Generative AI Design Patterns

□ I. THÔNG TIN SẢN PHẨM
□ Mã sản phẩm : STT1723
□ Nhà xuất bản : O'Reilly Media
□ Tác giả : Valliappa Lakshmanan and Hannes Hapke
□ Ngôn ngữ : Tiếng Anh
□ ISBN : 9798341622661
□ Số trang : 506 trang
□ Hình thức : Bìa Mềm, RUỘT IN ĐEN TRẮNG, BÌA IN MẪU LASER GIẤY C300 CÓ CÁN
□ Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
□ Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và highlight thoải mái.
□ Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

□ II. MÔ TẢ SẢN PHẨM
□ 1.Mô tả sản phẩm đầy đủ
Generative AI enables powerful new capabilities, but these come with significant limitations that developers must address to ship reliable applications or agents. In "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications," experts Valliappa Lakshmanan and Hannes Hapke provide a comprehensive library of 32 tried-and-true design patterns. These patterns are specifically designed to address the most frequent hurdles encountered when building applications with Large Language Models (LLMs) and other generative models, including issues like hallucinations, nondeterministic responses, and knowledge cutoffs. The book codifies years of research and real-world engineering experience into practical advice that can be immediately integrated into professional projects. Each pattern is structured to provide maximum utility, beginning with a clear description of the problem, followed by a proven pattern to solve it. Crucially, every pattern includes a fully coded example and a deep dive into the potential trade-offs, ensuring that developers understand not just how to implement a solution, but also when and why it is appropriate. Key areas covered in the book include controlling content style to follow specific grammar or tone, extending model capabilities while balancing risk, and improving reliability by designing around inherent LLM limitations. It also explores enabling agents to take action—building systems that can plan, self-correct, and collaborate—and composing multiple patterns into complex agentic workflows. Whether you are a machine learning engineer, a software developer, or an AI architect, this guide serves as both an inspirational deep-dive and a daily reference for troubleshooting production-level AI problems. By focusing on durable principles and repeatable patterns rather than specific ephemeral frameworks, Lakshmanan and Hapke offer a robust framework for building the next generation of intelligent systems that are scalable, reliable, and effective in real-world environments.

□ 2. Tác giả
Valliappa (Lak) Lakshmanan is the Co-founder and CTO of Obin AI, a startup building deep domain agents for finance. Previously, he served as a Director for Data Analytics and AI Solutions at Google Cloud and as a Research Scientist at NOAA. He is a Fellow of the American Meteorological Society and has authored numerous O'Reilly books on data science and machine learning. Hannes Hapke is a Senior Machine Learning Engineer at Digits and an active contributor to the TensorFlow community. He has co-authored several technical books, including "Building Machine Learning Pipelines" and "Machine Learning Production Systems."