AI ENGINEERING IN PYTHON : A Code-First Guide To Prompts, RAG, Agents, Guardrails, and Evals, Most AI content teaches prompts in isolation. This book teaches AI engineering: how to design systems that stay accurate, safe, and maintainable when they meet real users, real data, and real production constraints. You’ll go beyond “I can call an LLM” to “I can ship an AI system.” Starting from the fundamentals of non-deterministic programming, you’ll learn prompt patterns that behave consistently, composition techniques with LCEL , and the discipline of context engineering (token budgets, memory, and retrieval strategy). From there, you’ll build production-grade RAG pipelines, then move into agents—LLMs that can call tools, plan multi-step work, and operate safely with guardrails , evaluation , and observability . Every chapter is hands-on and designed to be run locally with runnable examples. You’ll learn not only what works, but why it works, when it fails, and how to debug and improve it. By the end, you’ll have a practical toolbox for building real AI applications: grounded RAG , safe Agents , graph-orchestrated workflows, and the measurement systems required to keep quality high over time, and production agents.