Hands-On AI Engineering with Python: Build LLM Applications, RAG Systems, AI Agents, and Production-Ready AI Projects

The future of software engineering is AI engineering—and Python developers are leading the way.

If you already know Python and want to build real-world AI applications instead of experimenting with isolated prompts, 

Hands-On AI Engineering with Python gives you the practical roadmap you've been looking for.

Unlike books that focus only on prompt writing or AI theory, this guide teaches you how to design, build, evaluate, and deploy production-ready AI systems. Every concept is explained through hands-on examples, real engineering decisions, and modern development practices used by today's AI teams. The book is written for developers who want to move beyond demos and create reliable applications powered by Large Language Models.

Inside you'll learn how to:

✔ Build LLM-powered applications with Python

✔ Master prompt engineering for reliable outputs

✔ Create Retrieval-Augmented Generation (RAG) systems from scratch

✔ Work with embeddings and vector databases

✔ Build intelligent AI agents and multi-agent workflows

✔ Implement function calling and tool integration

✔ Evaluate AI systems using practical testing frameworks

✔ Reduce hallucinations with guardrails and safety techniques

✔ Optimize latency, cost, and scalability for production

✔ Deploy, monitor, and maintain AI applications with confidence

Whether you're a Python developer, backend engineer, data scientist, technical founder, or software engineer transitioning into AI, this book provides a structured, step-by-step learning path that reflects how modern AI systems are actually built. 

By the end of the book, you won't just understand how Large Language Models work—you'll have the skills to engineer complete AI applications, deploy them in production, and confidently build the next generation of intelligent software. 

Start building production-ready AI systems with Python today.