RAG Systems at Scale

Master the architecture behind enterprise-grade Retrieval-Augmented Generation (RAG).

As organizations race to deploy AI assistants, knowledge copilots, and intelligent search platforms, one challenge remains: delivering accurate, trustworthy, and scalable answers from enterprise knowledge. RAG Systems at Scale: Design Patterns & Vector Stores is a practical guide for architects, AI engineers, platform teams, and technology leaders building production-ready RAG solutions.

Learn how to design high-performance retrieval pipelines, select the right vector database, optimize embeddings and chunking strategies, implement hybrid search, reduce hallucinations, improve observability, and govern enterprise knowledge systems with confidence. Explore real-world architecture patterns, Graph RAG, Agentic RAG, multi-hop reasoning, long-context systems, security frameworks, evaluation methodologies, and cost optimization strategies used by modern enterprises.

Packed with practical lessons, architecture tradeoffs, operational insights, and enterprise case studies, this book provides a blueprint for building reliable AI knowledge platforms that can scale from thousands to millions of users and from millions to billions of documents.

Whether you're designing your first RAG platform or scaling an enterprise AI ecosystem, this book delivers the real-world guidance needed to build trustworthy, production-grade AI systems.