Practical LLM Engineering with Python: Build AI Agents, RAG Pipelines, MCP Servers, and Production AI Applications

Build Real AI Applications — Not Toy ProjectsYou don’t need a PhD to build production AI systems.You need working code, real engineering patterns, and practical guidance from someone who has actually shipped AI applications in the real world.Practical LLM Engineering with Python is a hands-on guide designed for developers who want to build modern AI applications fast — without drowning in academic theory or hype.Inside This Book, You’ll Learn How To:Work with OpenAI, Claude, and Gemini APIsBuild RAG pipelines using ChromaDB, FAISS, and PineconeCreate AI agents with LangGraphDevelop multi-agent systems using CrewAIBuild MCP servers from scratchDeploy AI applications with FastAPI and DockerAdd observability and tracing with LangSmithStructure scalable AI engineering workflows in PythonWhat Makes This Book Different?Every concept is explained through real, executable code.No academic fluff. No fake projects. No incomplete examples hidden behind GitHub repositories.Every implementation is fully explained, practical, and designed to help you build deployable AI systems you can actually use in production.Who This Book Is ForPython developers entering AI engineeringData engineers integrating LLMs into modern pipelinesSoftware engineers building AI productsFreelancers and entrepreneurs creating AI-powered applicationsDevelopers who learn best by buildingBy the end of this book, you won’t just understand LLM engineering.You’ll have a portfolio of real AI applications you can deploy, showcase, monetize, and use professionally.If you want a practical AI engineering book that stays open beside your editor while you code, this is the one. Read more