#embeddings

An embedding space is a latent vector space where items such as words, documents, images, users, or products are represented as numerical coordinates. Items with related meanings or features are positioned near one another, which lets models compare similarity, retrieve neighbors, cluster concepts, and perform operations such as interpolation or vector addition.

“A search system can embed both a question and an article into the same embedding space, then retrieve the article whose vector is closest to the question.”

Latent operations are edits, traversals, or measurements performed on vectors inside a model latent space. Common operations include shifting a vector along a learned direction, interpolating between two vectors, subtracting one concept vector from another, masking dimensions, or sampling nearby points. These operations are used to explore how a model organizes concepts and to guide generation without rewriting the whole model.

“A designer might interpolate between two image embeddings to create a smooth transition from one visual concept to another.”