The TXT File: ChatGPT o3: Testing the new CRAFT™️ Experiment Comment System

THE CRAFT™️ EXPERIMENT :: WEEK 2 :: POST 7

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ChatGPT o3: Testing the new CRAFT™️ Experiment Comment System

RESPONSE FROM CHATGPT:

Here are a few points I’d like to clarify before we move on:

1. Specific Task to Solve
   Point 4 indicates that a concrete entrepreneur-oriented task will be provided “following this.” Could you outline that task now so I can focus the upcoming prompt development correctly?

2. Preferred Comment & Markup Conventions
   The CRAFT spec introduces the #H->AI:: and #AI->H:: prefixes and several custom data types.

   • Should I adopt those prefixes (e.g., #AI->H::Note:) and data-type labels in the responses I generate for you, or keep replies in plain prose until explicitly asked to embed CRAFT-style comments? ​

3. Output Format Expectations
   When I deliver prompts, explanations, or code/HTML samples, do you want them:

   • in raw Markdown / plain text,

   • wrapped in CRAFT blocks (e.g., ### START PYTHON CODE BLOCK ### … ### END PYTHON CODE BLOCK ###), or

   • converted to full HTML via the craft_to_html utilities?

Everything else appears clear. Once I have those details, I’m ready to proceed.

Sources

o3

You said:

1. The specific task will be answering my questions about the attached file. 2. Yes, please use the CRAFT spec. 3. wrap in CRAFT blocks. Our first task is to evaluate the file and list 10 creative ways that I might use comments to communicate with you, the A.I.

ChatGPT said:

Thought for 10 seconds

START REGULAR TEXT BLOCK

#AI->H::Structure: (Overview of Creative Comment Uses)

Below are 10 creative ways you can leverage the CRAFT™️ comment system to guide or query me (the AI). Each technique references the prefixes defined in the spec and is followed by a short example. ​

1. Inline Role-Swap Trigger
   Use #H->AI::Role: inside code to switch my persona on the fly.
   Example – ask me to become a security-analyst mid-function.

2. Self-Review Checkpoint
   Insert #H->AI::Review: before a complex block; I’ll audit the logic and suggest fixes.

3. Dynamic Detail Dial
   Vary verbosity with #H->AI::DetailLevel: (e.g., INTERMEDIATE, TECHNICAL).

4. Contextual Source Pinning
   Lock me to specific docs using #H->AI::UseSource: so citations stay on-target.

5. Error Contingency Plan
   Embed #H->AI::OnError: to dictate graceful fallbacks or alt-flows if something fails.

6. Perspective Flip
   Add #H->AI::Perspective: to force a different viewpoint—say, novice vs. expert.

7. Focus Beacon
   Highlight must-address items with #H->AI::Focus: so nothing gets missed in output.

8. Implicit Reasoning Reveal
   Request transparent thinking via #H->AI::Reasoning: to see step-by-step logic.

9. Output Format Switch
   Control layout instantly with #H->AI::Structure: (e.g., ask for a Markdown table).

10. Deduplication Sweep
    Place #H->AI::DeDuplicate: over a block of prompts or data; I’ll return only uniques.

END REGULAR TEXT BLOCK

START PYTHON CODE BLOCK

def analyze_log(file_path): """ Quick demo showing three of the above comment tricks in situ. """ #H->AI::Role: (Act as a cybersecurity incident responder) #H->AI::Focus: (Prioritize detection of suspicious IP addresses) #H->AI::DetailLevel: TECHNICAL suspicious_ips = set()

arduino

CopyEdit

for line in open(file_path): if "Failed login" in line: ip = line.split()[3] suspicious_ips.add(ip) #H->AI::Review: (Evaluate if this detection logic misses brute-force attempts) #H->AI::OnError: (If review fails, suggest a regex-based alternative) return suspicious_ips

END PYTHON CODE BLOCK

Sources

o3

You said:

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

ChatGPT said:

START REGULAR TEXT BLOCK

#AI->H::Structure: (Ten Ways the AI Can Leverage CRAFT-Style Comments)

Below are 10 creative patterns I can employ—using the #AI->H:: family of comments—to keep our collaboration transparent, efficient, and fun.

1. Real-Time Risk Alerts
   #AI->H::Caution: — I flag security, compliance, or factual-accuracy concerns before you act on a suggestion.

2. Targeted Improvement Suggestions
   #AI->H::RecommendedChange: — I highlight a precise snippet (text, code, or prompt) and propose a refined replacement in situ.

3. Clarification Requests
   #AI->H::Question: — When context is missing, I embed a focused query so you can answer inline without derailing flow.

4. Self-Assessment & Revision Loops
   #AI->H::Review: — I critique my own draft against stated criteria, revise it, then present both the critique and the polished version.

5. Adaptive Role Guidance
   #AI->H::RecommendDiffRole: — I suggest switching my persona (e.g., from “Prompt Engineer” to “UX Copywriter”) to better fit the next task.

6. Granularity Nudges
   #AI->H::RecommendMoreDetailHere: — I mark sections that would benefit from deeper explanations, additional examples, or data citations.

7. Function Extraction Hints
   #AI->H::RecommendMakingThisAFunction: — While reviewing Python, I signal repetitive logic ripe for refactoring into a reusable function.

8. Object-Oriented Refactors
   #AI->H::RecommendMakingThisAnObject: — I identify clusters of related data/behavior and propose a clean class structure.

9. Progress Beacons
   #AI->H::Status: — Periodically, I output succinct status checkpoints (e.g., “50 % through data-cleaning step; no errors so far”).

10. Feedback Invitations
    #AI->H::RequestingFeedback: — I explicitly pause to ask for your thoughts on tone, depth, or direction, fostering true co-creation.

END REGULAR TEXT BLOCK

START PYTHON CODE BLOCK

def sample_workflow(data): """ Demo function illustrating several AI->H comment types in action. """ processed = []

rust

CopyEdit

#AI->H::Status: (Starting normalization loop) for record in data: # … processing logic … processed.append(record.upper()) #AI->H::Review: (Did we handle unicode edge cases? If not, flag) #AI->H::RecommendMakingThisAFunction: (Extract line-by-line transform into normalize_record()) if len(processed) < 5: #AI->H::Caution: (Very small sample size; insights may be unreliable) pass #AI->H::RequestingFeedback: (Should output be CSV, JSON, or printed summary?) return processed

END PYTHON CODE BLOCK

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