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What's RAG?

Retrieval Augmented Generation. In basic terms, it optimizes output of LLMs by using additional external data sources before answering queries. (That actually might be too basic of a description)

Here:

https://blogs.nvidia.com/blog/what-is-retrieval-augmented-ge...

Is it same as embedding? Is embedding an RAG method?

I don't think so. I think embedding is just converting token string into its numeric representation. Numeric representations of semantically similar token strings are close geometrically.

RAG is training AI to be a guy who read a lot of books. He doesn't know all of them in the context of this conversation you are having with him, but he sort of remembers where he read about the thing you are talking about and he has a library behind him into which he can reach and cite what he read verbatim thus introducing it into the context of your conversation.

I might be wrong though. I'm a newb.

Retrieval augmented generation.

> Retrieval Augmented Generation (RAG) is a technique where the capabilities of a large language model (LLM) are augmented by retrieving information from other systems and inserting them into the LLM’s context window via a prompt.

(stolen from: https://github.com/psychic-api/rag-stack)

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