Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Enterprise Rag: Scaling Retrieval Augmented Generation

Enterprise Rag: Scaling Retrieval Augmented Generation

Suard, Tyler

Paperback

DatabasesGeneral Computers

PREORDER - Expected ship date October 27, 2026

ISBN10: 1633435474
ISBN13: 9781633435476
Publisher: Manning Publications
Published: Oct 27 2026
Pages: 225
Weight: 0.59
Language: English
Free PDF and epub formats plus online reader with AI assistant.

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.

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