Debugging Machine

AI systems fail differently than traditional software. They don't crash—they produce wrong answers with high confidence. They don't throw exceptions—they hallucinate plausible-sounding falsehoods. Debugging AI requires a fundamentally different toolkit. Every production AI system has bugs. The difference between a professional AI deployment and an amateur one is not the absence of bugs—it's the presence of systems to detect, diagnose, and correct them. This book provides the complete debugging toolkit for AI systems: understanding hallucinations, detection strategies, root cause analysis, correction techniques, human-in-the-loop verification, and architectural prevention.