Embeddings: Meaning as Geometry

An embedding maps each token to a point in a high-dimensional space — typically hundreds or thousands of coordinates. The training objective is simple: tokens used in similar contexts end up close together.

'Cat' and 'kitten' land as neighbors; 'cat' and 'carburetor' end up far apart. The model never reads a dictionary — proximity is meaning.