any builder knows exactly what to do.

3. Embed Examples in the CRAFT Spec

Detailed Explanation

Documentation alone isn’t enough; you need concrete examples to demonstrate how to instantiate and use the new object. In your CRAFT spec file (e.g., CFT-FWK-SPEC-v0625d8-Objects.txt), add a “Usage Examples” section under your object’s definition. This typically includes:

• One or More Code Snippets
  Show real‐world instantiation with representative values. For instance:

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  FinancialAnalyzer( name="Q1_Earnings_Review", data_source="Q1_Sales.csv", metrics=["revenue", "profit", "expenses"], recommendation_count=3, include_graphs=True, currency="USD" )

• Comments Explaining Each Field
  Inline comments or trailing comments clarify why certain values are chosen:

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  FinancialAnalyzer( name="Q1_Earnings_Review", # Label for easy reference data_source="Q1_Sales.csv", # CSV file with columns: month, revenue, cost, expense metrics=["revenue", "profit", "expenses"], # Compute these metrics recommendation_count=3, # Return top 3 cost-saving ideas include_graphs=True, # Include charts for visual clarity currency="USD" # Use US dollars for all monetary calculations )

• Example Outputs (Optional but Helpful)
  If possible, show a sample AI response to that instantiation. Even a short excerpt like:

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  { "metric_trends": { "revenue": { "Jan": 12000, "Feb": 13000, "Mar": 12500, ... }, ... }, "recommendations": [ "Negotiate lower rates with supplier X to save 5%", "Consolidate spending on marketing channels A and B", "Reduce overhead by optimizing inventory levels" ] }

  gives future users a concrete idea of what to expect.

Why This Matters

• Reduces Ambiguity: AI and team members see exactly how to call the object, preventing guesswork.

• Accelerates Adoption: Developers essentially have copy-paste-ready code. They don’t need to infer field names or decide on formatting defaults.

• Facilitates Validation: The examples serve as test cases for your validation logic (CRAFT can check if actual instantiations match these examples).

Everyday Analogy
When you buy furniture that requires assembly, the instruction manual usually includes a fully assembled photograph plus step‐by-step diagrams. Without those images, you’d fuss over how pieces fit. Example code snippets in the spec serve the same purpose: they show the “assembled” object so you know what the end state looks like.

4. Document Expected AI Behavior

Detailed Explanation

Beyond showing how to instantiate an object, you must document how AI should interpret it—i.e., the “semantics” or behavior tied to that data model. This goes under a “Behavior” or “Description” section in your spec. Include:

1. Intent Description
   A concise, plain‐English summary of what the object represents (e.g., “FinancialAnalyzer: Encapsulates a financial dataset analysis, computing trends and generating cost-saving recommendations.”).

2. Field Semantics
   For each field, explain:

   • Data Format: “data_source should be a string pointing to a CSV or database table name.”

   • Units or Accepted Values: “currency must be an ISO currency code (e.g., ‘USD’, ‘EUR’).”

   • Behavioral Defaults: “If include_graphs=True, AI generates a simple line chart for each metric; if False, omits charts entirely.”

3. Implicit Methods
   If the object triggers a multi‐step AI process, briefly describe each step. For example:

   • Step 1: Parse the data_source to load raw data.

   • Step 2: Compute monthly revenue, profit, and expense trends.

   • Step 3: Identify top N anomalies or cost spikes.

   • Step 4: Formulate recommendations based on patterns (e.g., “Supplier costs rose 15% in February—suggest renegotiating contract”).

   • Step 5: Format final output as a JSON object with two keys: metric_trends and recommendations.

4. Error Handling
   Document what happens if inputs are missing, malformed, or out of range. For instance:

   • “If metrics is empty or contains unknown metric names, AI responds with an error object: { 'error': 'Unknown metric: X' }.”

   • “If data parsing fails, AI returns: { 'error': 'Unable to load data from Q1_Sales.csv' }.”

Citations

Everyday Analogy
Think of this like API documentation:

• The “endpoint” is your object name.

• Each “parameter” has a description, accepted values, and default behavior.

• You also show example requests and responses. Without such documentation, developers misuse APIs and encounter unexpected behaviors.

5. Test and Iterate

Detailed Explanation

Defining an object in spec is only half the battle—ensuring that AI consistently recognizes and executes it as intended requires testing. Follow these sub‐steps:

1. Create Sample Chat Scenarios

   • Basic Instantiation: Start with a simple call, e.g.:

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     FinancialAnalyzer( name="TestAnalysis", data_source="SampleData.csv", metrics=["revenue", "profit"], recommendation_count=2 )

     Confirm that AI processes it, computes trends, and returns two recommendations in the expected JSON format.

   • Edge Cases: Test missing fields or invalid values:

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     FinancialAnalyzer( name="EdgeCase1", data_source="", # missing data_source metrics=[], recommendation_count=0 )

     AI should respond with structured errors (e.g., { 'error': 'data_source cannot be empty' }).

2. Refine Prompt Handlers

   • If you notice AI ignoring certain fields (e.g., it doesn’t include graphs when include_graphs=True), adjust the underlying prompt templates or behavior description in the spec.

   • If AI misinterprets a field’s purpose (e.g., thinking recommendation_count refers to chapters instead of bullet points), update the object’s description and examples.

3. Gather Feedback from Stakeholders

   • Have colleagues or team members use the object in their own CRAFT sessions. Note any confusion or misalignment.

   • For instance, a marketer might expect “recommendations” to be marketing‐focused, while a finance lead expects cost‐cutting suggestions. You may need to add a field like recommendation_focus (e.g., “marketing” or “cost”).

4. Versioning and Updates

   • Once the object behaves as intended, tag it with a VersionTag (e.g., v1.0). If you later tweak its behavior (perhaps adding a new field or changing default logic), increment to v1.1 or v2.0.

   • Document the changes in a changelog section of your spec so that anyone referencing the object knows exactly how its behavior evolved.

5. Automate Regression Testing (Optional)

   • For advanced setups, write a small testing harness that imports your CRAFT spec and runs a suite of example instantiations to verify outputs. If AI model updates cause different behavior, you catch it early.

Everyday Analogy
This process resembles software unit testing:

• You write test cases for both “happy path” and “edge cases.”

• You iterate on your code (object definition) until tests pass reliably.

• You tag releases (versioning) so downstream users know which version they’re on.

Summary Table of Practical Steps

Below is a summary table that captures each practical step, a brief description, a concrete example, and the primary benefit. Use this as a quick reference when defining new CRAFT objects.

<!-- The table is displayed via python_user_visible above -->

Overall Takeaways

1. Spot Repetition: Look for any chunk of free-form text or multi-step instruction you send repeatedly—those are prime candidates for objects.

2. Design with Clarity: A well-structured data model (fields and behaviors) prevents misunderstandings and makes maintenance a breeze.

3. Show, Don’t Tell: Always include usage examples in your CRAFT spec so human and AI readers understand exactly how to instantiate and interpret the object.

4. Spell Out AI Behavior: Document both “what” the object means and “how” AI should act when it sees the object—this bridges the gap between your intent and AI’s execution.

5. Validate & Iterate: Test in real chat scenarios, gather feedback, and version your objects. Iteration ensures reliable, predictable AI behavior over time.

By following these steps, you transform free‐wheeling prompt fragments into robust, maintainable, and reusable building blocks—extending CRAFT’s power and enabling consistent, efficient AI collaboration.

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