10 ChatGPT & Claude Prompts for Debugging Code Faster
"Why doesn't this work" wastes a turn. These ten prompts are structured around what actually needs to be true for AI-assisted debugging to work — evidence first, guessing last.
The single biggest lever in AI-assisted debugging isn't which model you use — it's whether you paste real evidence (the actual error, the actual code) or describe the problem from memory. Every prompt below assumes you're pasting evidence. Adapt the bracketed parts to your situation.
1. Root-cause a specific error
Here is the full error/stack trace, unedited: [paste]
Here is the code it points to, plus its immediate callers: [paste]
What I was doing when it happened: [one sentence]
Based only on this evidence, identify the most likely root cause and the minimal fix. If you need more information to be confident, say what would confirm it instead of guessing.2. Explain an error message in plain terms
I got this error and don't fully understand it: [paste error]
Context: I was [what you were doing].
Explain: (1) what this error means in one sentence, (2) the most likely cause given my context, (3) exact steps to try, in order.3. Find a race condition or concurrency bug
This method is called from multiple threads/workers concurrently: [paste code]
Walk through step by step what happens if two calls execute at the exact same time. List every piece of shared state touched, then state whether this is thread-safe and why.4. Compare working vs. broken versions
This code worked before a recent change and broke after. Here's the diff: [paste diff]
Explain specifically which part of this diff caused the regression and why, referencing the exact lines — not a general explanation of what the diff does.5. Diagnose a memory leak
Memory dump shows [N] live instances of [Type], growing over time. GC root path: [paste root path from your memory profiler]
Relevant code: [paste]
Explain the retention path based only on this evidence, and propose a fix that addresses the actual reference lifecycle, not just a symptom.6. Get unstuck without giving away the whole problem
I'm stuck on this bug and want to keep working it myself. Here's the code and the symptom: [paste]
Give me one specific area to investigate next, and why — not the full diagnosis or fix.7. Verify a fix is actually correct
Here's the original buggy code, the bug, and my proposed fix: [paste all three]
Verify: does this fix actually address the root cause? Are there edge cases my fix doesn't handle? Could this fix introduce a new problem elsewhere?8. Reproduce an intermittent bug
This bug happens intermittently, maybe 1 in 20 runs. Here's the code and what varies between runs (input data, timing, concurrency): [paste]
List the specific conditions that would need to align for this to fail, ranked by likelihood, so I can try to force a reproduction.9. Understand a legacy function nobody remembers writing
This function has no documentation and the original author is gone: [paste code]
Explain what it does step by step, then flag anything that looks like it might be a workaround for a bug elsewhere, versus intentional business logic — and why you think so.10. Post-mortem: what actually went wrong
Here's the timeline of what happened during this incident: [paste timeline/logs]
Summarize: root cause, contributing factors, and 2-3 concrete follow-up actions that would prevent this specific failure mode from recurring — not generic advice like 'add more tests.'Every one of these pastes real evidence and constrains the model against guessing beyond it. That combination — evidence plus an explicit "don't speculate beyond this" constraint — is what separates useful AI-assisted debugging from confident-sounding guesses.
The Existing Project Advanced program covers evidence-first debugging as a full method, including deadlock investigation and memory analysis with real thread-dump and heap-dump workflows, not just the compressed prompt templates above.