Define what good means for this task before generating anything, then build a fixed evaluation set of real inputs with rubric scored outputs. Combine automated checks (factual consistency against the source, format validity, refusal and latency rates) with sampled human review. Ship behind a flag, measure downstream user behavior, and keep the eval set as a regression suite for model version changes.
Why interviewers ask this
LLM features are routine now, and the failure mode is shipping on vibes after a few impressive demos. Interviewers want the same rigor you would apply to any model: a held out set, a defined rubric, automated plus human evaluation, and a downstream business metric. Mentioning a regression suite for provider and version changes shows you understand these systems move underneath you.
How to structure your answer
- Define the quality rubric before looking at any outputs.
- Build a fixed evaluation set from real user inputs.
- Combine automated checks with sampled human grading.
- Tie it to a downstream behavior metric behind a flag.
- Keep a regression suite for provider and version changes.
Example answer
The trap here is that the first ten outputs always look amazing and everyone wants to ship. So before I generate anything, I write down what good means: faithful to the source document, covers the key points, right length, no invented facts. Then I pull a couple hundred real inputs, weighted toward the hard cases rather than the pretty ones, and freeze that as an evaluation set. On the automated side I check factual consistency against the source, format validity, and refusal rate, and I use a model as judge for the rubric scores, but only after calibrating it against human grades on a subset, because an ungrounded judge mostly agrees with itself. A human still grades a sample every cycle. Then I ship behind a flag to a slice of traffic and watch what users actually do: do they edit the summary, do they open the full document anyway. That downstream behavior is the real metric. And I keep the eval set as a regression suite, because when the provider updates the model underneath you the behavior shifts, and you want to learn that from your own tests rather than from support tickets.
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See how it worksFollow-up questions to expect
- How would you validate that a model as judge agrees with human graders?
- What would make you roll the feature back after launch?
- How do you estimate the cost per request before committing to it?
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