A well-written and practical book for anyone building applications with foundation models. It focuses on real-world AI engineering challenges, with clear explanations and actionable examples rather than just theory. Great for developers and ML engineers looking to build production-ready AI systems. Highly recommended!

It's always daunting to pick up a technical book that's over 500 pages long or 21 hours long. However, this book did not disappoint. Not every section, of course, addressed my particular needs. However, the entire treatise was clearly communicated with a broader technical audience in mind. That should be no surprise because Chip Huyen, besides being an AI expert, taught graduate school classes in AI at Stanford and writes science fiction as a side hobby. This book is simply the best technical introduction I've encountered to date.

The book starts with high-level concepts about AI, which would be accessible to all sorts of scientific folks. Then it focuses on technical topics that are of most interest to engineers. It does an excellent job of centering around concepts first and not being wedded to particular technologies which will soon change. I valued the insights so much that, after listening to the audiobook, I even bought a paper copy to have for a reference.

I plan to continue to read about AI engineering, but given that I haven't taken formal coursework in the topic, this book served as an equivalent to a graduate school class to give me confidence to dive deeper. Although some math were presented, the audiobook was incredibly accessible, unlike with some technical books. For those who spend time commuting in cars, I recommend listening to the text if you don't have time to flip through a paper book.

Overall, this book raised my game significantly about AI. Where other books obscure with technical jargon, this book enlightens with clear concepts. I still need to brush up on a few focused topics to ready myself for a project, but I'm much more fluent about the ideas than before. I highly recommend this in-depth introduction, at least for the next few years until the field outpaces our knowledge once again.

Reading this book was fun and helped me connect dots of concepts that hitherto felt like just buzz words. After reading this book, I was prepped enough to read on higher level books. Highly recommended if you're just starting to learn AI and LLMs.

Love this book. Very insightful and extremely helpful in breaking down many of the complex aspects of AI so they are easy to understand!!!

Great comprehensive book on AI engineering. This book simplifies the concepts and techniques of advanced AI development with practical applications across Generative AI

Great book for beginners

This is an easy-to-read, how it's made book, relevant -- just need to look up more recent advancements in some of the author's citing. Almost finished reading along with local tech community book club, and it is sparking lots of interesting conversation

I am Full-stack developer, and I looked for a book which give me the work methodology building AI application.

I have read 50% of this book, I have to say that this book is written so well, each page is very clear, and the reading is fluent (something which hard to provide in technical books), and I am not native English speaker.

Thank you for this book.

كتاب رائع تغليف جيد والورق والكتابه واضحه سرعه بشحن وتوصيل المعلومات فيه قيمه جدا جدا دخفت دورات كثير ماأستفدت زي هذا الكتاب أنصح فيه وبشده مممتع جدا وسهل الفهم

Chip Huyen has written a genuinely practical guide for AI practitioners.

The standout chapters are the ones on RAG and agentic systems — the author is upfront that these areas are still experimental and evolving fast, but that's exactly what makes her treatment of them so valuable.

A gem.

The central idea of the book is that foundation models have become so powerful and expensive to build that, instead of training models, many organizations might be better off creating applications on top of them. The book covers evaluation, guardrails, security, finetuning, context construction, inference optimization, user feedback and architecture.

The level of detail is excellent: we're looking under the hood just enough to understand what's going on, but keep that high level perspective that allows the book to give a overview of a broad topic in just 500 pages.

I highly recommended this book to engineers looking for an overview of AI engineering — as opposed to ML engineering, which might be too low-level for them and be more relevant for data scientists.

Fantastic book and great and timely delivery

If you feel like the ground is shifting beneath your feet as an engineer, you aren't imagining it and Chip Huyen’s AI Engineering is one of the best guides to make sense of the chaos.

Our February 2026 Data & AI Book Club recently dissected this book, and the consensus was clear; it’s a manual for the new reality of software development.

Whether you are a seasoned architect or a developer just starting to integrate agentic workflows, this book provides the framework to stay relevant. It’s not just about learning tools; it’s about understanding the shift in economics and strategy that defines the current year.

AI is a fast evolving space and so much has already happened since the book was published. We look forward to a second edition that includes AI engineering with vibe coding, AI platforms and multi agent systems.