11 Must-Read AI and LLM Engineering Books for Developers in 2025

Software Engineer to AI Engineer - 11 Books to Learn AI and LLM Engineering

Hello guys, Artificial Intelligence is no longer a futuristic idea — it’s already reshaping how we write code, build products, and run businesses.

Whether you’re a backend developer, frontend engineer, or DevOps specialist, transitioning into AI engineering could be one of the smartest career moves you make in 2025.

The explosion of Large Language Models (LLMs) like GPT, Claude, and Gemini has created an urgent demand for engineers who can deploy, fine-tune, and build applications around these powerful models.

But becoming an AI Engineer isn’t just about knowing a few Python libraries. It requires understanding LLMs, machine learning systems, data pipelines, production-grade deployments, prompt engineering, and increasingly, agentic AI systems.

The good news? You don’t need to go back to school. The following 10 books are great resource for anyone to go from software engineer to AI engineer in 2025.

These are also the most recommended books on AI, ML, and LLM, and you will find them recommend by Reddit as well as HN and other AI and Machine Learning forums.

By the way, if you prefer online courses then books then I highly recommend you to start with The AI Engineer Course 2025: Complete AI Engineer Bootcamp, one of the most comprehensive resource to become an AI Engineer in 2025.

10 Best Books to become an AI Engineer in 2025

Without any further ado, here are the 10 best books to become an AI Engineer in 2025. These books covers LLMs, AI Engineers, and all other skills you need to transition from Software Engineer to AI Engineer in 2025.

1. AI Engineering by Chip Huyen

This is the definitive book for understanding what it means to engineer AI systems that work in the real world.

Before reading this book, I checked with people who are working as AI Engineer to find out exactly what they do and most of them are responsible for building a multi-agent chat bot using LLMs.

One friend told me that that he don’t do any ML and his previous AI Engineering job involved building LLM agents as well.

This means a good knowledge of LLM is very important for transitioning from Software Engineer to AI Engineer and that’s where this book really shines.

Chip Huyen — Stanford lecturer and founder of Claypot AI — breaks down the end-to-end lifecycle of machine learning systems, from model development to deployment and monitoring.

It covers all important topics an AI Engineer should know including MLOps, real-time ML, data-centric AI, production challenges.

Here is the link to get this book — AI Engineering by Chip Huyen

2. The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne

Do you want to understand the entire LLM stack — embedding models, vector databases, prompt chaining, retrieval augmented generation (RAG)?

This handbook by

Paul Iusztin

and

Maxime Labonne

is one of the best guides for developers who want to build, fine-tune, and deploy LLM applications and for for those who love to go deeper and applying more robust models.

This book cover all key skills and libraries like like LangChain, OpenAI APIs, RAG, fine-tuning, evals, agents.

I found about this book on Reddit and I must say the authors have done an amazing job in explaining the concepts. I am currently at chapter number 6 and loving the way the content has been structured.

The authors have made it quite practical and hands-on.

Here is the link to get this book — The LLM Engineering Handbook

3. Designing Machine Learning Systems by Chip Huyen

This is another ML book which I found on Reddit and love it.

This book bridges the gap between ML theory and software engineering practice. It walks you through building scalable and maintainable ML pipelines.

Its actually a great companion to AI Engineering which teaches you general use of existing models in development.

It also provide good overview of the several different concepts in this field and covers topics like Data pipelines, model deployment, scalability, software design for ML.

Though, It’s not a code focused book and there’s a negligible amount of code throughout the entire book. But the conceptual content is good and I’d highly recommend it

Here is the link to get this book — Designing Machine Learning Systems

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

If you are looking for a book to learn LLMs in depth then this book is for you. Created by Louis-Francois Bouchard and Louie Peter this book will teach you what it take to build and deploy LLM in production.

It covers everything from choosing the right architecture to scaling LLMs in production using best practices including Infrastructure, prompt tuning, inference optimization, evaluation.

Overall a great book on LLM and for Software Developers and engineers interested in real-world deployments of LLMs.

Here is the link to get this book — Building LLMs for Production

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

If you want to dive into the internals of how LLMs like GPT are built — this is your book.

Author and expert, Sebastian Raschka demystifies transformers, attention mechanisms, and training pipelines, all with code examples.

This is also a great book to learn about transformer architecture, training, tokenizer implementation, code-first learning.

