Evals That Don't Lie: Engineering trustworthy LLM evaluation pipelines with golden datasets, judges, and regression gates

Stop shipping LLM features on vibes.Are you tired of deploying prompts only to have users immediately discover glaring bugs? Evals That Don't Lie is the definitive, production-tested playbook for building robust evaluation pipelines that accurately predict real-world quality. Written specifically for AI, ML, and product engineers, this book treats evaluation as a rigorous software engineering discipline rather than an academic exercise.In this book, you'll discover how to transition from vibes-based development to data-driven confidence. You'll master the practical techniques needed to scale automated testing, calibrate judges, and protect your user experience from regression.Inside, you will learn how to: - Build an actionable failure taxonomy that categorizes errors and guides immediate engineering fixes.- Construct and maintain golden datasets that accurately reflect production distribution without contamination.- Design reliable LLM-as-judge systems with calibrated rubrics and Cohen's kappa above 0.7.- Run human-in-the-loop calibration sessions to achieve high reviewer agreement.- Integrate regression gates into CI/CD that run in under ten minutes to block broken releases.- Evaluate complex agents, multi-turn dialogues, and tool-use trajectories using milestone grading.- Manage pipeline economics, minimizing latency and API costs while operating at scale.Stop guessing if your fine-tuned model or updated system prompt is actually better. Implement the engineering practices used by top AI teams to deploy LLM applications with total confidence. Get your copy of Evals That Don't Lie today and start shipping with certainty.