AI applications are easy to prototype. The hard part is proving they are reliable.
If you are building LLM applications, RAG systems, AI agents, copilots, chatbots, or production AI workflows, you already know the problem: a model can sound confident and still be wrong. A retrieval system can cite the wrong source. A prompt update can silently break previous behavior. An AI agent can call the wrong tool, skip approval, expose private data, or take an action it should never have taken.
AI Testing and Evaluation Engineering gives you the practical discipline needed to move from impressive AI demos to dependable production systems.
This book is a hands-on guide to testing, evaluating, securing, monitoring, and improving modern AI applications. It shows how to build reliable LLM applications, evaluate RAG pipelines, test AI agents, design automated evals, measure quality, detect regressions, red-team security risks, apply guardrails, monitor production behavior, and connect AI evaluations to CI/CD workflows.
Instead of treating AI quality as a vague feeling, this book teaches you how to measure it with structure, evidence, and repeatable engineering workflows.
Inside, you will learn how to:
- Build automated evaluation pipelines for LLM applications
- Create golden datasets and realistic AI test scenarios
- Measure correctness, relevance, groundedness, factuality, and citation accuracy
- Test prompt changes and detect AI regression before release
- Evaluate RAG retrieval quality, chunking, embeddings, hybrid search, and reranking
- Test AI agents for planning, tool use, memory, permissions, approvals, and recovery
- Red-team prompt injection, jailbreak attempts, data leakage, and agent vulnerabilities
- Design guardrails for safer input, output, retrieval, and tool execution
- Monitor AI quality, cost, latency, usage, traces, incidents, and production failures
- Integrate AI tests into CI/CD pipelines with release gates and version control
- Build complete AI reliability platforms for real-world production systems
This is not another beginner guide to making AI apps. It is a professional engineering reference for the next phase of AI development: making AI systems trustworthy enough to use in real products, real teams, and real business workflows.
Whether you are an AI engineer, software developer, ML engineer, QA tester, data scientist, MLOps practitioner, technical lead, or founder building with generative AI, this book will help you answer the questions every serious team must face:
Can your AI system be tested?
Can its failures be measured?
Can its outputs be trusted?
Can its agents be controlled?
Can its behavior be monitored after deployment?
If you want to build AI applications that are not only powerful, but reliable, secure, measurable, and production-ready, get your copy of AI Testing and Evaluation Engineering today.