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

6. Hands-On Large Language Models: Language Understanding and Generation

This is an ideal book for hands-on learners. It teaches how to implement, fine-tune, and scale LLMs like BERT, GPT, and T5.

If you’re aiming to build custom AI features into your applications, this book is a must.

It also covers key topics like Pretrained models, Hugging Face, fine-tuning, deployment, inference, which is must for any LLM Engineer.

Overall a nice book to learn about real world LLM Engineering and fine-tuning an LLM.

Here is the link to get this book — Hands-On Large Language Models: Language Understanding and Generation

7. Prompt Engineering for LLMs

Prompt engineering is emerging as a critical skill for building effective LLM-based applications, after all that’s the only thing which is going to separate one developer from other.

You can get gold or garbage from LLMs as it all depends upon your prompting skills.

This book dives into prompt types, chaining techniques, and optimization strategies to get the best results from models like GPT-4. It also covers things like Prompt templates, few-shot prompting, instruction tuning, and evaluation techniques.

Overall one of the best book to learn about Prompt Engineering and I highly recommend this to every Software engineers including Java developers.

Here is the link to get this book — Prompt Engineering for LLMs

8. Building Agentic AI Systems

Agentic systems are the next evolution of LLM applications — think AI agents that can plan, reason, and act.

If you seen AI agent in action, particularly coding features which takes normal developer a week in few minutes then you know that we all need AI agents which can do all the heavy lifting for us.

With the invention of MCP and several MCP servers to integrate LLM with popular tools like database, IDEs, and other systems, building AI agent is going to be a key skill for near future and this book is a great resource to learn how to build Agentic AI systems.

This book provides a deep dive into architectures and use-cases for agent-based systems and it also covers key topics like Multi-agent coordination, memory systems, long-term reasoning, open-ended tasks.

If you are interested in Agentic AI then I highly recommend this book to you.

Here is the link to get this AI Agent book — Building Agentic AI Systems

9. Prompt Engineering for Generative AI

This is another book on Prompt Engineering which I recommend to software developers.

This book takes a broader take on prompt engineering that includes applications in text, image, and code generation.

It also covers key topics like Multimodal prompting, style transfer, control techniques, creative generation.

If you’re working with tools like GPT, DALL·E, Midjourney, or Copilot, this book will show you how to guide outputs effectively.

Here is the link to get this book — Prompt Engineering for Generative AI

10. The AI Engineering Bible

This is another comprehensive guide that covers the full AI engineering stack — from foundational models and vector databases to orchestrating LLM workflows and tools like LangChain and LlamaIndex.

In this book you will learn about RAG, LLMOps, open-source models, tooling, and full-stack AI development.

This book is idea for senior software engineers who want to become a principle AI Engineer. It covers most of the AI tools and technology you need to succeed as AI engineer.

Here is the link to get the book — The AI Engineering Bible

11. Generative AI System Design Interview (Bonus book)

In our side, 11 is considered a lucky number so I thought to add another of my favorite book on Generative AI which also cover System Design.

This book is great for senior software engineers or Tech lead who are responsible for designing and creating AI applications.

This book come from the ByteByteGo team but written by by Ali Aminian and Hao Sheng instead of Alex Xu who has been involved on all of their previous books like ML System Design and Coding Patterns books along with System Design Interview — an Insider guide.

Designing generative AI (GenAI) systems for interviews can be complex and challenging. This book offers a clear and structured approach to help you tackle a wide range of GenAI system design questions.

It also provides a practical framework and real-world examples to make learning these concepts easier.

This book also complements their ML System Design Interview book.

While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.

The great thing is that you will get a 7-step framework to help you tackle GenAI system design interview question as well as 10 real-world GenAI system design questions with in-depth solutions, and most importantly 280+ diagrams to demystify complex GenAI systems.

Here is the link to get this book — Generative AI System Design Interview

Conclusion

That’s all about the 10 Must Read AI and LLM books for Software Engineers in 2025. Becoming an AI engineer in 2025 isn’t about memorizing ML algorithms — it’s about engineering intelligence into your software.

These 11 books will give you the theoretical foundations, hands-on skills, and production knowledge to build state-of-the-art AI applications.

Whether you’re building your own GPT-based chatbot, launching an AI startup, or transitioning into an AI-focused role at your company, reading these books will put you ahead of the curve.

Start with one, but aim to read them all.

The AI revolution isn’t waiting — and the best time to level up is now.