This is for LLMs. In general RAG takes a user prompt and uses it to find potentially relevant documents in the database. It then enriches the original prompt with those documents so that the LLM has context that wasn't in its training dataset.

RAG -> Vector search -> means that your documents are not indexed as full text but as Vectorized objects which mean that then you can search using concepts instead of exacts strings you would use with a regular "Fulltext search".

This makes the search less precise and more powerful at the same time (ie it could look clever to some extent).