That’s the question I have to answer, but a twist. Explain it like I’m talking with someone who cares little about tech.
Let me begin with an analogy. You walk into a big liquor store filled with rows and rows of beer. Some domestic but most imported. You’re looking for something special: a dark beer with a bit of a bite.
You talk to a knowledgeable store person and they point you to three areas filled with IPAs and German lagers. You say thanks because you would never have found those on your own, and never by reading the labels. The shelves are aren’t marked “strong” but the floor staff understood your meaning.
Now we have a hint of how a vector embedding work. A vector database stores information on the basis of meaning. It ingests content and stores information by meaning, and not keywords. In a RAG (retrieval augmented generation) system, the search tool breaks down queries into meaning rather than exact word matches.
Vectors are used twice in a RAG system: once with indexing (the information is added to the database) and once with the query (the search).
Back to the beer store, imagine there were no staff. We’d be stuck with a simple search: looking shelf tags and beer labels. We might get lucky and find a label with our search word or we could just guess and hope the selection matches our intent.