Vector search is not enough.
If your RAG system struggles with error codes, exact identifiers, or rare technical terms, BM25 and hybrid retrieval might be the missing piece.
A breakdown of BM25, Hybrid Search, RRF, and Ensemble Retrievers. https://t.co/Sl1Z7e8pXE
I've done sales calls to Indian clients for my previous two products multiple times and never fumbled once.
Today, for the first time, I made sales calls to leads in the US and spoke absolute gibberish. ππ
I keep seeing people confuse fine-tuning with RAG, so here's how I understand the difference.
Fine-tuning is when you train a model on examples to change how it behaves or responds.
RAG is when you fetch relevant information from external documents and pass it to the model while answering.
For example, if you want a model to follow a particular writing style, fine-tuning can help.
But if you want it to answer questions based on your company's latest reports, RAG makes more sense.
You can also combine both. They're meant for different things.
You showed the world that human perseverance can go toe-to-toe with God-given talent , you've inspired millions if not billions across the globe , this will forever be your victory .
Sam Altman says people having too much RAM in their personal computers is a waste of the world's limited supply.
He says that memory should be used in AI data centers instead.