Practical LLM Engineering with Python

Build Real AI Applications - Not Toy Projects

You 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 APIs
• Build RAG pipelines using ChromaDB, FAISS, and Pinecone
• Create AI agents with LangGraph
• Develop multi-agent systems using CrewAI
• Build MCP servers from scratch
• Deploy AI applications with FastAPI and Docker
• Add observability and tracing with LangSmith
• Structure scalable AI engineering workflows in Python

What 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 For

• Python developers entering AI engineering
• Data engineers integrating LLMs into modern pipelines
• Software engineers building AI products
• Freelancers and entrepreneurs creating AI-powered applications
• Developers who learn best by building

By 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.