Generative AI Design Patterns
Solutions to Common Challenges When Building Genai Agents and Applications
$144.33
- Paperback
496 pages
- Release Date
21 October 2025
Summary
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 …
Book Details
| ISBN-13: | 9798341622661 |
|---|---|
| Author: | Valliappa Lakshmanan, Hannes Hapke |
| Publisher: | O'Reilly Media |
| Imprint: | O'Reilly Media |
| Format: | Paperback |
| Number of Pages: | 496 |
| Release Date: | 21 October 2025 |
| Weight: | 878g |
| Dimensions: | 177mm x 234mm |
You Can Find This Book In
About The Author
Valliappa Lakshmanan
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.
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.