AI Engineering: Building Applications with Foundation Models

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ISBN-13: 978-1098166304, ISBN-10: 1098166302

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Book details

ISBN-10

1098166302
ISBN-13

978-1098166304
Edition

1st
Publisher

O'Reilly Media
Publication date

January 7, 2025
Language

English
Dimensions

7 x 1.08 x 9.19 inches
Print length

532 pages

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Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.

Understand what AI engineering is and how it differs from traditional machine learning engineering
Learn the process for developing an AI application, the challenges at each step, and approaches to address them
Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
Choose the right model, dataset, evaluation benchmarks, and metrics for your needs
Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.
AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

Review

"This book offers a comprehensive, well-structured guide to the essential aspects of building generative AI systems. A must-read for any professional looking to scale AI across the enterprise."

- Vittorio Cretella, former global CIO at P&G and Mars

"Chip Huyen gets generative AI. She is a remarkable teacher and writer whose work has been instrumental in helping teams bring AI into production. Drawing on her deep expertise, AI Engineering is a comprehensive and holistic guide to building generative AI applications in production."

- Luke Metz, co-creator of ChatGPT

"Every AI engineer building real-world applications should read this book. It's a vital guide to end-to-end AI system design, from model development and evaluation to large-scale deployment and operation."

- Andrei Lopatenko, Director Search and AI, Neuron7

"This book serves as an essential guide for building AI products that can scale. Unlike other books that focus on tools or current trends that are constantly changing, Chip delivers timeless foundational knowledge. Whether you're a product manager or an engineer, this book effectively bridges the collaboration gap between cross-functional teams, making it a must-read for anyone involved in AI development."

- Aileen Bui, AI Product Operations Manager, Google

"This is the definitive segue into AI Engineering from one of the greats of ML Engineering! Chip has seen through successful projects and careers at every stage of a company and for the first time ever condensed her expertise for new AI Engineers entering the field."

- swyx, Curator, AI Engineer

About the Author

Chip Huyen works in the intersection of AI, data, and storytelling. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup (acquired), worked on GPU optimization for data processing, and taught Machine Learning Systems Design at Stanford. Her last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages.

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Chip Huyen

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I’m Chip Huyen, a writer and computer scientist. I grew up chasing grasshoppers in a small rice-farming village in Vietnam.

I work in the intersection of AI, data, and storytelling. Previously, I built machine learning tools at NVIDIA, Snorkel AI, Netflix, and founded an AI infrastructure startup (acquired).

I also taught Machine Learning Systems Design at Stanford.

My last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages (very proud!).

In my free time, I like writing stories. I'm also the author of 4 Vietnamese story books.

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From the Publisher

Who This Book Is For

This book is for anyone who wants to leverage foundation models to solve real-world problems. This is a technical book, so the language of this book is geared toward technical roles, including AI engineers, ML engineers, data scientists, engineering managers, and technical product managers. This book is for you if you can relate to one of the following scenarios:

You’re building or optimizing an AI application, whether you’re starting from scratch or looking to move beyond the demo phase into a production-ready stage. You may also be facing issues like hallucinations, security, latency, or costs, and need targeted solutions.
You want to streamline your team’s AI development process, making it more systematic, faster, and reliable.
You want to understand how your organization can leverage foundation models to improve the business’s bottom line and how to build a team to do so.

You can also benefit from the book if you belong to one of the following groups:

Tool developers who want to identify underserved areas in AI engineering to position your products in the ecosystem.
Researchers who want to better understand AI use cases.
Job candidates seeking clarity on the skills needed to pursue a career as an AI engineer.
Anyone wanting to better understand AI’s capabilities and limitations, and how it might affect different roles.

I love getting to the bottom of things, so some sections dive a bit deeper into the technical side. While many early readers like the detail, it might not be for everyone. I’ll give you a heads-up before things get too technical. Feel free to skip ahead if it feels a little too in the weeds!

	Designing Machine Learning Systems: An Iterative Process for Production-Ready...	AI Engineering: Building Applications with Foundation Models

Customer Reviews	727	217
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Product information

Publisher	O'Reilly Media
Publication date	January 7, 2025
Edition	1st
Language	‎English
Print length	532 pages
ISBN-10	1098166302
ISBN-13	978-1098166304
Item Weight	‎1.85 pounds
Dimensions	7 x 1.08 x 9.19 inches
Best Sellers Rank	#1,244 in Books (See Top 100 in Books) #1 in Enterprise Applications #1 in Machine Theory (Books) #1 in Natural Language Processing (Books)
Customer Reviews	4.6 out of 5 stars 217Reviews

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Customers find the book comprehensible, with one review noting how the author thoroughly explains every topic. The book receives positive feedback for its readability, with one customer describing it as refreshing to read.

