CRAFT™️ Experiment: Gemini: Rethinking A.I. Objects (3 of 5)

The CRAFT™️ Experiment
Written By Richard Ketelsen

THE CRAFT™️ EXPERIMENT :: WEEK 6 :: POST 3

Gemini: Rethinking A.I. Objects (3 of 5)

Outline: Leveling Up CRAFT with Interactive Objects

Introduction: From Data Structures to Dynamic Partners

The current CRAFT framework brilliantly uses object-like "Data Types" (e.g., SocialPost, Workflow) to structure information. The next evolution is to introduce true "Project Objects"—dynamic entities that combine data, functions, and even their own interactive logic. This moves beyond simple data containers to create active partners in our workflow, fully embracing the CRAFT philosophies of "Code as Conversation" and "Human-AI Partnership".

1. The Stateful Object: The "Project Brain" for Session Continuity

  • Concept: Create objects that manage their own state and can be passed between chat sessions using the HANDOFF_SNAPSHOT system. This provides a massive leap for the "Cumulative Intelligence" principle.

  • Example Idea: The ProjectTracker Object.

    • An object that encapsulates a project's key metrics, tasks, and status.

    • Instead of just describing progress in a handoff, we pass the object itself. The next AI assistant can immediately interact with it (e.g., MyProject.get_status() or MyProject.calculate_burn_rate()).

2. The Interactive Object: The "AI Guide" That Prompts Back

  • Concept: Design objects that, upon creation or modification, can proactively prompt the user for the next logical step. This turns a passive object into an active guide, streamlining complex workflows.

  • Example Idea: The AB_Test Object.

    • A user defines an A/B test: HomepageTest = AB_Test(name="Homepage CTA Button", goal="Increase Signups").

    • The AI, guided by the object's internal logic, would respond: #AI->H::Note: (AB_Test object created. To proceed, please provide the 'Control' and 'Variant' using the .add_variant() method.) This embodies the "Human-AI Partnership" by having the AI guide the user through a structured process.

3. The Generator Object: The "Content Factory"

  • Concept: Develop objects that act as specialized factories for content generation. These objects would hold core strategic information (like audience and tone) and use it to execute a variety of creative tasks consistently.

  • Example Idea: The ContentCampaign Object.

    • Initialize an object with high-level strategy: NewCampaign = ContentCampaign(audience=TargetAudience("marketing professionals"), tone=AITone("friendly", "professional"), topic="The Future of AI in Marketing").

    • Execute methods that use this built-in context: NewCampaign.generate_blog_titles(count=10), NewCampaign.draft_linkedin_posts(count=5), NewCampaign.create_email_subject_lines().

4. The Persona Object 2.0: The "Virtual Team Member"

  • Concept: Evolve the existing AIPersona data type into a full-fledged TeamMember object. This allows for the creation of a virtual team of AI specialists that can be assigned specific roles and tasks within a single chat.

  • Example Idea: The TeamMember Object.

    • Instantiate a virtual team:

      • CriticalEditor = TeamMember(persona="Expert Editor", skills=["Conciseness", "Clarity"])

      • CreativeDirector = TeamMember(persona="Visionary Brainstormer", skills=["Innovative Ideas", "Visual Concepts"])

    • Delegate tasks directly: CriticalEditor.review(my_blog_draft) or CreativeDirector.suggest_visuals_for(my_blog_draft). This makes collaboration more structured and powerful.

Sources

Great, please expand on 1. The Stateful Object: The "Project Brain" for Session Continuity

Show thinking

Of course. Let's expand on the concept of the Stateful Object, transforming it from a theoretical idea into a practical tool for the CRAFT framework.

1. The Stateful Object: The "Project Brain" for Session Continuity

The single biggest challenge in complex, multi-session AI projects is context decay. Each new chat is a fresh start, and continuity relies on summarizing the past. The Stateful Object solves this by creating a "Project Brain"—a living object that encapsulates the project's entire state and travels from one session to the next.

This concept directly supports two core CRAFT philosophies:

  • Cumulative Intelligence: The project's knowledge base isn't just described; it's contained within an object that actively grows and evolves.

  • Human-AI Partnership: The object becomes a shared, single source of truth that both the user and the AI can query, update, and rely on.

