Generative Ai Design Patterns: Solutions to Common Challenges When Building Genai Agents and Applications - Couverture souple

Lakshmanan, Valliappa; Hapke, Hannes

Couverture souple

ISBN 13 : 9798341622661

Editeur : O'Reilly Media, 2025

Synopsis

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 are already hard at work compiling a library of tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs and other GenAI models--hallucinations, nondeterministic answers, and knowledge cutoffs among them. You'll find 31 of the most essential here.

Authors Valliappa Lakshmanan and Hannes Hapke codify advances in research and real-world experience into advice that you can readily incorporate into your projects. Each detailed explanation includes a description of the problem, a proven pattern to solve it, an example, and a discussion of potential trade-offs. Whether you read it cover to cover for inspiration or use it as a daily reference, this practical guide will help you troubleshoot whatever problems may arise.

• Design around the limitations of LLMs, such as hallucination and nondeterminism
• Force LLMs to generate text that follows a specific style or grammar
• Maximize creativity while balancing different types of risk
• Extend the capability of an LLM beyond just content creation
• Use patterns together to solve a variety of different use cases

A propos du livre

À propos des auteurs

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.

Détails de l'édition

• Éditeur: O'Reilly Media
• Date d'édition: 2025
• Langue: anglais
• ISBN 13: 9798341622661
• Reliure: Broché
• Nombre de pages: 496
• Coordonnées du fabricant: O'Reilly; https://oreilly.com; ; 1005 Gravenstein Highway North; Sebastopol; CA; 95472; Etats-Unis
• Personne responsable: gpsr@libri.de; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne