Embeddings Aren’t Magic: The Geometry of How Vectors Encode Meaning

Modern AI converts text, images, and audio into high-dimensional vectors called embeddings. Similarity becomes geometry, measured using L1, L2, Chebyshev, or cosine metrics. Each distance metric reshapes how meaning is interpreted. Vector databases rely on nearest-neighbor search in this space. Intelligence, at its core, is geometry operating in high dimensions.