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Thanks Jason, you're right. We spend so much time around RAG that we forget this is niche term. RAG -> retrieval augmented generation. Given a query, finding t
by dakshgupta 3y ago
Thanks Jason, you're right. We spend so much time around RAG that we forget this is niche term.
RAG -> retrieval augmented generation. Given a query, finding the parts of the codebase most relevant to the query, and supplying it to the LLM so it can answer the query. The typical way to do this is semantic similarity, or chunking + embedding the corpus being searched, embedding the query and finding the k most cosine-similar chunks.
- jasonkester 3y agoThanks. Even that is pretty dense for someone who hasn’t been living and breathing LLMs for the last year. https://xkcd.com/2501/ https://xkcd.com/2501/ You’ll want to find a balance between telling people what your thing does with no jargon or acronyms whatsoever, while signaling to people who know as much as you about this stuff (all four of them) that it’s got those cool ingredients. All the best.
- dakshgupta 3y agoHaven't seen that xkcd before, that's really funny. Good advice on calibrating the jargon level. Thanks!
- brandall10 3y agoA simpler way to put it is it's a technique to get around the context limit/attention issues of an LLM... to inject the context that is most relevant to the user's query.
- fragmede 3y agoRAG vs fine-tuning makes a difference to you, but not to your users, who don't need to understand how they're not the same thing in order to use your product.