Generative AI Design Patterns: 32 GenAI Application Patterns
GenAI: Generative AI Design Patterns is a book about recurring challenges in building applications and agents with generative AI foundation models. It presents 32 patterns with coded examples and trade-offs, spanning hallucinations, inconsistent outputs, agent planning, and related engineering concerns.
At a glance
- Explains 32 design patterns for problems including hallucinations, nondeterministic responses, and knowledge cutoffs.
- Pattern descriptions are presented with coded examples and discussion of trade-offs.
- Coverage includes agent planning, self-correction, tool use, and collaboration among agents.
- Topics also include output style constraints, creativity and risk, latency, and cost.
Reference price: $79.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Coded examples and trade-off discussions make the material more actionable than a pattern list alone.
- Covers both application-level LLM challenges and agent-oriented designs.
- The stated audience includes software engineers, data scientists, and enterprise architects building on foundation models.
Technical specifications
8 specifications| Publisher | O'Reilly Media |
|---|---|
| Publication date | November 11, 2025 |
| Edition | 1st |
| Language | English |
| Print length | 506 pages |
| ISBN-13 | 979-8341622661 |
| Weight listed | 1.92 pounds |
|---|---|
| Dimensions | 7 x 1.02 x 9.19 inches |
What to know before buying
- The book says background requirements vary by pattern. Some topics call for experience in API design, large-scale software, statistics and machine learning, or hardware optimization.
Who is it for?
A good fit for
- Software engineers developing GenAI applications or agents on foundation models.
- Data scientists and enterprise architects looking for recurring design approaches to reliability and production concerns.
- Readers seeking examples of patterns for agent planning, tool calling, and multi-agent collaboration.
Less suited to
- Readers seeking a guide focused on training bespoke machine-learning models. The book describes its focus as applications built on foundation models.
- Readers who need every chapter to assume only beginner-level programming knowledge. The listing says some patterns require specialized knowledge or experience.
Customer reviews on Amazon
- 5 stars
71% 71% - 4 stars
17% 17% - 3 stars
12% 12% - 2 stars
0% 0% - 1 star
0% 0%
The available evidence provides aggregate ratings but no written customer feedback, so it does not identify specific strengths, complaints, or reasons behind scores. The distribution is mostly five-star with a smaller four- and three-star share, which is a generally favorable but limited signal. It cannot establish how readers assess the book's examples, technical depth, or usefulness for their particular experience level.
Frequently asked questions
What topics does this book cover?
The listing describes 32 design patterns for common foundation-model application challenges, including hallucinations, inconsistent outputs, knowledge cutoffs, and building agents that plan, self-correct, act, or collaborate.
Does it include code examples?
Yes. The listing says each pattern includes a fully coded example and discusses trade-offs.
Who is the book intended for?
The stated audience includes software engineers, data scientists, and enterprise architects building applications powered by generative AI foundation models.
Is the material suitable for beginners?
The listing says about 75% can be understood by a junior software engineer or a third-year computer science student, while the remainder requires specialized knowledge or experience.
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