AI Evals Engineering in Practice: Test, Benchmark, and Improve LLM Applications, RAG Pipelines, and AI Agents Before Production Your AI application works in a demo. But how do you know it will work reliably in production? An LLM can generate convincing but incorrect answers. A RAG pipeline can retrieve irrelevant documents, produce unsupported claims, or miss critical information. An AI agent can select the wrong tool, execute unauthorized actions, or report success when the task actually failed. For AI engineers, software developers, and technical teams, the challenge is clear: How do you measure AI quality, identify failures, and prevent costly regressions before deployment? Without reliable AI evals, every prompt change, model upgrade, or agent modification introduces uncertainty. Traditional unit tests alone cannot adequately measure the behavior of complex, AI-powered applications. AI Evals Engineering in Practice provides a practical, code-driven approach to building reliable evaluation systems for LLM applications, retrieval-augmented generation (RAG) pipelines, and AI agents. Through progressive hands-on projects, executable Python examples, evaluation frameworks, and automated testing workflows, you'll learn how to transform subjective AI quality concerns into measurable engineering decisions. Inside, you'll learn how to: Build automated AI evaluation pipelines using Python, pytest, DeepEval, and reusable test cases to detect incorrect outputs and prevent regressions. Measure and improve RAG accuracy by evaluating retrieval quality, answer faithfulness, groundedness, and hallucination risks using practical metrics and Ragas. Test AI agents beyond their final answers by verifying tool selection, permissions, execution traces, task completion, and actual system-state changes. Benchmark LLM applications systematically by comparing model responses, prompt versions, accuracy, latency, costs, and evaluation reliability. Automate production quality checks with GitHub Actions, security evaluations, Promptfoo, CI/CD release gates, and observability workflows. You'll also learn how to build golden datasets, calibrate LLM-as-judge evaluations against human assessments, diagnose failed tests, and determine whether an application change represents a genuine improvement. Rather than presenting disconnected framework demonstrations, this book develops three connected engineering projects: an automated LLM evaluation harness, a RAG testing and improvement pipeline, and an AI agent production-readiness system. You'll work with reproducible test fixtures, deliberately broken application behaviors, practical evaluation metrics, security checks, and working code examples that demonstrate how failures are detected and investigated. The book distinguishes deterministic tests from evaluations requiring live AI models, helping you understand both the strengths and limitations of your results. By the final chapter, you'll understand how to assemble an evaluation workflow that supports measurable quality improvements, regression prevention, and evidence-based deployment decisions. Stop relying on successful demos as proof of reliability. Start engineering AI applications you can test, measure, and improve with confidence. Get your copy of AI Evals Engineering in Practice and build the evaluation skills needed to move AI systems toward production with greater confidence.