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Comprehension Readability

Customers find the book comprehensible, with one customer noting how the author thoroughly explains every topic, while others appreciate its concise and detailed approach.

"...The focus on evaluation and observability throughout multiple chapters was also refreshing to read as it explored a topic that at the heart of..." Read more

"Yes- this is a very informative book." Read more

"A dense read, but insightful. Nice work." Read more

"...The book is getting me excited about working in the field - building an AI based product, leveraging existing models, digging into foundation model..." Read more

Customers find the book readable, with one noting it is refreshing to read and another describing it as a comprehensive resource.

"...and observability throughout multiple chapters was also refreshing to read as it explored a topic that at the heart of success/failure for AI..." Read more

"A dense read, but insightful. Nice work." Read more

"...looking to level up with generative AI and LLM's, and I think this book is great!..." Read more

"...the area, and this is what Chip Huyen manages to achieve in this comprehensive book...." Read more

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Hockey Dave
Verified Purchase
If you’re building AI applications you should read this
Reviewed in the United States on June 24, 2025
Format: Kindle

This book presents a collection of helpful ideas and suggestions to aid engineers in developing AI applications on top of LLM models. I’ve already recommended it to the AI engineers that I work with.

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Alexander Nelson
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Amazing Resource!
Reviewed in the United States on June 12, 2025
Format: Kindle

If you’re at all interested in building products using large language models, this book is definitely a must read. The focus on evaluation and observability throughout multiple chapters was also refreshing to read as it explored a topic that at the heart of success/failure...

If you’re at all interested in building products using large language models, this book is definitely a must read. The focus on evaluation and observability throughout multiple chapters was also refreshing to read as it explored a topic that at the heart of success/failure for AI products but is mostly in its infancy.

One person found this helpful

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Bia Kanhan
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Fantastic
Reviewed in the United States on June 21, 2025
Format: Paperback

What a fantastic book! A great resource for people who interested in AI and its inner workings.

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Ricardo
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Excellent
Reviewed in the United States on June 20, 2025
Format: Paperback

Yes- this is a very informative book.

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Darryl Romano
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Great comprehensive book on the subject
Reviewed in the United States on April 16, 2025
Format: Kindle

Great comprehensive book on AI engineering. This book simplifies the concepts and techniques of advanced AI development with practical applications across Generative AI

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Amazon Customer
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Insightful read
Reviewed in the United States on June 5, 2025
Format: Paperback

A dense read, but insightful. Nice work.

One person found this helpful

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David Schneider
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Fantastic Resource for Leveling Up in Generative AI and LLMs!
Reviewed in the United States on March 18, 2025
Format: Kindle

I'm only up to Chapter 4, and this book is fantastic! I'm coming from a ML/deep learning background, looking to level up with generative AI and LLM's, and I think this book is great! I was hesitant at first, there is so much to find for free - but this book is...

I'm only up to Chapter 4, and this book is fantastic! I'm coming from a ML/deep learning background, looking to level up with generative AI and LLM's, and I think this book is great! I was hesitant at first, there is so much to find for free - but this book is concisely pulling it together with many interesting details! If I had been bouncing around on the web instead of reading this book, I don't think I'd know 1/2 as much about these early chapter topics as I do now! The book is getting me excited about working in the field - building an AI based product, leveraging existing models, digging into foundation model training - and there's still 7 more chapters to go!

12 people found this helpful

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Lak Lakshmanan
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Well-written, comprehensive, and authoritative
Reviewed in the United States on January 20, 2025
Format: Paperback

In academia, there is the concept of a "review article" -- it summarizes and organizes the major research findings into a framework that makes it easy to come up to speed on a topic. Frequently, the review articles themselves end up defining the area, and this is...

In academia, there is the concept of a "review article" -- it summarizes and organizes the major research findings into a framework that makes it easy to come up to speed on a topic. Frequently, the review articles themselves end up defining the area, and this is what Chip Huyen manages to achieve in this comprehensive book. The quality of the writing and diagams are uniformly high -- Chip uses simple language to great effect.

I think of myself as being somewhat up to date, but I have learned something new every chapter and not just minor details. For example, I had missed the Deep Mind paper pointing to "self-delusion" as the reason for hallucinations. Chip provides a clear explanation and shows an example. This fundamentally affects my intuitive understanding of model errors.

