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Google’s come a long way since PageRank + terms. Ancient doesn’t mean bad, but usually it means outdated and that’s the case here. Search algos are subsumed by learning models, our species can do better now.

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So, I’m not entirely sure if I follow you here… How would one use a language model to find a document out of a corpus of existing documents? As opposed to finding an answer to a question, trained on documents, which I can see. I mean answering a query like “find the report containing X”?

I see search as encompassing at least two separate, but related, domains: information gathering/seeking (answering a question) and information retrieval (find the best matching document). I’m curious how LLMs can help with the later.

That's the 'vector search' people are talking about in this discussion. Use the LLM to generate an embedding vector that represents the 'meaning' of your query. Do the same for all the documents (or better with chunks of all the documents). Find the document vector that's closest to your query vector and you have a document that has a 'meaning' similar to your query. Obviously that's just a starting point. And lots of folks are doing hybrid where they combine bm25 search with some sort of vector search (e.g. run them in parallel and combine results, or do a bm25 and then use vector search to rerank the top results).

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