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.
It 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.
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
About the book
Enterprise RAG teaches you to build production-ready RAG systems. The guide draws from author Tyler Suard's real-world experience developing effective RAG solutions for Fortune 500 companies. Learn to utilize agent-based retrieval, triage logic, query rewriting, and other cutting-edge strategies for effective RAG. Plus, essential tips and advice ensure you can sidestep RAG's landmines and handle common problems, from picking the right LLM to handling hallucinations and inaccurate completions.
About the reader
For software developers proficient in Python.
About the author
Tyler Suard is a Senior AI Researcher and Developer at a fortune 500 company.
