Embeddings

An embedding turns a piece of data into a list of numbers, a vector, that captures its meaning. Two sentences that mean similar things produce vectors that sit near each other in that space, even if they share no words. That’s the trick behind semantic search: instead of matching keywords, you compare vectors and find the closest ones.

Embeddings are the foundation of retrieval-augmented generation. When you store documents in a vector database, you’re storing their embeddings; when a question comes in, you embed it too and pull back the chunks whose vectors are nearest. The quality of the embedding model decides how well “near in vector space” matches “actually relevant” — which is why embedding choice is one of the levers that makes or breaks a retrieval system.