This article is interesting cause of its scale, but does not touch on how to properly use RAG best practices. We wrote up this blog post on how to actually build a smart enterprise AI RAG based on the latest research if it's interesting to anyone: https://bytevagabond.com/post/how-to-build-enterprise-ai-rag...
It's based on different chunking strategies that scale cheaply and advanced retrieval
Interesting to see still solutions being developed for RAG. We developed a solution similar to yours: Automatic indexing from GDrive, SharePoint etc. and then advanced hierarchical chunking, context header based markdown conversion etc... All the tricks that were published last year while RAG was still the "new" kid in town. We finally open sourced everything as the competition from the big players (Notion AI, Google etc.) was daunting. If anyone is interested, this blog post about all the techniques we tried and what actually works is still relevant and up2date: https://bytevagabond.com/post/how-to-build-enterprise-ai-rag...
After building enterprise RAG from scratch, sharing what I learned the hard way. Some techniques I expected to work didn't, others I dismissed turned out crucial. Covers late chunking, hierarchical search, why reranking disappointed me, and the gap between academic papers and messy production data. Still figuring things out, but these patterns seemed to matter most.