If You’re Learning AI, These 5 Books Are All You Need

#books #ai #llm #machinelearning

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

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.

1. The LLM Engineering Handbook by Paul Iusztin & Maxime Labonne

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.

Here is the link to get the book --- The LLM Engineering Handbook

You can also combine this book with the Full Stack AI Engineering course by Paul and his team. This one is the most popular super in-depth, around 80 hours, and very useful for Python devs to become AI engineers.

2. AI Engineering by Chip Huyen

Chip Huyen's AI Engineering explores how modern AI applications are designed and scaled in real-world settings.

It's a perfect follow-up if you've already learned the basics and want to understand infrastructure, data pipelines, and deployment challenges in the age of foundation models.

What I like most about this book is how Chip emphasizes engineering discipline, reproducibility, monitoring, and CI/CD for ML systems, something most books skip entirely.

Here is the link to get the book --- AI Engineering

IF you want, you can also combine this with the Artificial Intelligence A-Z: Learn How to Build an AI on Udemy course for active learning.

3. Designing Machine Learning Systems by Chip Huyen

Another brilliant work by Chip Huyen, this book focuses more on machine learning system design from data collection and labeling to model deployment and maintenance.

It's full of practical insights that align closely with what top tech companies expect in ML engineering roles.

If you're preparing for AI/ML interviews or aiming to design robust ML infrastructure, this is the most actionable book to start with.

**Here is the link to get the book - Designing Machine Learning Systems

4. Building LLMs for Production by Louis-François Bouchard & Louie Peters

This book is a practical guide to bringing LLMs into production environments safely and efficiently. It covers serving strategies, fine-tuning methods, vector databases, and integrating LLMs with existing applications.

What sets it apart is its focus on operational excellence --- latency, cost optimization, and observability --- topics rarely discussed in LLM literature but crucial for real-world success.

Here is the link to get the book --- Building LLMs for Production

By the way, he also has a course based on the book. If you want some active learning, you can also combine his course with the book, here is the link

5. Build a Large Language Model (from Scratch) by Sebastian Raschka, PhD

Sebastian Raschka's work stands out for its technical depth and clarity. This book walks you through every step of building a transformer model from scratch --- tokenization, attention mechanisms, optimization, and fine-tuning.

If you're a developer who wants to move beyond using APIs and truly understand how these models work, this book is a must-read. It's one of the best hands-on guides to the inner workings of LLMs.

Here is the link to get the book --- Build a Large Language Model (from Scratch)

And, if you want, you can also combine this with this 10-Hours LLM Fundamentals (Video) for active learning.

How do I Choose These Books?

When selecting these AI and LLM books, I look for three key things: practical relevance, technical depth, and author credibility.

Books written by practitioners who've built production systems tend to offer the most actionable insights.

I also favor those that include code samples, real-world deployment tips, and design considerations.

I avoid books that are overly academic or focused solely on theory --- the AI world moves too fast for that.

The five titles above strike the right balance between understanding concepts and applying them effectively. They'll remain valuable even as the technology continues to evolve.