AI Engineering: Building Applications with Foundation Models

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.

Excellent information and a comprehensive and structured approach.

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.

Easy read, contains enough detail

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

Increadibly valuable piece of knowledge. Absolute must have for all people interested in leveraging the power of foundadional models.

This book covers a lot. In fact maybe too much. Much of the content links to research papers and blogs. The section covering the attention mechanism is far too short and doesn't do anything to help readers understand this complex topic in detail. The section covering frameworks is miniscule; for a book that claims to cover "the process of building applications with readily available foundation models" this was surprising. In summary, if you want broad brush of AI engineering covering pre-training, post-training, fine tuning, RAG etc then this will provide you with that. If you actually want learn how to build AI applications using foundation models you probably need to go elsewhere.

I am sure this is very informative but I can't get past the daft the reader sounds AI which is really off-putting when listening to an audio book.