The TXT File: Google Gemini 2.5 Pro Experimental: Testing the new CRAFT™️ Experiment Comment System

The CRAFT™️ Experiment

Apr 28

Written By Richard Ketelsen

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

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Google Gemini 2.5 Pro Experimental: Testing the new CRAFT™️ Experiment Comment System

RESPONSE FROM GOOGLE GEMINI:

Okay, based on the CRAFT specification document (P-CRAFT001_CRAFT-SPEC_V04282025A.txt) you provided, here are 10 creative ways you could use the #H->AI:: comment system to communicate with me:

Layered Context Building: Instead of one large context block, incrementally build the scenario using multiple comments. Start broad and get specific.

Python

#H->AI::Context: (We are developing a marketing campaign for a new SaaS product.)
#H->AI::Context: (The target audience is small business owners in the creative industry.)
#H->AI::Context: (The primary goal is lead generation, secondary is brand awareness.)
#H->AI::Focus: (Prioritize strategies suitable for LinkedIn and Instagram.) # [cite: 22]
#H->AI::Directive: (Suggest 3 distinct campaign concepts.) # [cite: 21]

Simulating Conditional Logic: Guide the AI's response path based on potential conditions using #H->AI::Consider:.

Python

#H->AI::Directive: (Draft an outreach email.) # [cite: 21]
#H->AI::Consider: (If the recipient is a current customer, include a loyalty discount mention.) # [cite: 23]
#H->AI::Consider: (If the recipient is a cold lead, focus on the primary pain point solution.) # [cite: 23]
#H->AI::Constraint: (Keep the email under 200 words.) # [cite: 22]

Dynamic Prompt Templating & Execution: Define a generic structure with placeholders, then use #H->AI::Set: to populate it for specific tasks within the same session.

Python

def generate_report_summary(report_text):
    #H->AI::Placeholder: (REPORT_TYPE) # [cite: 28]
    #H->AI::Placeholder: (KEY_METRIC) # [cite: 28]
    #H->AI::Directive: (Summarize the provided report_text, focusing on the KEY_METRIC for a REPORT_TYPE report. Highlight key trends.) # [cite: 21]
    #H->AI::Structure: (Output as bullet points, max 5 points.) # [cite: 24]
    pass # Actual implementation would use the text and comments

# Later in the session:
#H->AI::Set: (REPORT_TYPE = "Quarterly Sales") # [cite: 28]
#H->AI::Set: (KEY_METRIC = "Conversion Rate") # [cite: 28]
#H->AI::Directive: (Execute generate_report_summary with the latest settings on the attached sales_data.txt) # [cite: 21]

Defining Mini-Workflows: Outline a sequence of actions for the AI to perform using multiple #H->AI::Directive: comments for structured task execution.

Python

#H->AI::Directive: (Step 1: Analyze the competitive landscape based on provided competitor_list.txt.) # [cite: 21]
#H->AI::Directive: (Step 2: Identify the top 3 unique selling propositions for our product based on the analysis.) # [cite: 21]
#H->AI::Directive: (Step 3: Draft an elevator pitch incorporating these USPs.) # [cite: 21]
#H->AI::EvaluateBy: (Clarity of USPs, conciseness of pitch.) # [cite: 26]

Assigning Prioritization Weights: Use #H->AI::Focus: combined with informal weighting to guide the AI's attention within a complex request.

Python

#H->AI::Directive: (Review the draft business plan.) # [cite: 21]
#H->AI::Focus: (Pay close attention to the financial projections - Priority High.) # [cite: 22]
#H->AI::Focus: (Review the marketing strategy - Priority Medium.) # [cite: 22]
#H->AI::Focus: (Check for grammatical errors - Priority Low, but do a quick scan.) # [cite: 22]

Setting Up Advanced Error Handling: Define specific fallback actions using multiple #H->AI::OnError: comments for robustness.

Python

#H->AI::Directive: (Generate market trend data for the specified industry using online sources.) # [cite: 21]
#H->AI::UseSource: (Prioritize data from Forbes and WSJ if available.) # [cite: 25]
#H->AI::OnError: (If recent data (last 6 months) is unavailable, state this and use data from the last year.) # [cite: 25]
#H->AI::OnError: (If specific industry data cannot be found, analyze trends in the broader sector and note the generalization.) # [cite: 25]

Creating Session Bookmarks: Use #H->AI::Metadata: to label key parts of your interaction, then use #H->AI::UseSource: to refer back to them explicitly.

Python

#H->AI::Metadata: (ID: PersonaDev_Alex - Defining the Alex persona for the project.) # [cite: 24]
# ... [Persona details] ...

# Later in the chat:
#H->AI::Directive: (Draft email copy appealing to the persona defined earlier.) # [cite: 21]
#H->AI::UseSource: (Reference ID: PersonaDev_Alex for persona details.) # [cite: 25]

Specifying Ethical Guardrails: Employ #H->AI::Constraint: to enforce specific ethical considerations or brand voice rules for sensitive tasks.

Python

#H->AI::Directive: (Generate customer service response templates for negative reviews.) # [cite: 21]
#H->AI::Constraint: (Avoid accusatory language, even if the review seems unfair.) # [cite: 22]
#H->AI::Constraint: (Ensure all responses align with our brand voice: empathetic and solution-oriented.) # [cite: 22]
#H->AI::Constraint: (Do not make promises the company cannot keep.) # [cite: 22]

Iterative Refinement Loop: Use a combination of #H->AI::Review:, #H->AI::EvaluateBy:, and #H->AI::Question: to guide the AI through cycles of improvement on its output.

