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RAG stands for Retrieval Augmented Generation. The purpose is to search a corpus of text by meaning rather than exact match.

I had to look it up.

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That sounds more like semantic search and vector db.

RAG is simply fetching external data (retrieval) and adding it to LLM context (augmenting) prior to generating a final response.

Any time LLMs do a grep or a web search to answer the query, it’s RAG. Many people use vector db for their own RAG implementation bc of the semantic search benefits.

Because people writing about RAG never explained what RAG is and exclusively wrote about embeddings and vector dbs, for most people RAG became "embeddings + vector db".

People don't understand that any sort of retrieval before generation is RAG.

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