Production AI Engineering is a practical guide to building AI systems that work outside demos. It explains what changes when software starts depending on LLMs: probabilistic behavior, token limits, latency, cost, retrieval quality, prompt injection, structured outputs, evaluations, and observability. The book is written for software developers, technical leads, and architects who want to turn AI features into reliable products. It covers the core patterns and trade-offs behind RAG, tool use, agent loops, context engineering, safety controls, and production operations. The focus is on clear engineering judgment: what to measure, where systems fail, and how to design AI applications that are maintainable, secure, and cost-aware.