Generative AI Design Patterns
Solutions to Common Challenges When Building Genai Agents and Applications
Published on 21. October 2025
Book
Paperback/Softback
496 pages
979-8-3416-2266-1 (ISBN)
Description
Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs.
This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs.
Design around the limitations of LLMs
Ensure that generated content follows a specific style, tone, or format
Maximize creativity while balancing different types of risk
Build agents that plan, self-correct, take action, and collaborate with other agents
Compose patterns into agentic applications for a variety of use cases
This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs.
Design around the limitations of LLMs
Ensure that generated content follows a specific style, tone, or format
Maximize creativity while balancing different types of risk
Build agents that plan, self-correct, take action, and collaborate with other agents
Compose patterns into agentic applications for a variety of use cases
More details
Language
English
Place of publication
Sebastopol
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 231 mm
Width: 173 mm
Thickness: 30 mm
Weight
876 gr
ISBN-13
979-8-3416-2266-1 (9798341622661)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Valliappa (Lak) Lakshmanan works closely with management teams across a range of enterprises to help them employ data and AI-driven innovation to grow their businesses. Previously, he was the Director for Data Analytics and AI Solutions on Google Cloud and a Research Scientist at NOAA. He co-founded Google's Advanced Solutions Lab and is the author of several O'Reilly books and Coursera courses. He was elected a Fellow of the American Meteorological Society (the highest honor offered by the AMS) for pioneering machine learning algorithms in severe weather prediction. Hannes Hapke is a Senior Machine Learning Engineer at Digits, and has co-authored multiple machine learning publications, including the book Building Machine Learning Pipelines and Machine Learning Production Systems by O'Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC or O'Reilly's TensorFlow World and is an active contributor to TensorFlow's TFX Addons project. Hannes is passionate about machine learning engineering and production machine learning use cases using the latest machine learning developments.