[rnd0]

>I wouldn't use either of them for RAG

What's RAG?

[sp332]

Since the models have a limited context size, you pre-process a bunch of data that might be related to the task (documentation, say) and generate a semantic vector for each piece. The when you ask a question, look up just the few pieces that are semantically most simlar and load them into the context along with the question. Then the LLM can generate a new answer with the most relevant pieces of data.

[andy99]

Retrieval augmented generative, basically giving it some text passage and asking questions about the text.

[dmezzetti]

If you want more on RAG with a concrete example: https://neuml.hashnode.dev/build-rag-pipelines-with-txtai