Machine-learning technique representing data as vectors in high-dimensional space, clustered by semantic meaning. LLMs encode text meaning and place it somewhere in the embedding space. OpenAI's Ada embedding model outputs 1536-dimensional vectors. Semantic (vector) search computes distances from a query vector to points in a vector database using Manhattan, Euclidean, or cosine similarity.