Embeddings are dense vector representations that map discrete data (text, images, code, audio, graphs) into continuous high-dimensional spaces where semantic similarity corresponds to geometric proximity. They power modern AI applications including search, retrieval-augmented generation (RAG), recommendation systems, and classification. Unlike sparse representations that encode presence/absence, embeddings capture nuanced meaning and relationships through learned patterns, enabling machines to compare, cluster, and reason about complex data using distance metrics.