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.
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.