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What the interviewer is testing
Whether you evaluate the application on your task instead of quoting public benchmarks, can build an eval set, make an LLM judge trustworthy, and wire evaluation into releases.
Answer framework
- Say why it is hard: open-ended outputs, many correct answers, several quality dimensions, and behavior that shifts with small changes.
- Build a task-specific eval set: real or realistic inputs covering common cases, edge cases and known failures, with expected outputs or grading criteria, versioned, starting at 50 to 200 examples, with a held-out portion.
- Choose graders: exact match and programmatic checks first, then a calibrated LLM judge, then human review.
- Make the judge reliable: precise rubric, reference answers, justification before score, pairwise comparison, order swapping, calibration against human labels.
- Run offline evals as regression tests in CI, and measure online with A/B tests, feedback and task completion.
Sample answer
Evaluating LLM applications is hard because outputs are open-ended, many answers can be correct, quality has several dimensions (correctness, faithfulness, tone, safety), and small prompt or model changes shift behavior. So I build a task-specific eval set instead of relying on public benchmarks.
I start from real or realistic inputs, cover common cases, edge cases and known failures, write expected outputs or grading criteria for each, and version it. I start with 50 to 200 examples, grow it from production failures, and keep a held-out portion that is never used for prompt tuning.
For grading I use exact match and programmatic checks where I can, and an LLM judge where I cannot. The judge gets a precise rubric and a reference answer, gives a short justification before the score, and I swap answer order to control position bias and calibrate against human labels.
Offline, the suite runs in CI on every prompt, model or retrieval change and blocks the change if key metrics drop beyond a threshold. Online, I measure live behavior through A/B tests, user feedback, task completion and sampled reviews. Offline gates releases; online tells me whether users are actually better off.
Likely follow-ups
Common mistakes
- Answering with public benchmark scores instead of a task-specific eval set.
- Tuning prompts on the same examples used to report results, with no held-out portion.
- Trusting an LLM judge that was never calibrated against human labels.
- Running evals by hand occasionally instead of on every change.
- Stopping at offline metrics without checking whether users are better off.
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Related questions
Based on question #61, #62, #63, #64, #69, #72 in the full question bank.