An embedding model converts inputs — words, sentences, images, or audio — into dense numerical vectors where semantically similar items cluster together. Popular models include OpenAI's text-embedding-ada-002, Sentence-BERT, and CLIP. These models power semantic search, recommendation systems, and RAG pipelines. The quality of embeddings directly determines downstream AI application performance.