Overview

Do you use ChatGPT for research? GitHub Copilot for coding? Claude for planning? Then you've probably wondered: ""Wait, is this output actually right?"" Large language models are powerful but unpredictable. They hallucinate facts, flip conclusions on paraphrased inputs, and confidently fabricate citations. A major airline faced legal action when their chatbot gave wrong policy information. Lawyers received sanctions for submitting AI-generated fake case citations. Traditional testing doesn't work because there's no ""one right answer."" This book solves that problem with practical, proven testing techniques you can use today-whether you're chatting with AI for summaries, coding with AI assistants, or building autonomous agents. WHAT YOU'LL LEARN: - How to catch hallucinations before they reach production - Evidence-anchoring: making AI cite its sources (and verify them automatically) - Property tests that work without ground truth - Self-consistency checks for high-stakes decisions - Cost-aware testing strategies that fit your budget - Statistical methods explained in plain English (no PhD required) - Complete test harness you can build in a weekend - Red teaming and adversarial testing for AI security - Production monitoring and drift detection THREE READING PATHS IN ONE BOOK:

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