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

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.

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!

Great resource.

This should be the AI 101 for experienced developers who want to move into AI Engineering as a career option.

Good Job

Excellent content and quality print.

Comprehensive survey of AI field today. Good introduction to different aspects of AI, instead of focusing only on the models.

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

I still need to learn more technical things to be able to understand all knowledge that this books brings but I learned a lot and will use as guide on this process. I strong recommend to anyone that want to start and don’t know where start

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.

If you are working with LLMs, this book is a great read. I really liked how it treats foundation models as a new software stack rather than just “better models”, covering the full lifecycle from model selection and adaptation (prompts, RAG, fine‑tuning, agents) to evaluation‑driven development and deployment trade‑offs around latency and cost.

P.S. this book has the most interesting footnotes!

Fantastic book and great and timely delivery