Question
You need to generate documentation strings for ~8,000 internal functions overnight — no human is waiting, results land in a PR people review in the morning. Separately, you maintain an interactive 'explain this function' button in the IDE. A teammate uses identical model settings (same tier, same low max-tokens, streaming on) for both. What would you tune differently between these two jobs, and why?
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.