Lessons from Building a Fortune 500 RAG Chatbot (50M Records in 10–30s)
I’ve spent the past year and a half constructing a Retrieval Augmented Generation (RAG) chatbot for a Fortune 500 manufacturing company, integrating over 50 million records across a dozen databases. Despite that scale, the system can return relevant info in 10–30 seconds, and it’s now at 90% five-star user approval internally.
After tons of trial and error—embedding huge datasets, mixing vector + text search, handling concurrency, and dodging hallucinations, I decided to document it all in a book. It’ll be live on Manning.com’s Early Access soon (March 27th). If you’re tackling large-scale RAG or have questions about my approach (the struggles, the successes), feel free to ask. I’m happy to share lessons, config ideas, or gotchas so you can avoid the pitfalls I hit along the way.
I really love to hear about details. I have plan to build a RAG based on regulations. It is very hard because source are different and reading and interpreting legal documents completely different area of expertise. I can't answer some questions?
- How can I start small in very specific area?
- How can I grow it?
- How to validate and measure success of the RAG solution?
- How to feed with data continuesly?
Focusing on the RA part of the RAG, which techniques or tools would you say contributed the most to the quality of the results? What sort of tradeoffs did you have to make?
How did you measure success of the RAG solution beyond five-star user approvals? Are there any critical metrics that determine success or failure?
When you are dealing with documents with different structures, how to do the document chunking efficiently without losing important metadata?
do share the db you used for starters and the overall stack like MERN or MEAN or Firestore BAAS or Supabase or something extremely different..
27th marked!
What infrastructure did you use?
the title will be "Lessons from Building a Fortune 500 RAG Chatbot"?