# 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.

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- Price: 58.68 USD
- Previous price: 79.99 USD
- Discount: 27%
- Price checked: 2026-11-14T05:50:02.965Z
- Rating: 4.6 / 5
- Reviews: 28

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## 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.

## 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.

## Specifications

### Publication details
- Publisher: O'Reilly Media
- Publication date: November 11, 2025
- Edition: 1st
- Language: English
- Print length: 506 pages
- ISBN-13: 979-8341622661
### Dimensions and installation
- Weight listed: 1.92 pounds
- Dimensions: 7 x 1.02 x 9.19 inches

## Before you buy

- 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.

## 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 feedback

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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## Add-ons

- [AI Engineering: Building Applications with Foundation Models](https://best-deals.pro/visit/generative-ai-design-patterns-book)
- [Building Applications with AI Agents: Designing and Implementing Multiagent Systems](https://best-deals.pro/visit/generative-ai-design-patterns-book)

## Images

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