Why AI-generated bugs feel different — and mostly aren't
There's a specific frustration to debugging code you didn't write yourself line by line: it's harder to build an intuition for where the bug probably is. But the underlying discipline is the same as debugging any code — reproduce it, isolate it, understand it, then fix it — and skipping straight to "re-prompt and hope" is what actually makes AI-generated bugs feel harder than they are.
Step 1: reproduce before you re-prompt
Before asking the AI to fix anything, make sure you can reliably trigger the bug yourself. "It's broken" is not reproducible; "clicking submit with an empty email field shows no error and silently does nothing" is. If you can't reproduce it consistently, any "fix" you get is unverifiable — you'll just be trusting that it worked.
Step 2: read the actual error, not just the symptom
It's tempting to describe what you observed ("the page is blank") rather than what actually failed (a specific error in the console, a specific failing line). Open the browser console, the terminal output, or the test failure and read the real message before doing anything else — it usually narrows the problem far more than the visible symptom does.
Step 3: use the model to narrow the search, not to guess blindly
A strong debugging prompt gives the AI the actual error, the relevant file or function, what you expected, and what's actually happening — this is the same principle covered in Best prompts for AI coding. Ask it to explain what it thinks is happening before asking for a fix; if the explanation doesn't match the symptom you're seeing, that's a sign to push back before accepting a change.
Step 4: isolate with a test rather than trial and error in the full app
Where possible, reproduce the bug in the smallest possible piece of code — a single function or component — rather than clicking through the entire application after every attempted fix. This is faster to iterate on and makes it much easier to confirm a fix actually addressed the root cause rather than just changing the symptom you happened to be looking at.
When to stop trusting a fix and start over
If you're three or four attempted fixes deep and the bug keeps mutating into a slightly different bug, that's usually a sign the underlying approach — not just the specific line — is wrong. It's often faster at that point to step back, describe the actual requirement freshly, and let the AI (or you) reconsider the approach, rather than patching an approach that's fundamentally off.
Building habits that prevent the worst bugs
- Keep changes small enough that you can tell which recent edit caused a new problem
- Commit working states before asking for a risky change, so you have a clean rollback point
- Ask the AI to add a test for a bug once it's fixed, so it can't silently reappear
- Treat repeated failures on the same fix as a signal to change approach, not effort