Faster Retrieval for Better, More Efficient RAG

Re:Faster Retrieval for Better, More Efficient RAG by fcarraldo on Wednesday November 11, 2026 @08:42PM (#50461921)

> I found that just building a BM25 index, and giving the LLM a BM25 "search tool" and casting a "wide net" wins every single time compared to learned methods

Funny, I did exactly this for my last RAG project. Works great for a small dataset.

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Re:Faster Retrieval for Better, More Efficient RAG by greesil on Wednesday November 11, 2026 @11:13PM (#50462997)

For some reason I thought everyone was using BERT vectors and cosine similarity to do this RAG stuff. This is not my jam, but I do dabble. Neat!

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Re:Faster Retrieval for Better, More Efficient RAG by freakynit on Wednesday November 11, 2026 @11:24PM (#50463061)

Same here. Up to a few millions of documents, this is already great. Combine this with a few other simple techniques and extra metadata during ingestion, and you already have a system that practically useable.

I have tried this by ingesting all of cloudflare's highly technical complex documents (more than 3000 A4 sized pages) and then comparing results with that of cloudflare's own chatbot in their dashboard.

In all cases my system either matched, or performed marginally better than cloudflare's own bot.

I do use a few more techniques though, but, not RDF triplets, and definitely no custom trained neural network.

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