Proposed Implementation: The ProjectTracker Object

To make this concrete, we can define a new PROJECT_OBJECTS class in the CRAFTALPHA project file.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class ProjectTracker:
    def __init__(self, name, budget=0, status="Planning"):
        self.name = name
        self.status = status
        self.budget = budget
        self.hours_logged = 0.0
        self.milestones = {} # Using a dictionary for milestones
        self.key_decisions = []

    def log_hours(self, hours, description):
        """Adds billable hours and a description of the work."""
        self.hours_logged += hours
        # H->AI::Context: (Log this activity for reporting)
        print(f"Logged {hours} hours for: {description}")

    def add_milestone(self, name, due_date):
        """Adds a new project milestone with a due date."""
        if name not in self.milestones:
            self.milestones[name] = {"status": "Not Started", "due_date": due_date}
            print(f"Milestone '{name}' added.")
        else:
            print(f"Milestone '{name}' already exists.")

    def complete_milestone(self, name):
        """Marks a milestone as complete."""
        if name in self.milestones:
            self.milestones[name]["status"] = "Completed"
            print(f"Milestone '{name}' marked as complete.")
        else:
            print(f"Milestone '{name}' not found.")

    def generate_report(self):
        """Generates a summary report of the current project state."""
        # H->AI::Directive: (Format this output clearly for a business summary)
        report = f"""
        --- Project Status Report: {self.name} ---
        Status: {self.status}
        Budget: ${self.budget:,.2f}
        Total Hours Logged: {self.hours_logged}

        Key Decisions:
        - {"- ".join(self.key_decisions) or "None"}

        Milestones:
        """
        for name, data in self.milestones.items():
            report += f"  - {name} (Due: {data['due_date']}) - Status: {data['status']}\n"

        print(report)

### END PYTHON CODE BLOCK ###

Workflow in Action: Across Two Sessions

This is how the ProjectTracker object makes the HANDOFF_SNAPSHOT system radically more effective.

Session 1: Project Kick-off

User: Let's start a new project. MyKetelsenAI_Project = ProjectTracker(name="Q3 Blog Content Strategy", budget=5000)

AI: #AI->H::Note: (ProjectTracker object 'MyKetelsenAI_Project' created with status 'Planning'.)

User: MyKetelsenAI_Project.add_milestone(name="Outline 5 Main Articles", due_date="2025-06-10")

AI: Milestone 'Outline 5 Main Articles' added.

(...work continues until the token limit is near...)

AI: We're approaching this chat's token limit. Let's create a transition HANDOFF_SNAPSHOT.

HANDOFF_SNAPSHOT Contents:

META.SESSION_ID: CFT-PROJ-0004 ... CURRENT_STATE: The active artifact is the MyKetelsenAI_Project object. Its current state is: {name: "Q3 Blog Content Strategy", status: "Planning", budget: 5000, hours_logged: 0, milestones: {"Outline 5 Main Articles": {"status": "Not Started", "due_date": "2025-06-10"}}, key_decisions: []} ...

Session 2: Seamless Continuation

The next AI assistant reviews the handoff, reinstantiates the MyKetelsenAI_Project object with its data, and is immediately ready.

User: Welcome back. Can you give me a status update on our project? MyKetelsenAI_Project.generate_report()

AI: Of course. Here is the current report:

--- Project Status Report: Q3 Blog Content Strategy ---
Status: Planning
Budget: $5,000.00
Total Hours Logged: 0.0

Key Decisions:
- None

Milestones:
  - Outline 5 Main Articles (Due: 2025-06-10) - Status: Not Started

This workflow completely eliminates the need for manual summaries or the risk of forgetting key details between sessions. The object is the project's memory, creating a truly seamless and efficient process.

Sources

Great, please expand on 2. The Interactive Object: The "AI Guide" That Prompts Back

Show thinking

Excellent. Let's expand on the second concept, the Interactive Object.

2. The Interactive Object: The "AI Guide" That Prompts Back

This concept moves an object from being a passive data container to an active participant in the workflow. An Interactive Object contains internal logic that allows it to guide the user, prompting them for the next logical action. This fundamentally enhances the "Human-AI Partnership" by creating a conversational loop where the AI, guided by the object, helps you build and execute complex tasks step-by-step.

