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.
about the book Enterprise RAG teaches you to build production-ready RAG systems. The guide draws from authors Tyler Suard and Darshil Modi’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.
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Enterprise RAG (MEAP v2) 
