Contextual embeddings convert words or data into numerical vectors that capture meaning based on the surrounding context. Unlike static embeddings where a word always maps to the same vector, contextual embeddings produce different representations depending on how a word is used.
The word "bank" means something different in "river bank" and "bank account," and contextual embeddings reflect that distinction. This makes them significantly more useful for retrieval and reasoning tasks where meaning depends on context — which is most enterprise language tasks.
