A RAG framework contains an LLM paired with a knowledge base.

A RAG process takes a query and assesses if it relates to subjects defined in the paired knowledge base. If yes, it searches its knowledge base to extract information related to the user’s question. Any relevant context in the knowledge base is then passed to the LLM along with the original query, and an answer is produced.

This helps with two things: firstly, it reduces the risk of hallucinations, and secondly, it reduces the chances that an LLM will leak sensitive data, as you can leave it out of the training data.

The knowledge base can also be a recommender system, which will allow the LLM to extract context and feed that into the recommender that, in return, delivers crisp recommendations. (this idea is investigated in the RecSys23 article: Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models (https://lnkd.in/dHvK8SNJ)
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