AI Engineering: Building Applications with Foundation Models

I don't normally have the time for reviews anymore but I had to do one for this book. This book is excellent. The level of detail and range of topics was just right. With some books I've had to force myself to finish. This one kept me interested throughout the entire book and provided everything clearly. I'm more interested in usage of foundation models (LLM's, RAG, etc.) but the chapters on model pre-training/training/evaluation provided great detail. I'm looking forward to more works from Chip.

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.

A bit pricey to what I usually buy, but I can confidently say "You get what you pay for"! I am so jealous of the author's clarity and easy tone that somehow manages to convey an impressive amount of information. In technical writing, if it looks easy, it certainly wasn't! If I survive the technopocalypse, I look forward to reading more of Chip's books!

A great resource for anyone looking to enter the field of AI engineering. The book assumes some prior experience with basic ML and AI concepts, which allows it to dive deeper into practical and relevant topics without spending too much time on fundamentals. Highly recommended if you have that foundation and want to take the next step.

Very informative. The audio version could use a little polishing like reading a table is not very conducive to the understanding and should be skipped

This book is so incredibly well-organized and well-edited that it’s hard to believe it’s a first edition rather than a third or fourth. I love when authors invite readers to jump around and read chapters out of order; in this case, every section proved helpful. This book offers a clear and valuable overview of AI, I plan to keep it close at hand as a reference.

Enjoying it so far. I’m on chapter 3 as of right now and I can confidently say it’s very eye opening. I’d recommend it to anyone looking into machine learning or AI engineering.

Chip Huyen has done it again—delivering a smart, thorough guide that takes readers step by step through complex material with remarkable clarity. Through simple, accessible examples, she empowers readers to achieve their goals. The modular structure allows experienced readers to navigate at their own pace, while her unmatched coverage of practical applications sets this work apart. Her approachable tone builds reader confidence, ensuring full comprehension of the material. Well-documented and diverse sources provide a robust foundation, while her presentation style—concise, clear, and thoughtfully structured with short, easy to digest paragraphs—creates an ideal learning experience. Important points and deeper insights are segregated and clearly marked for easy reference. This resource will undoubtedly become a valued reference, likely to evolve alongside the field itself. Thank you, Chip! A worthy successor to your first volume -- and we eagerly await your next contribution to the field. ~ Denise Shekerjian, author Uncommon Genius (Viking, Penguin)

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.

The book had exactly the level of depth I needed. I’m coming from the data engineering side and needed some complete overview of AI Engineering. The book gave a complete coverage of the key topics while still going with some details (but avoiding the non-necessary technicalities). The reference are really valuable and worth the de-tour while reading.

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.

Best book on AI engineering. It is not based on technologies, but on principles and patterns. It is a MUST if you start with agents development or if you just want to know about AI topics.

I was looking for an AI book that would be fit-for-purpose for someone with tech knowledge but did not want to code AI. Most of the books I found were either too basic (simplistic overviews) or too deep into the subject (how to actually code in a specific language) This book, for me, filled that missing space. It covered the introduction into AI well, forming, for me, a good understanding to how/what AI can do at present (with some history thrown in). Then it moved into deeper levels for a fuller appreciation of the environment. Its not a specific language/coding book - for that look elsewhere. However, you need to walk before you can run and I believe this book fills that space.