I Read 20+ Books on Artificial Intelligence, LLMs, and Agentic AI: Here Are My Top 10 Recommendations for 2026
My favorite books to learn Artificial Intelligence, LLM, and Agentic AI engineering in 2026
Hello friends, if you want to become an AI Engineer, LLM Engineer, Machine Learning Engineer, or simply understand how modern AI systems like ChatGPT, Claude, Gemini, and AI Agents are built, then reading the right books can save you hundreds of hours of trial and error.
Over the last few years, I’ve read more than 20 books on Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Prompt Engineering, RAG, and Agentic AI systems.
Some books were highly academic.
Some were too theoretical.
And some were just collections of prompts with little engineering value.
But a handful of books stood out because they focus on what modern developers actually need: building, deploying, scaling, and maintaining AI-powered applications in production.
If you’re serious about AI Engineering in 2026, these are the books I recommend the most.
By the way, if you are someone who learn better with course and starting with AI then I also suggest you to start with a course like Full Stack AI Engineering on Towards AI Academy, its created by Louis-François Bouchard, Co-founder & CTO, and author of Building LLMs for Production book, which I have mentioned below.
Build Real Products with LLMs, Context Engineering, RAG.
10 Best Books To Learn AI Engineering, LLMs, and Agentic AI in 2026
Without any further ado, here are the 10 books I found great for learning AI Engineering, LLMs, both fine-tuning and deploying into production and Agentic AI, particularly creating and deploying AI agents in 2026
1. The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne
If I had to recommend just one book for aspiring AI Engineers, this would be it.
The LLM Engineering Handbook bridges the gap between machine learning theory and production-grade AI systems. It covers everything from RAG and vector databases to evaluation, deployment, observability, and optimization.
What I like most is that it’s written from an engineering perspective rather than a research perspective.
If you want to build real AI products, start here.
Best For:
- AI Engineers
- Backend Developers
- Software Engineers transitioning into AI
Here is the link to get this book — The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne
He has also created an awesome course on Agentic AI, called “Agentic AI Engineering” on Towards AI and if you are learning Agentic AI engineering, I highly recommend you to checkout his course also. It’s very practical and hands-on and you will love it.
Production AI Agents Course: Learn Agentic Engineering
2. AI Engineering by Chip Huyen
Chip Huyen has become one of the most respected voices in AI Engineering.
This book explains how to build applications using foundation models and modern AI stacks. It focuses on practical challenges such as inference, evaluation, deployment, monitoring, and production reliability.
Unlike many AI books, this one focuses on shipping products rather than training models.
It’s one of the most important books published in the AI era.
Here is the link to get this book — AI Engineering by Chip Huyen
AI Engineering: Building Applications with Foundation Models
For active learning, you can also combine this book with the Full Stack AI Engineering course on Towards Academy, which is created by Louis-François Bouchard, author of popular LLM book.
Build Real Products with LLMs, Context Engineering, RAG.
3. Designing Machine Learning Systems by Chip Huyen
Before you build AI products, you need to understand how machine learning systems work.
This book covers the complete ML lifecycle, including data engineering, model development, deployment, monitoring, and scaling.
Many of the concepts discussed here directly apply to modern LLM and Agentic AI systems.
This is one of the best books I’ve ever read on production machine learning.
Here is the link to get this book — Designing Machine Learning Systems by Chip Huyen
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
4. Building LLMs for Production by Louis-François Bouchard and Louie Peters
Many developers know how to call an LLM API.
Very few know how to deploy and operate LLM-powered applications reliably.
This book focuses on production concerns such as architecture, evaluation, optimization, scalability, latency, and deployment.
If you’re building customer-facing AI products, this book is incredibly valuable.
Here is the link to get this book — Building LLMs for Production by Louis-François Bouchard and Louie Peters
5. Build a Large Language Model (From Scratch) by Sebastian Raschka
Want to understand what happens inside GPT models?
This book is fantastic.
Sebastian Raschka walks you through building an LLM from scratch using Python and PyTorch.
You’ll learn tokenization, embeddings, transformers, attention mechanisms, training pipelines, and inference.
This is probably the best hands-on book available for understanding LLM internals.
Here is the link to get this book — Build a Large Language Model (From Scratch) by Sebastian Raschka
Build a Large Language Model (From Scratch)
6. Hands-On Large Language Models
This is one of the most practical books on LLM development.
It combines theory with implementation and covers embeddings, fine-tuning, retrieval systems, prompt engineering, evaluation, and deployment.
If you learn best by building projects, you’ll love this book.
I found the examples particularly useful for understanding real-world LLM applications.
Here is the link to get this book — Hands-On Large Language Models
Hands-On Large Language Models: Language Understanding and Generation
7. Prompt Engineering for LLMs
Prompt engineering has evolved significantly over the last few years.
This book covers advanced prompting strategies including:
- Chain of Thought
- ReAct
- Few-Shot Learning
- Self-Consistency
- Prompt Optimization
Many developers underestimate how much performance improvements can come from better prompting.
This book helps fill that gap.
Here is the link to get this book — Prompt Engineering for LLMs
Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications
8. Building Agentic AI Systems
Agentic AI is one of the hottest areas in AI right now.
Instead of simple chatbots, modern AI systems can reason, plan, use tools, retrieve information, and collaborate with other agents.
This book teaches how to design and build these systems.
It covers agent architectures, orchestration, memory, tool usage, planning, and multi-agent workflows.
If Agentic AI interests you, this is a must-read.
Here is the link to get this book — Building Agentic AI Systems
9. Prompt Engineering for Generative AI
This book is ideal for developers, product managers, analysts, and AI practitioners who want to get better results from generative AI systems.
It provides practical frameworks and examples that can immediately improve your interactions with modern LLMs.
The real strength of this book is its focus on practical applications rather than theory.
Here is the link to get this book — Prompt Engineering for Generative AI
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
10. The AI Engineering Bible
The title may sound ambitious, but this book contains a tremendous amount of practical knowledge.
It combines AI Engineering principles, workflows, tooling, deployment strategies, and production practices into a single resource.
If you’re looking for a broad overview of the AI Engineering landscape, this is a great place to start.
Here is the link to get this book — The AI Engineering Bible
Bonus: LLMs in Production
This book narrowly missed my top ten, but it’s still excellent.
As the name suggests, the focus is entirely on deploying, operating, monitoring, and scaling LLM-powered applications in production.
If your goal is building enterprise-grade AI systems, I highly recommend reading this after finishing some of the books above.
Here is the link to get this book — LLMs in Production
LLMs in Production: From language models to successful products
And, if you need a course, you can also join Master LLM Engineering & AI Agents: Build 14 Projects course on Udemy. It’s both project-based and hands-on and you can get it for just $10 on Udemy sales.
LLM Engineering, RAG, & AI Agents Masterclass [2026]
Final Thoughts
The AI industry is evolving faster than any technology wave I’ve seen in my career.
Frameworks change.
Models change.
Tools change.
But the engineering principles behind building reliable, scalable, and maintainable AI systems remain valuable for years.
If you’re serious about becoming an AI Engineer in 2026, I would recommend starting with:
Then move on to Agentic AI, Prompt Engineering, and Production LLM books.
These books will give you a much stronger foundation than simply watching random YouTube videos or chasing the latest AI trend.
Happy reading and happy building!
Which AI book has had the biggest impact on your career? Let me know in the comments.
P. S. — If you just want to do one thing at this time, I suggest you to start with the Associate AI Engineer for Developers track on Datacamp. It’s one of the best structured program for developers to become AI Engineer in 2026, you will thank me later.
AI Engineer Course: Become an AI Engineer | DataCamp
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