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.
Ask HN: Lessons from Building a Fortune 500 RAG Chatbot (50M Records in 10–30s)
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#9I 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?
I hope all that helps, let me know if you have any other questions!
Re: Ask HN: Lessons from Building a Fortune 500 RAG Chatbot (50M Records in 10–30s)
#10Focusing 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?
What tradeoffs? It is fast and accurate, but it does get expensive when you have over 50 million records.