It directly addresses the user pain point of "Difficulty Crafting Effective Prompts" by reducing the need to remember a complex sequence of commands. The object itself shows you the way.

Proposed Implementation: The AB_Test Object

A/B testing is a perfect use case for entrepreneurs. It has a clear, multi-step process that an Interactive Object can streamline. Here is how it could be defined in the PROJECT_OBJECTS section.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class AB_Test:
    def __init__(self, name, hypothesis, goal):
        self.name = name
        self.hypothesis = hypothesis
        self.goal = goal
        self.variants = {}
        self.status = "Defining"
        # H->AI::Directive: (The following is an instruction to the AI, triggered by the object's creation.)
        # This is the "prompt back" feature.
        print(f"""#AI->H::Note: (AB_Test object '{self.name}' created.
# To proceed, please define your 'control' and at least one 'test' variant using the .add_variant() method.)""")

    def add_variant(self, name, description):
        """Adds a variant (e.g., 'control' or 'test1') to the experiment."""
        self.variants[name] = {"description": description}
        print(f"Variant '{name}' added: {description}")
        # H->AI::Consider: (Check if the test is ready to start and guide the user.)
        if "control" in self.variants and len(self.variants) > 1:
            print(f"""#AI->H::RecommendedChange: (All necessary variants are now defined.
# You can begin the experiment by calling the .start_test() method.)""")
        else:
            print("#AI->H::Note: (Please add a 'control' and at least one 'test' variant to proceed.)")

    def start_test(self):
        """Begins the A/B test, changing its status to Active."""
        if "control" in self.variants and len(self.variants) > 1:
            self.status = "Active"
            print(f"A/B Test '{self.name}' is now Active.")
        else:
            print("#AI->H::Caution: (Cannot start test. Please ensure a 'control' and at least one 'test' variant are defined.)")
            
### END PYTHON CODE BLOCK ###

The Guided Workflow in Action

Notice how the conversation feels less like giving commands and more like a guided consultation.

Session: Designing an Experiment

User: Let's set up a test for our website's call-to-action button. HomepageCTA = AB_Test(name="Homepage CTA Efficacy", hypothesis="A more direct call-to-action will increase sign-ups.", goal="Increase user sign-ups by 5%.")

AI: (Triggered by the object's creation) #AI->H::Note: (AB_Test object 'Homepage CTA Efficacy' created. # To proceed, please define your 'control' and at least one 'test' variant using the .add_variant() method.)

User: Great. Let's add the control. HomepageCTA.add_variant(name="control", description="The current 'Learn More' button.")

AI: (Triggered by the object's internal logic) Variant 'control' added: The current 'Learn More' button. #AI->H::Note: (Please add a 'control' and at least one 'test' variant to proceed.)

User: OK, here's the test variant. HomepageCTA.add_variant(name="test1", description="A new 'Start Your Free Trial' button.")

AI: (The object now sees all conditions are met and prompts for the next logical step) Variant 'test1' added: A new 'Start Your Free Trial' button. #AI->H::RecommendedChange: (All necessary variants are now defined. # You can begin the experiment by calling the .start_test() method.)

This interaction perfectly embodies the "Structure Enables Freedom" principle. The object provides a rigid, best-practice structure for setting up the test, freeing the user to focus their creative energy on the substance of the test—the hypothesis and the variants—rather than on the procedural syntax of the framework.

Sources

Great, please expand on 3. The Generator Object: The "Content Factory"

Show thinking

Of course. Here is an expansion on the third concept, the Generator Object, designed to function as a "Content Factory."

3. The Generator Object: The "Content Factory"

The Generator Object is a strategic engine designed to solve one of the biggest challenges in content creation: maintaining a consistent voice, tone, and message across multiple formats. This object acts as a central "factory" where you define the high-level strategy (the what and why) once. Then, you can command it to produce a variety of specific content pieces (the how), all of which automatically align with the core strategy.

This model is a powerful implementation of several CRAFT principles:

  • Structure Enables Freedom: By defining the strategic structure upfront, you are freed from repetitive setup and can focus on the creative output.

  • Automation: It automates complex instructions into a single, reusable call.

  • Conservation of Tokens: It dramatically shortens instructions, replacing long, descriptive prompts with concise method calls.

