Overview

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 challenges: Design golden datasets that adapt as your product evolves Implement rigorous, reproducible evaluation pipelines with proven open-source tools Monitor cost, quality, and safety metrics that matter in real production environments Automate judge logic, rubric scoring, and red-team sweeps to catch failures before users do Integrate CI/CD for fast, auditable feedback on every change Transform production failures into golden test cases for continuous improvement Inside, you'll master field-tested techniques for: Setting up evaluation harnesses that actually scale Writing and calibrating rubrics as code Slicing and dashboarding observability data to guide development Keeping your release process audit-ready and cost-efficient Applying lessons from real-world case studies-including support automation, contract review, and fail-safe enterprise deployment Are you ready to build LLM systems that perform, improve, and stand up to scrutiny? Take the step from hopeful launches to confident releases-grab your copy of Building AI Evals and start engineering with certainty today.

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