Vector databases, explained simply

Vector databases sound intimidating, but the core idea is simple: turn meaning into numbers, then find the numbers that are close together.

Meaning as coordinates

An embedding places a piece of text somewhere in a vast space, where similar ideas land near each other. Search becomes a question of distance.

That is the whole trick behind retrieval-augmented generation: fetch the nearby, relevant context and hand it to the model so its answers stay grounded.