What are embeddings in machine learning?

Answer

Embeddings are dense numerical vectors that represent text, images, or other data in a continuous space where semantic similarity corresponds to geometric closeness. A model maps each input to a fixed-length vector so that related items sit near each other, enabling similarity search, clustering, and classification. Embeddings power semantic search and retrieval-augmented generation, where queries and documents are compared by the distance between their vectors.