"Evaluating Generative AI with Galileo: Measuring Quality, Detecting Hallucinations, and Monitoring LLMs in Production"

Generative AI systems fail in ways traditional software never does: they hallucinate, drift, ignore instructions, misuse retrieved context, and degrade silently in production. This book is written for experienced engineers, ML practitioners, platform teams, and technical leaders who need a rigorous approach to evaluating and operating LLM applications with confidence. Using Galileo as the organizing framework, it treats quality, observability, and runtime protection as one continuous engineering discipline rather than a collection of disconnected tools.

Readers will learn how to design trustworthy evaluation datasets, run controlled prompt and model comparisons, and choose the right metrics for correctness, grounding, instruction adherence, safety, and hallucination detection. The book also shows how to instrument LLM and agent workflows for deep observability, monitor quality trends in live traffic, investigate regressions through traces and signals, and enforce runtime guardrails for prompt injection, PII, toxicity, and other high-risk behaviors. Throughout, the emphasis is on diagnosis, trade-offs, and production-grade decision making.

Rather than offering a beginner survey, this is a deeply technical guide for teams already building or operating GenAI systems. Familiarity with LLM applications, RAG pipelines, APIs, and modern observability concepts will help readers get the most from it. Structured around practical workflows and failure-driven analy