A practical guide to designing LLM evaluation strategies for production AI systems. Covers starting from failure modes rather than generic metrics, building labeled golden datasets, combining deterministic and model-graded checks, including cost and latency as release criteria, running evals in CI with tiered suites, and maintaining eval datasets as products evolve. Includes concrete examples of failure-mode-to-eval mapping tables, LLM-as-judge rubrics, CI result comparisons, and a 10-step checklist.
Table of contents
How to Pick the Right LLM EvalsStart with failure modes, not generic metricsPick evals based on the job your LLM performsDo not evaluate only happy-path examplesBuild a labeled golden datasetUse model graders carefullySeparate deterministic checks from judgment-based checksInclude cost and latency in your eval suiteVersion prompts and eval criteria togetherRun evals in CI before prompt and model changes shipUse dashboards that compare versionsChoose thresholds based on riskRefresh evals with production dataA practical checklist for picking LLM evalsThe right evals make failures actionable39 Impressions