Build evaluation systems that reveal whether your LLM applications are improving or regressing, using calibrated judges, RAG and agent metrics, CI regression tests, online evals, and cost-aware pipelines Key Features
Build a complete Python eval harness from datasets and scorers to CI and dashboards
Evaluate RAG, agents, and prompts with calibrated metrics and human feedback
Move from offline testing to production evals, guardrails, and reliability workflows
Book Description LLM applications can look healthy in dashboards while their answers quietly become less accurate, less useful, or less reliable. AI Evals in Practice gives developers and AI engineers a systematic way to measure quality, catch regressions, and make evidence-based improvements before and after deployment. You will build evalkit, a complete Python evaluation harness, while learning the core components of an eval: datasets, scorers, runners, and golden test sets. You will create deterministic scorers and LLM-as-judge evaluations, then calibrate judges and mitigate common biases. The book applies these foundations to prompt regression testing and CI, RAG retrieval and generation metrics, agent trajectories and tool calls, and human annotation workflows. You will then extend evaluation into production with online sampling, guardrails, and cost- and latency-aware pipelines, while comparing tools such as DeepEval, promptfoo, Langfuse, and Braintrust. A case study and final project bring the pieces together into an eval-driven development workflow with CI and a dashboard. By the end, you will be able to design evaluation pipelines that help you ship LLM systems with measurable, repeatable quality. What you will learn
Design eval datasets, scorers, runners, and golden test sets
Build deterministic metrics for repeatable quality checks
Calibrate LLM judges and reduce common evaluation bias
Add prompt regression tests to continuous integration
Measure retrieval and generation quality in RAG systems
Evaluate agent trajectories, tool calls, and multi-turn behavior
Run human annotation workflows and production online evals
Control evaluation cost and latency without losing signal
Who this book is for This book is for AI engineers, LLM application developers, machine learning engineers, platform engineers, and technical leads who build or operate production systems using large language models. It is especially useful for teams working with prompts, RAG pipelines, agents, or AI features that need measurable quality and regression protection. Readers should be comfortable with Python and familiar with building or integrating LLM applications.
