9.6Interview Talking Point

"I treat evaluation as one skill applied to two targets. For AI-generated test suites, I track mutation score, coverage delta per generation session, and flake rate -- coverage tells me what the tests touch, mutation score whether they'd catch a bug there, flake rate whether I can trust the signal. For LLM-backed features, I design evals instead of exact-match assertions: a golden dataset, scored metrics with thresholds, run in CI on every prompt or model change. For RAG features I measure the stages separately -- Precision@K and Recall@K for retrieval, grounding and citation accuracy for generation -- because the fix for a retrieval failure is completely different from the fix for a hallucination. And I version and regression-test the prompts themselves, because a prompt change is a behavior change."