1 / 2AI Engineering: Building Applications with Foundation Models
AI Engineering: Building Applications with Foundation Models by Chip Huyen is a technical guide to designing and deploying applications built on readily available foundation models. It covers model selection, evaluation, adaptation methods such as prompt engineering and RAG, and practical concerns including latency and cost.
At a glance
- Explains how AI engineering differs from traditional machine-learning engineering and introduces the AI stack.
- Covers evaluation of open-ended models, including AI-as-a-judge approaches.
- Discusses prompt engineering, RAG, fine-tuning, agents and dataset engineering.
- Addresses model and dataset selection, evaluation metrics, serving latency and cost.
Reference price: $113.98
Price checked: Nov 12, 2026
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Pros and cons
Pros
- Connects model-adaptation techniques with the broader process of building and deploying AI applications.
- The stated audience includes AI and ML engineers, data scientists, engineering managers and technical product managers.
- Includes practical discussion of evaluation and production concerns such as hallucinations, latency and cost.
Cons
- The book is explicitly technical, and some sections go deeper into technical detail than every reader may want.
- At 532 pages, it is a substantial print reference rather than a brief introduction.
Technical specifications
9 specifications| Publisher | O'Reilly Media |
|---|---|
| Publication date | Jan. 7 2025 |
| Edition | 1st |
| Language | English |
| Print length | 532 pages |
| ISBN-10 | 1098166302 |
| ISBN-13 | 978-1098166304 |
| Weight listed | 930 g |
|---|---|
| Dimensions | 17.53 x 2.79 x 22.86 cm |
What to know before buying
- The listing describes a technical treatment and says some sections become more detailed, so readers seeking a nontechnical overview may find parts demanding.
Who is it for?
A good fit for
- AI and ML practitioners building or improving foundation-model applications.
- Data scientists, engineering managers and technical product managers involved in AI development.
- Readers exploring model evaluation, RAG, fine-tuning, agents or production deployment.
- Job candidates and researchers looking to understand AI engineering skills and use cases.
Less suited to
- Readers seeking a nontechnical, short introduction to artificial intelligence.
- Readers who do not want detailed technical sections.
Customer reviews on Amazon
- 5 stars
80% 80% - 4 stars
11% 11% - 3 stars
4% 4% - 2 stars
2% 2% - 1 star
3% 3%
Customer feedback describes the book as practical, detailed and clear, with reported strengths in useful knowledge for AI practitioners. Experiences with readability and print quality are mixed. That matters for a substantial technical book: its subject coverage may suit practitioners, while reading comfort and the physical edition may not meet every buyer's preferences. Aggregate ratings lean strongly positive, but ratings alone do not explain individual experiences.
Positive themes
- Customers describe the content as practical and detailed, with clear explanations.
Mixed feedback
- Customer feedback is divided on readability and print quality.
Frequently asked questions
What topics does AI Engineering cover?
The listing describes AI application development with foundation models, including the AI stack, model and dataset selection, evaluation, prompt engineering, RAG, fine-tuning, agents, and deployment concerns such as latency and cost.
Who is this book aimed at?
Its stated technical audience includes AI engineers, ML engineers, data scientists, engineering managers and technical product managers, as well as some researchers and job candidates.
Is this a beginner-friendly, nontechnical book?
The listing says people with minimal or no prior AI experience can use foundation models to build applications, but it also identifies the book as technical and notes that some sections go deeper.
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