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LLM Evals and AI Test-Suite Quality
19.1🔒Two Evaluation Problems, One Skill SetBy mid-2026, QA job postings routinely list LLM eval design as a core competency, alongside RAG metrics and frameworks like Ragas, TruLens…
19.2🔒Measuring AI Test-Suite QualityThe review checklist (Chapter 8), the failure modes (Chapter 9), and the quality gates (Chapter 11) are per-test judgments. At the suite…
19.3🔒Prompts Are Code -- Test Them Like CodeYour prompt templates determine the quality of every suite the team generates. A silent regression in a template (or a model upgrade that…
19.4🔒LLM Evals: Testing the AI Inside Your ProductWhen the product embeds an LLM, assert response == expected is dead on arrival: the same input can produce different valid outputs. The…
19.5🔒RAG-Specific MetricsRetrieval-augmented features fail in two distinct places, so you measure two distinct stages:
19.6🔒Q&ASelf-Assessment Quiz1. Which three suite-level metrics measure the quality of AI-generated tests, and what does each catch that the others miss? 2. Why should…
19.7🔒Key Takeaways- Evaluation is the through-line of AI-era QA: one discipline, two targets -- the tests AI writes for you and the AI inside your product…
19.8🔒ExercisesExercise 19.1 (Beginner): Take one AI-generated test file from a previous chapter's exercise and compute its mutation score with mutmut or…
19.9🔒Career Translation- Established suite-level quality measurement for AI-generated tests (mutation score, per-session coverage deltas, flake rate), ensuring…
19.10🔒Q&AInterview Depth CheckPrompt: Your team adopted AI test generation six months ago. Leadership asks: "Is our test suite better or worse for it?" How do you answer…