An embedding is a dense vector of numbers learned by a model to capture the semantic meaning of the original data (text, image, audio, etc.). Vectors that are semantically similar are close to one another in the embedding space, which enables downstream tasks such as similarity search or retrieval-augmented generation. It is not the raw token sequence, random diffusion noise, or any form of markup.
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Fundamentals of Generative AI
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