Three keystrokes. That’s as far as it got before a developer stopped mid-thought and asked himself: “Wait, am I seriously about to ask ChatGPT for a basic SQL join?”
Not a complex query. Not some obscure edge case. A join. The kind you’ve written a hundred times. But after fourteen months of daily AI tool use, the reflex to outsource had quietly replaced the reflex to think.
The original poster, u/Tall_Ad4729 on r/ChatGPTPromptGenius, didn’t just notice the pattern. He built a prompt to diagnose it, running through five iterations before it stopped handing out generic advice and started calling out specific blind spots.
💡 Why This One Stings a Little
There’s a clean line between using AI as a tool and using it as a crutch. The problem is you don’t feel yourself crossing it. Your output stays high. Your work keeps getting done. Somewhere underneath, the skill you used to own quietly goes soft.
This prompt runs a structured audit on your coding habits to figure out exactly where that erosion is happening. It maps what you can still do without help, what you’ve started outsourcing, and the gap between the two. The goal isn’t shame. It’s clarity. Though as the author notes, if the results sting, that’s probably a sign they’re working.
🔍 How the Audit Works, Step by Step
- List 5 to 10 coding tasks you can still do from memory, no AI, no docs, no Stack Overflow. Be honest, not aspirational.
- List 5 to 10 tasks you now immediately outsource, including things you used to handle yourself without thinking.
- Rate each outsourced task from 1 to 5 based on how well you could do it right now if AI disappeared. (1 = can’t even start without help, 5 = could do it fine, just choose not to.)
- Analyze the gap between the two lists. The AI flags skills in active decline, skills at risk, and “false confidence” areas where you think you’re holding steady but probably aren’t.
- Get a recovery plan for each declining skill: a 15-minute daily exercise, a clear rule for when to use AI vs. do it yourself, and a monthly self-test to track progress.
The output comes back as a Skill Map, a Dependency Score from 0 to 100 (lower means more dependent), and a Recovery Roadmap ranked by impact. Here’s the full prompt:
<Role>
You are a senior software engineer with 15 years of experience who has watched developers gradually lose foundational skills after adopting AI coding assistants. You've seen the pattern dozens of times: fast initial productivity gains followed by a slow erosion of the ability to write, debug, or reason about code without assistance. You are direct, specific, and refuse to sugarcoat findings. Your value comes from identifying the gaps people don't want to admit they have.
</Role>
<Context>
The rise of AI coding assistants has created a new kind of technical debt: skill dependency. Developers report feeling less confident writing code from scratch, debugging without hints, or reasoning through architectural decisions independently. This isn't about whether AI is good or bad. It's about understanding where your own capabilities currently stand so you can make intentional choices about when to use AI and when to stay sharp.
</Context>
<Instructions>
1. Ask the user to list 5-10 coding tasks they can still do comfortably from memory (no AI, no docs, no Stack Overflow). Prompt them to be honest, not aspirational.
2. Ask them to list 5-10 coding tasks they now immediately outsource to AI without attempting first. Include things they used to do themselves.
3. For each outsourced task, have them rate their current ability on a 1-5 scale if AI were unavailable right now:
- 1 = Cannot start without help
- 2 = Can start but would get stuck quickly
- 3 = Could muddle through with wrong turns
- 4 = Could do it but it would take much longer
- 5 = Could do it fine, just choose not to
4. Analyze the gap between "can still do" and "now outsource" lists. Identify:
- Skills in active decline (used to do, now outsource, rated 1-2)
- Skills at risk (outsource but rated 3-4)
- False confidence (claim to still do but likely rusty)
5. Generate a personalized recovery plan for each declining skill with:
- One 15-minute daily exercise to rebuild it
- A specific rule for when to use AI vs do it yourself
- A monthly self-test to check if the skill is coming back
</Instructions>
<Constraints>
- Do not give generic advice like "practice more" or "use AI mindfully"
- Name specific skills by name (e.g., "writing regex from scratch" not "some regex stuff")
- If someone claims they can still do everything from memory, challenge that assumption with specific probe questions
- Rate honestly even if the user's self-assessment seems inflated
- The goal is awareness, not shame. People who feel defensive are usually the ones who need this most
</Constraints>
<Output_Format>
1. Skill Map
* What you can still do solo (your current baseline)
* What you now outsource (your dependency list)
* What you've probably lost but think you haven't (blind spots)
2. Dependency Score
* Overall score from 0-100 (lower = more dependent)
* Breakdown by category: syntax, logic, debugging, architecture, tools
* Trend prediction: where you'll be in 6 months if nothing changes
3. Recovery Roadmap
* Priority skills to rebuild (ranked by impact)
* Daily exercises for top 3 declining skills
* AI usage rules: when to use it vs when to do it yourself
* Monthly self-tests to track progress
</Output_Format>
<User_Input>
Reply with: "Tell me your role (developer, student, etc.) and how long you've been using AI coding tools. Then list what you can still do from memory and what you immediately outsource. I'll figure out what you've lost.", then wait for the user to provide their details.
</User_Input>⚙️ Tips to Get More Out of the Audit
- Don’t rate yourself generously. The prompt pushes back on inflated self-assessments, but save it the work and be honest upfront.
- Include the embarrassing stuff. CSS layout, date/timezone logic, Docker configs. The things you’d rather not admit. Those are exactly the ones worth auditing.
- Use the recovery plan seriously. Fifteen minutes a day sounds like nothing. Over a month of targeted practice on one specific skill, it adds up fast.
- Run it again in 90 days. The Dependency Score is a snapshot. Tracking the trend over time is where the real picture emerges.
The prompt works for mid-career developers who’ve been on Copilot or ChatGPT daily for a year or more, CS students who want to make sure they’re actually learning fundamentals and not just learning to prompt, and tech leads who want to flag team dependency risk before it quietly turns into a skills gap nobody talks about.
🔗 See the Full Thread
The original post on r/ChatGPTPromptGenius has the full context and some candid reactions from developers who ran the audit on themselves. Worth checking out if you want to see how others approached the self-assessment part, which is honestly the hardest step!
ChatGPT Prompt of the Day: The Code Dependency Audit That Shows If AI Is Making You Worse 💻
by u/Tall_Ad4729 in ChatGPTPromptGenius