AI Testing Handbook: Evaluation and Confidence Engineering for Modern Systems

AI Testing Handbook: Evaluation and Confidence Engineering for Modern AI What happens when an AI application performs perfectly during development but starts hallucinating, retrieving the wrong information, or failing when real users depend on it? How do you know when an AI system is truly ready to ship? AI Testing Handbook: Evaluation and Confidence Engineering for Modern AI is a practical guide to testing, evaluating, and improving modern AI applications. It teaches you how to move beyond guesswork and use measurable evidence to determine whether LLMs, RAG systems, AI agents, and production AI applications are accurate, reliable, secure, and ready for deployment. Inside this book, you'll learn how to: Build effective AI evaluation strategies and datasets Choose meaningful metrics and quality standards Test LLM accuracy, consistency, and reliability Detect hallucinations and improve grounded responses Evaluate retrieval and end-to-end RAG performance Test AI agents, tool calls, and multi-step tasks Use human reviewers and LLMs as evaluation judges Build release gates and confidence scorecards Test safety guardrails and prompt injection risks Monitor production AI and detect quality drift Automate and scale AI evaluation workflows The book also includes practical examples, working code where needed, evaluation templates, checklists, confidence scorecards, and real-world projects that help turn theory into practical skills. Whether you're an AI engineer, software developer, QA professional, machine learning engineer, data scientist, technical lead, or student, you'll learn how to identify AI failures earlier, improve reliability, reduce costly production mistakes, and make stronger deployment decisions based on evidence rather than assumptions. AI is rapidly moving from experimentation into production. Knowing how to build an AI application is no longer enough. Companies increasingly need professionals who can test AI quality, evaluate reliability, detect failures, monitor performance, and determine whether an application is truly ready for real users. Developing these skills now can help you stay prepared as AI engineering continues to mature. Don't wait until production failures expose problems that better testing could have caught. Get your copy of AI Testing Handbook today and learn how to test smarter, measure what matters, and build modern AI with confidence.