An embedding model turns "how do I reset my password" and "forgot my login credentials" into vectors that sit near each other, even though they share almost no words. That makes meaning searchable: embed your documents once, embed the query, and find the nearest neighbors.

This is the machinery under semantic search, RAG, recommendation, and clustering — including the related-articles links on this site, which come from comparing article embeddings.