An embedding model converts “how do I reset my password?” and “I forgot my login” into vectors that are close together, even though they share few words. Measuring the distance between vectors enables semantic search, recommendations, clustering and deduplication.
Embeddings are the foundation of retrieval-augmented generation, where relevant documents are found by vector similarity and passed to a language model.