Embeddings
Embeddings are numeric vector representations of text (or other data) that place similar meanings close together, enabling semantic search and RAG.
An embedding model converts a piece of text into a vector — a list of numbers — such that semantically similar texts produce nearby vectors. This lets you measure meaning by distance rather than keyword overlap.
Embeddings power semantic search, clustering, deduplication, and the retrieval step in RAG. You embed your documents once, store the vectors, and at query time embed the question and find the nearest documents.
Choice of embedding model, chunk size, and the vector store all shape retrieval quality — which in turn shapes the quality of anything built on top, including RAG answers.