[Project] I built an enterprise-scale Retrieval Augmented Generation system for a Fortune 500 company—now I’ve written a book on it!

Hey r/MachineLearning,

I spent the past year and a half building a Retrieval Augmented Generation system for a 100-year-old Fortune 500 manufacturing company with over 50,000 employees and 5 million products. The chatbot we ended up deploying answers thousands of queries a day, searches over 50 million records in 10 to 30 seconds, and it’s officially become the go-to tool for our customer support team. I coded the entire pipeline—from chunking our mountain of PDFs to hooking up concurrency for parallel lookups—and tested it until it was stable enough to hand off.

Because so much of this work was trial-and-error and there’s no single “classic RAG textbook” yet, I decided to write one. I’m excited to announce that on March 27th, you can preview my upcoming book on Manning.com, where I’ll walk you through all the steps, pitfalls, and best practices for building a RAG system that can handle real enterprise demands. This covers everything from chunking strategies and embedding pitfalls to asynchronous searches and user feedback loops, plus all the lessons I learned from blowing up my dev environment a few times.

If you’re looking to build something similar, beyond a cool proof-of-concept that breaks under pressure, I hope you’ll check out the MEAP (Manning Early Access Program) on March 27th. You’ll get to read early chapters, leave feedback, and help shape the final release. By sharing my roadmap for ingesting huge datasets, rewriting queries, triaging questions, and merging vector-based and text-based searching, I aim to save you the frustration of reinventing the wheel. Whether you want to glean practical tips from my success story or see how to handle data on this scale, this book is for you. Feel free to ask any questions—I’m happy to discuss!