Proposed Implementation: The ContentCampaign Object

A content campaign is the perfect use case. It requires a unified strategy across blog posts, social media, and emails. This object would be defined in the PROJECT_OBJECTS section and would intelligently use other CRAFT Data Types.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class ContentCampaign:
    def __init__(self, name, topic, core_message, target_audience, tone, keywords=[]):
        self.name = name
        self.topic = topic
        self.core_message = core_message
        self.target_audience = target_audience # Expects a TargetAudience object [cite: 87]
        self.tone = tone # Expects an AITone object [cite: 86]
        self.keywords = keywords

        print(f"#AI->H::Note: (ContentCampaign '{self.name}' is configured. You can now generate content.)")

    def generate_blog_titles(self, count=5):
        """Generates blog titles based on the campaign's topic and keywords."""
        # H->AI::Directive: (Generate {count} blog titles for {self.target_audience.description}
        # about {self.topic}, using a {self.tone.description} tone. Include these keywords: {self.keywords})
        print(f"Generating {count} blog titles...")
        # AI would generate titles here

    def draft_social_post(self, platform):
        """Drafts a social media post, adapting it to the specified platform."""
        # H->AI::Focus: (Use the SocialPost data type to create a post for {platform}[cite: 83].
        # The content should convey the '{self.core_message}' to a {self.target_audience.description} audience.
        # Use a {self.tone.description} tone and incorporate these hashtags: {self.keywords})
        print(f"Drafting a {platform} post about '{self.topic}'...")
        # AI would generate a social post here

    def create_email_subject_lines(self, count=3):
        """Generates compelling email subject lines for the target audience."""
        # H->AI::Directive: (Create {count} email subject lines for a campaign about '{self.topic}'.
        # They must capture the attention of a {self.target_audience.description} and reflect a {self.tone.description} tone.)
        print(f"Generating {count} email subject lines...")
        # AI would generate subject lines here
### END PYTHON CODE BLOCK ###

The "Content Factory" Workflow in Action

This workflow transforms a series of disconnected tasks into a streamlined production line.

Step 1: Configure the Factory (One-Time Setup)

User: Let's build a campaign to promote our new AI-powered analytics feature.

Q3_Analytics_Push = ContentCampaign(
    name="Q3 AI Analytics Launch",
    topic="Using AI to uncover business insights",
    core_message="Our new tool saves you time by finding hidden trends in your data automatically.",
    target_audience=TargetAudience("Tech-savvy entrepreneurs", knowledge_level="intermediate")[cite: 87],
    tone=AITone("informative", "authoritative", "concise")[cite: 86],
    keywords=["AI analytics", "business intelligence", "data insights", "SaaS"]
)

AI: #AI->H::Note: (ContentCampaign 'Q3 AI Analytics Launch' is configured. You can now generate content.)

Step 2: Run the Production Line (Rapid Content Generation)

User: Give me some blog titles. Q3_Analytics_Push.generate_blog_titles(count=3)

AI: Generating 3 blog titles... 1. "Stop Digging: How AI Analytics Surfaces Key Business Insights Automatically" 2. "Beyond Dashboards: 3 Ways AI is Revolutionizing Business Intelligence" 3. "Is Your Data Lying? Using AI to Uncover the Truth"

User: Now draft a post for LinkedIn. Q3_Analytics_Push.draft_social_post(platform="LinkedIn")

AI: Drafting a LinkedIn post about 'Using AI to uncover business insights'... (AI generates a professional post, referencing the core message and using the specified keywords as hashtags)

User: Perfect. I need subject lines for the announcement email. Q3_Analytics_Push.create_email_subject_lines(count=2)

AI: Generating 2 email subject lines... 1. "Your data has a story. Let AI tell it." 2. "The new, faster way to find business insights is here."

With the Generator Object, creating a dozen coordinated content pieces is no longer a dozen separate, detailed prompts. It's one strategic setup followed by a dozen simple, efficient commands.

Sources

Great, please expand on 4. The Persona Object 2.0: The "Virtual Team Member"

Show thinking

Excellent. Let's explore the final and most collaborative concept: evolving the AIPersona into a "Virtual Team Member" object.

