Embeddings are everywhere in modern AI, but the word "embedding" is doing a lot of…
This is a dev post classified by Jev as Databases (a free resource), kept by the Dev Radar because it carries real work, not commentary.
Embeddings are everywhere in modern AI, but the word "embedding" is doing a lot of different jobs. A token embedding is a learned row in a vocabulary matrix. A contextual embedding is a hidden-state vector whose value depends on the surrounding sequence. A sentence or document embedding usually requires another step => some pooling or model-specific readout that turns variable-length representations into one fixed-size vector. These are related ideas, but they are not interchangeable. The geometry is learned too. There is nothing inherently semantic about a vector just because it has 768 or
Posted by Tech with Mak (44.8k followers) 2 days ago · 614 likes · 15.7k views · view the original post on X. Kept by the Dev Radar as Databases.
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