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
