Question
You ask an AI for a Python regex to extract @mentions from chat messages so you can notify users. It returns `re.findall(r'@(\w+)', text)`. It pulls names fine until QA reports notifications firing from email addresses (`@gmail` in `a@gmail.com`), code blocks (`@property`), and it misses `@jean-luc` and `@O'Brien`. You re-prompt 'be more accurate' and it adds `\b`, which fixes nothing. How do you re-steer this to actually match your product's mention rules?
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it — tests, edge cases, reading critically — and how you’d re-prompt or decompose to get it right.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.