Of course, there's a danger with writing a review of a fast moving field. Just today, DeepSeek published an article showing that they can avoid SFT altogether and do just train a model on preferences, alphago-style. If this takes off, Chapter 7 will need a second edition.

Strongly recommend this book. It's invaluable for anyone building applications using GenAI models.

20 people found this helpful

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Top reviews from other countries

Ralcanta

Verified Purchase
Conocimientos prácticos y actualizados
Reviewed in Mexico on April 25, 2025

Excelente libro,actualizado a la época que estamos viviendo y con conocimientos prácticos.

Excelente libro,actualizado a la época que estamos viviendo y con conocimientos prácticos.

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Julien Zaegel

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Great overview
Reviewed in Canada on June 13, 2025

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,...

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.

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.

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Martin F.

Verified Purchase
Perfekt für alle, die neu in AI Engineering einsteigen
Reviewed in Germany on May 28, 2025

Ich habe mir dieses Buch aus reinem Interesse am Thema AI Engineering gekauft – und bin absolut begeistert. Die Autorin versteht es, komplexe Inhalte auf eine angenehm verständliche Weise zu vermitteln. Besonders hilfreich finde ich, dass nach nahezu jeder theoretischen...

Ich habe mir dieses Buch aus reinem Interesse am Thema AI Engineering gekauft – und bin absolut begeistert. Die Autorin versteht es, komplexe Inhalte auf eine angenehm verständliche Weise zu vermitteln. Besonders hilfreich finde ich, dass nach nahezu jeder theoretischen Erklärung ein passendes Beispiel folgt. Das macht es auch für Einsteiger ohne Vorkenntnisse leicht, dem Inhalt zu folgen. Was mir besonders positiv aufgefallen ist: Ich bin mittlerweile etwa bei der Hälfte des Buches und bin noch auf keinen einzigen Code-Schnipsel gestoßen – eine willkommene Abwechslung! Auch auf konkrete Tools wird größtenteils verzichtet. Stattdessen verweist die Autorin auf zahlreiche weiterführende Quellen, die bei Interesse zur Vertiefung einladen. Ich finde, dieses Buch ist ideal für alle, die sich zum ersten Mal intensiver mit AI Engineering beschäftigen möchten. Es liefert einen umfassenden, gut strukturierten Überblick über die wichtigsten Themen auf hohem Abstraktionsniveau und stellt praxistaugliche No-Code-Ansätze vor – das sorgt für einen angenehmen Lesefluss und macht das Buch gleichzeitig informativ und zugänglich. Für mich ganz klar: eine uneingeschränkte Kaufempfehlung!

Ich habe mir dieses Buch aus reinem Interesse am Thema AI Engineering gekauft – und bin absolut begeistert. Die Autorin versteht es, komplexe Inhalte auf eine angenehm verständliche Weise zu vermitteln. Besonders hilfreich finde ich, dass nach nahezu jeder theoretischen Erklärung ein passendes Beispiel folgt. Das macht es auch für Einsteiger ohne Vorkenntnisse leicht, dem Inhalt zu folgen.

Was mir besonders positiv aufgefallen ist: Ich bin mittlerweile etwa bei der Hälfte des Buches und bin noch auf keinen einzigen Code-Schnipsel gestoßen – eine willkommene Abwechslung! Auch auf konkrete Tools wird größtenteils verzichtet.

Stattdessen verweist die Autorin auf zahlreiche weiterführende Quellen, die bei Interesse zur Vertiefung einladen.

Ich finde, dieses Buch ist ideal für alle, die sich zum ersten Mal intensiver mit AI Engineering beschäftigen möchten. Es liefert einen umfassenden, gut strukturierten Überblick über die wichtigsten Themen auf hohem Abstraktionsniveau und stellt praxistaugliche No-Code-Ansätze vor – das sorgt für einen angenehmen Lesefluss und macht das Buch gleichzeitig informativ und zugänglich.

Für mich ganz klar: eine uneingeschränkte Kaufempfehlung!

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Athlos

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Good but....
Reviewed in Japan on May 9, 2025

Content is good but applications contents almost nothing. Disappointed.

Content is good but applications contents almost nothing.
Disappointed.

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Soulaimane

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Shipment quality is really damaged
Reviewed in the United Arab Emirates on May 17, 2025

The shipment received really damaged

The shipment received really damaged

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