Securely blend advanced LLM with your own databases, documentation, and code repos using these techniques for enterprise-quality retrieval augmented generation.

Retrieval Augmented Generation, or RAG, is the gold standard for using domain-specific data, such as internal documentation or company databases, with large language models (LLMs). Creating trustworthy, stable RAG solutions you can deploy, scale, and maintain at the enterprise level means establishing data workflows that maximize accuracy and efficiency, addressing cost and performance problems, and building in appropriate checks for privacy and security. This book shows you how.

Inside Enterprise RAG you’ll learn:

• Build an enterprise-level RAG system that scales to meet demand
• RAG over SQL databases
• Fast, accurate searches
• Prevent AI “hallucinations”
• Monitor, scale, and maintain RAG systems
• Cost-effective cloud services for AI

Enterprise RAG goes beyond the theory and proof-of-concept examples you find in most books and online discussions, digging into the real issues you encounter deploying and scaling RAG in production. In this book, you’ll build a RAG-based information retrieval app that intelligently assesses data from common business sources, chooses the appropriate context for your LLM, and even writes custom SQL queries as needed.