Embeddings su ne wakiloki na lamba (vectors) na bayanai (rubutu, hotuna, da sauransu) waɗanda suke kama da ma'anar ma'ana — suna ajiye abubuwanda suka kama jiya a wuraren vector. Sune ginshikin mahimman AI na jiya, suna ba da damar semantic search, shawarwari, da RAG.
Abin da embeddings ne
EMBEDDING → a VECTOR (list of numbers) representing data (a word, sentence, image, etc.):
→ captures MEANING → semantically similar items have SIMILAR vectors (close in vector space)
→ e.g. 'king' and 'queen' have similar embeddings; 'cat' and 'dog' are closer than
'cat' and 'car'
→ produced by models (embedding models) that learn meaningful representations
→ turns data into numbers that capture semantic meaning (meaning as geometry)
