How do you decide how much to trust AI-generated output? Walk me through the way you balance moving fast against verifying, and give me a concrete example of a time your verification caught something the model got wrong.
The interviewer is looking for: a personal framework tied to stakes and how cheaply the output can be verified, a real caught mistake, the practices you rely on to catch problems, and the lines you have drawn about what you will and will not send to an external model.
How to approach it
- Hint 1
This is a values-style question, not a story prompt -- the interviewer wants your operating framework first and then evidence that you actually live by it. The underlying assessment is Trust: whether speed has made you sloppy, and whether you have a repeatable way of deciding what needs checking.
- Hint 2
Build the framework from what actually varies in your own work, then split it into two or three named buckets. The axis usually sits in one of a few places: how cheaply the output can be checked, how much context the task needs that only you have, how far the blast radius reaches if a mistake ships, how reversible the change is. Pick the one you genuinely operate on rather than the one that sounds best. Then choose one example where a check caught something real -- a deprecated internal API, a legacy pattern the model had never seen, a subtly wrong domain assumption, a confidently invented function. Also have a data line ready: what you will not paste into an external model.
- Hint 3
Structure it as framework then example, and keep the framework to two or three named buckets so it stays speakable. Name the mechanisms, do not gesture at them -- the tests you write before accepting a change, the diff review you insist on, CI gates, a staging soak. End on the concrete catch and what you changed in your loop because of it, so it lands as practice rather than philosophy.
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