Open Source If You’re Learning Ai, These 5 Books Are All You Need

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Hello Devs, I've spent the past one and a half years diving deep into the world of Artificial Intelligence and Large Language Models (LLMs).

From engineering systems that scale to understanding model internals and prompt optimization, I've gone through more than 20 books to truly grasp the fast-evolving AI landscape.

Some were theoretical, others highly practical, but only a few stood out as must-reads for anyone serious about building, deploying, or understanding AI systems.

If you're an AI engineer, developer, researcher, or even an ambitious learner wanting to understand the shift toward LLM-driven applications, this list will save you countless hours of exploration.

These five books offer both the depth and practicality needed to navigate today's AI ecosystem, from foundational understanding to hands-on implementation.

This book is arguably the best hands-on resource for anyone who wants to build, fine-tune, and deploy LLMs efficiently.

Paul and Maxime have done an excellent job bridging the gap between theory and production engineering.

You'll learn about prompt optimization, retrieval-augmented generation (RAG), function calling, model evaluation, and more, all with actionable examples.

I found this especially valuable for understanding the end-to-end lifecycle of LLM products and how to turn research models into production-ready systems.

Source: Dev.to