AI Engineering: Building Applications with Foundation Models

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.

Probably the best book on the topic

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.

This book is a must read for anyone who wants to incorporate AI in their solutions. It carefully explains how AI works, what it can do and what it cannot yet do. It contains a lot of examples, case studies and data analysis. It helps engineers, software and otherwise, to learn how to think about AI and how to start using it to make their products better. The author shows a lot of hands-on experience. It is not written like scientific paper (except one chapter which is clearly labelled) but often refers to papers. Definitely worth reading.

Easy read, contains enough detail

Excellent information and a comprehensive and structured approach.

Chip has summarised the past few years of rapid development in a concise and understandable format. Perfect for any data specialist.

I don’t really understand all the good reviews. Did the readers not really read the book or do they not build AI Systems. The book has good parts but if you think it will help you build AI Systems you are mistaken. It only focusses on GenAI products and how to build them, but that is a small world in AI Development. The authority only shortly mentions the rest of AI (what she calls traditional ML). The book could also be much shorter and tables about the adoption and development of GenAI tools are quickly outdated and make the book much longer than needed. I actually think the Editor could have done a better job and shortened the book by 50% and clarifying that its GenAI products not AI Engineering.

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