9.7Key Takeaway

Evaluation is the through-line of AI-era QA. Measure AI-generated suites with mutation score, coverage deltas, and flake rate so "the AI wrote lots of tests" never substitutes for "the suite catches bugs." Treat prompts as code: versioned, reviewed, and regression-tested against golden inputs. And when the product itself contains an LLM, bring eval frameworks (Ragas, TruLens, OpenAI Evals) and stage-level RAG metrics (Precision@K, Recall@K, grounding, citation accuracy) -- the QA engineer who can design these evals is doing the same job as always: turning "it seems fine" into a measured, gated, repeatable answer.