4. The Persona Object 2.0: The "Virtual Team Member"

This concept elevates the existing AIPersona Data Type into a fully functional TeamMember object. Instead of applying a single, temporary persona to the AI, you can now instantiate a persistent team of virtual specialists. Each "team member" has a distinct role, skillset, and can be delegated tasks directly. This transforms the "Human-AI Partnership" from a guiding principle into a tangible, interactive workflow.

It allows a user to orchestrate a collaboration between multiple AI specialists within a single chat, getting nuanced, multi-faceted feedback on their work without having to constantly redefine roles and context.

Proposed Implementation: The TeamMember Object

This object would be defined in the PROJECT_OBJECTS section. It is designed to contain another CRAFT Data Type (AIPersona), demonstrating how objects can be composed for greater power.

File: CFT-PROJ-0004_CRAFTALPHA-v0625a1-Objects.txt

Python

# =========================================================
# 5. PROJECT_OBJECTS - Class definitions and object templates
# =========================================================

### START PYTHON CODE BLOCK ###
class TeamMember:
    def __init__(self, name, persona):
        self.name = name
        self.persona = persona # Expects an AIPersona object
        self.task_log = []
        
        print(f"#AI->H::Note: (Virtual team member '{self.name}', the {self.persona.role}, has joined the project.)")

    def review(self, content):
        """Reviews content from the perspective of this team member's persona."""
        self.task_log.append(f"Reviewed content: {content[:50]}...")
        # H->AI::Directive: (Assume the role of '{self.name}'. Adopt the following persona: {self.persona}.
        # Now, critically review the following content and provide actionable feedback based on your specific focus and constraints.)
        # H->AI::Context: (The content to review is: {content})
        print(f"'{self.name}' is reviewing the content...")
        # AI would generate persona-specific feedback here

    def ideate(self, topic):
        """Brainstorms ideas on a topic from this team member's unique perspective."""
        self.task_log.append(f"Ideated on: {topic}")
        # H->AI::Directive: (Assume the role of '{self.name}' with the persona {self.persona}.
        # Brainstorm a list of creative ideas about the following topic: {topic}.
        # Your ideas should reflect your persona's unique focus.)
        print(f"'{self.name}' is brainstorming ideas about '{topic}'...")
        # AI would generate persona-specific ideas here
### END PYTHON CODE BLOCK ###

The "Virtual Team" Workflow in Action

This workflow demonstrates how to leverage a team of specialists to develop an idea from a rough draft to a polished piece.

Step 1: Assemble the Team

User: Let's build our content team for this project.

Editor = TeamMember(
    name="Casey",
    persona=AIPersona(role="Critical Editor", focus=["Clarity", "Conciseness"], constraints=["Avoid jargon", "Challenge weak arguments"])
)
Marketer = TeamMember(
    name="Riley",
    persona=AIPersona(role="Growth Marketer", focus=["Audience Engagement", "SEO Keywords"], constraints=["Must have a clear call-to-action"])
)

AI: #AI->H::Note: (Virtual team member 'Casey', the Critical Editor, has joined the project.) #AI->H::Note: (Virtual team member 'Riley', the Growth Marketer, has joined the project.)

Step 2: Multi-faceted Review of a Blog Draft

User: Riley, take a look at this draft first. I want to make sure it's engaging. Marketer.review("Our new product is a synergy of forward-thinking paradigms...")

AI (as Riley, the Marketer): "Casey' is reviewing the content... Feedback: The hook is a bit dry. Let's open with a question that speaks to the customer's pain point. For SEO, we need to include 'AI analytics' and 'SaaS' in the first paragraph. Also, the call-to-action is buried at the end—let's move it up.

User: Great feedback. I've updated the draft. Now for the hard part. Casey, your turn. Editor.review("Updated draft text...")

AI (as Casey, the Editor): 'Casey' is reviewing the content... Feedback: "Synergy of forward-thinking paradigms" is meaningless jargon. Let's cut it. Be direct. What does the product *do*? I've flagged three sentences that are over 25 words long; we need to shorten them for clarity. The argument in paragraph two is weak; it needs a specific data point to be convincing.

This layered feedback process—leveraging different AI specialists for different tasks—is a far more sophisticated and effective way to collaborate than using a single, generalist AI. It truly brings the concept of a Human-AI Partnership to life.

Richard Ketelsen
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