Ofer Mendelevitch, Forrest Sheng Bao | Hands-On RAG for Production. Design, Develop, and Deploy Production-Ready RAG Applications (2026) [PDF, EPUB]
Автор: Ofer Mendelevitch, Forrest Sheng Bao
Издательство: O'Reilly Media
ISBN: 979-8341621718
Жанр: Natural Language Processing, Generative AI, Computer Science
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications.
Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them.
Determine whether to build RAG yourself or deploy a RAG-as-a-service platform
Build a basic RAG stack that maximizes performance and cost-effectiveness
Measure key metrics such as hallucinations, response quality, latency, and cost
Address challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt design
Implement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG
Foreword by Sharon Zhou
Foreword by Jim Dowling
Preface
Chapter 1. Introduction to Retrieval-Augmented Generation (RAG)
Chapter 2. The Base RAG Stack
Chapter 3. Scaling Your RAG Stack
Chapter 4. Deploying RAG to Production
Chapter 5. The RAG Platform
Chapter 6. Evaluating Your RAG Application
Chapter 7. From RAG to AI Agents
Chapter 8. Multimodal RAG
Chapter 9. Knowledge-Enhanced RAG
Chapter 10. The Future of RAG
Index
About the Authors
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
