Building AI Evals Proven Techniques to Continuously Test, Monitor & Improve LLM Systems

What's the one thing that separates an AI system you can trust from one you hope won't break? It's not the number of parameters, the size of the dataset, or the flashiest benchmark scores-it's the discipline of relentless, real-world evaluation.
Building AI Evals is the developer's guide to making large language models robust, auditable, and production-ready. Written with hands-on energy, this book equips you to move beyond one-off tests and static metrics. Whether you're refining retrieval-augmented generation pipelines, integrating agents with complex tool use, or deploying LLMs at scale, this book gives you practical frameworks to build continuous, automated, and actionable evaluation systems from the ground up.
Cut through the noise and tackle real engineering challengesesign golden datasets that adapt as your product evolvesImplement rigorous, reproducible evaluation pipelines with proven open-source toolsMonitor cost, quality, and safety metrics that matter in real production environmentsAutomate judge logic, rubric scoring, and red-team sweeps to catch failures before users doIntegrate CI/CD for fast, auditable feedback on every changeTransform production failures into golden test cases for continuous improvementInside, you'll master field-tested techniques for:Setting up evaluation harnesses that actually scaleWriting and calibrating rubrics as codeSlicing and dashboarding observability data to guide developmentKeeping your release process audit-ready and cost-efficientApplying lessons from real-world case studies-including support automation, contract review, and fail-safe enterprise deployment

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