AI Engineering: Building Applications with Foundation Models

Surprised with the colour diagrams! Very good quality book. Coming to content of the book, it's a must read for someone to get idea on how to adapt AI in the applications and get good insight about AI terminologies. Very well structured and simple english makes it reading intresting.

The book was delivered in good condition. The contents of book are good. Although Agentic AI topics like LangChain, LangGraph etc. are not covered here. Also topics like MCP are not covered here. But overall it's a good book to read. I am yet to read this book.

Not a how-to practical book, but still a valuable resource in learning AI system. Great content and excellent writing style.

Enjoying the read. Might be a little boaring at times but it's worth the time and price. The first copy that I received had missing pages so please check for all the pages as soon as you receive it.

Great learning 👍 lot of information to gain knowledge pease keep writing more such books like this one. Thanks 😊

Knowledgeable book and nice explained

The book is in colour and I am happy with the condition of book. This was available in India. I will write a longer review once I read it.

Love it. This book is especially for you if you have no background in ML engineer and more specifically AI engineering. It will give you every bit of information using which you can make informed choices in your career and work. Highly recommended!

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.

Good books for Software engineers without much ML-preexquisite

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.

Chip Huyen’s AI Engineering: Building Applications with Foundation Models offers a concise yet comprehensive exploration of the core concepts that underpin modern AI engineering. In an era where AI tools, frameworks, and APIs evolve almost weekly, designing a coherent, durable book is no small feat—and Huyen succeeds admirably. The book is firmly grounded in theory, supported by clear diagrams that help illuminate complex ideas. While it doesn’t delve into code snippets or implementation-heavy examples, this feels like a deliberate choice rather than a shortcoming. The restraint in length is actually a strength: it makes the book more digestible, especially for readers who want to understand foundational principles without getting bogged down in fast-aging technical details. One of the biggest challenges in writing about AI today is the pace of change. Huyen avoids the trap of chasing trends and instead focuses on building conceptual clarity—something far more enduring. Whether you're a software engineer looking to transition into AI, a data scientist aiming to deepen your understanding of systems, or a product leader wanting to make more informed decisions, this book provides the scaffolding you’ll need. I couldn't recommend it more highly for anyone looking to master AI engineering or familiarize themselves with its essential concepts. This is a book you’ll want on your shelf—thoughtful, structured, and refreshingly free of unnecessary fluff.

All words and no substance - not even a single diagram or flowchart to illustrate a process - to call it 'engineering' completely misses the point.