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.

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.

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.

If you want to learn how to do AI engineering this is not the book. It’s not a practical book…

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.

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

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.