"DeepEval: Building an Automated LLM Evaluation Harness That Engineers Trust"
LLM applications rarely fail in obvious ways, which is why intuition, demos, and scattered prompt tests are not enough for serious engineering teams. This book is written for experienced practitioners who need a disciplined, automated way to evaluate model behavior they can defend in code review, CI pipelines, and release decisions. It frames DeepEval not as a scoring gadget, but as an engineering trust system for production-grade AI.
Across the book, readers learn how to model evidence with test cases, datasets, and goldens; choose metrics that match real failure modes; and understand the mechanics and limits of LLM-as-a-judge evaluation. It dives deeply into RAG assessment, multi-turn conversations, and agentic workflows, then shows how to scale evaluation with synthetic data, simulation, CI/CD integration, historical comparison, calibration, and failure analysis. The result is a practical blueprint for building eval suites that are reproducible, diagnosable, and operationally credible.
The treatment is advanced, implementation-aware, and organized around the decisions engineers actually face when shipping LLM systems. Readers should already be comfortable with modern LLM application architecture, testing practices, and production workflows. What distinguishes this book is its emphasis on trustworthiness: not merely how to run evaluations, but how to design an evaluation program whose results engineers will continue to believe.