Python

# [AI provides initial draft]
#H->AI::EvaluateBy: (Check against the 'fun, entertaining, informative' style from initial prompt.) # [cite: 26]
#H->AI::Review: (Refine the tone - it's a bit dry. Inject more enthusiasm.) # [cite: 27]
#H->AI::Question: (Can you add a relevant anecdote or example to make point 3 more concrete?) # [cite: 23]

Multi-Perspective Analysis: Instruct the AI to analyze a situation from different viewpoints defined using #H->AI::Perspective:.

Python

#H->AI::Directive: (Analyze the potential impact of launching feature X.) # [cite: 21]
#H->AI::Perspective: (Analyze from the perspective of a long-term power user.) # [cite: 27]
#H->AI::Perspective: (Analyze from the perspective of a brand new user.) # [cite: 27]
#H->AI::Perspective: (Analyze from the perspective of a customer support agent.) # [cite: 27]
#H->AI::Structure: (Organize findings by perspective.) # [cite: 24]

These examples leverage the defined comment types in more combined and nuanced ways to achieve greater control and creativity in your interactions.

Sources and related content

P-CRAFT001...V04282025A

TXT

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

Show thinking

Okay, drawing inspiration from the #AI->H:: comment types defined in the CRAFT specification file (P-CRAFT001_CRAFT-SPEC_V04282025A.txt), here are 10 creative ways I could use this system to communicate back to you:

Proactive Ambiguity Clarification: Instead of just getting stuck, I can pinpoint the ambiguity and suggest options.

Plaintext

#AI->H::Question: (Your directive mentioned optimizing the blog post for "better engagement." Could you clarify if the priority is comments, social shares, time-on-page, or another metric? Knowing this will help me tailor the suggestions more effectively.)

Suggesting CRAFT Framework Optimizations: I can recommend ways to make our interaction more efficient using CRAFT principles.

Plaintext

#AI->H::RecommendMakingThisAVariable: (You've described the 'Alex the AI Trailblazer' persona details multiple times. To save effort and ensure consistency, consider defining this using the `build_persona` function[cite: 49] or as a constant variable?)

Plaintext

#AI->H::RecommendMakingThisAFunction: (The sequence of steps you just outlined for competitor analysis is something we do often. Would you like me to help structure this as a reusable CRAFT function incorporating your steps?)

Indicating Confidence Levels: I can add notes about my confidence in specific parts of the response, especially for predictions or analyses based on incomplete data.
Plaintext
```
[Provides market forecast]
#AI->H::Note: (Confidence in the 5-year growth projection is moderate (7/10) due to rapidly evolving AI regulations.)
```

Highlighting Conflicting Constraints/Goals: I can use warnings to point out where your requests might be creating difficult trade-offs.

Plaintext

#AI->H::Caution: (You requested maximum detail [#H->AI::DetailLevel: (High)] but also a concise summary [#H->AI::Summarize:]. These conflict slightly. I will provide a detailed analysis followed by a separate executive summary. Please advise if you prefer a different approach.)

Suggesting More Effective Roles or Perspectives: If I think a different AI persona or analytical angle would yield better results.

Plaintext

#AI->H::RecommendDiffRole: (For generating truly novel "what if" scenarios as requested, switching my role from 'Prompt Engineering Expert' to 'Creative Futurist' might produce more boundary-pushing ideas. Shall I adopt that role for this task?)

Requesting Mid-Task Feedback: For complex, multi-stage tasks, I can deliver intermediate results and explicitly request your feedback before continuing.

Plaintext

[Presents initial draft of Section 1]
#AI->H::RequestingFeedback: (This is the draft for the introduction based on your outline. Does the tone and focus align with your vision before I proceed to draft the main body sections?)

Providing Explicit Source Attribution: Beyond standard citations, I can note the specific inputs used, especially if you directed me with #H->AI::UseSource:.

Plaintext

#AI->H::Note: (The competitive advantages listed were derived primarily from analyzing the 'competitor_features.csv' file you provided and cross-referencing with recent product announcements found via search.)

Identifying Opportunities for Deeper Analysis: Pointing out areas where more investigation could be valuable.

Plaintext

[Provides website traffic summary]
#AI->H::RecommendMoreDetailHere: (The data shows a significant drop-off on the pricing page. A deeper analysis of user behavior on that specific page (e.g., heatmaps, session recordings if available) or A/B testing pricing presentation could yield valuable insights. Would you like to explore strategies for this?)

Stating Assumptions or Pre-computation Checks: Clarifying the basis for my response upfront.

Plaintext

#AI->H::Note: (Proceeding with the analysis based on the assumption that the Q4 data provided is complete and finalized.)
#AI->H::Caution: (Detected potential outliers in the user survey data provided; these were included in the analysis but may warrant separate investigation.)

Linking Actions to CRAFT Concepts: Explicitly connecting my suggestions or outputs back to the CRAFT framework's philosophy or components.

Plaintext

#AI->H::Note: (This structured output format follows the 'Structure Enables Freedom' principle of CRAFT[cite: 6], aiming to make the information clear and easily reusable for your website content.)

These uses aim to make our collaboration more transparent, efficient, and aligned with the structured approach of the CRAFT framework.

CRAFT™️The CRAFT™️ Experimentweek2