Ask HN: How are you extracting the best performance out of your RAG pipeline?
I've been working on various RAG (Retrieval Augmented Generation) projects, and I'm curious if you all are seeing any generalizeable patterns in building the most performant RAG for any given dataset? For eg: is it even possible to say that in “most” cases, the best retriever setup is going to be a combination of semantic search (embeddings) + keyword search (BM25) + some xyz technique?My...