Tokens vs. Embeddings: Two Completely Different Things
Tokens vs. Embeddings: Two Completely Different Things
Many people talk about tokens and embeddings – often meaning “something to do with AI.” But they are two completely different things.
🔢 Tokens: The Text Building Blocks
Tokens are the text building blocks that a model works with. A sentence is broken down into small units (subwords, words, characters). The more text, the more tokens. Tokens are essentially a counting unit for input/output.
🧭 Embeddings: The Meaning Representation
Embeddings, on the other hand, are a meaning representation of text as a numerical vector. Imagine: “dog” doesn’t become “4 tokens,” but rather an arrow in a meaning space that places “dog” close to “puppy” and “animal” – and far away from “tax notice.”
💡 The Key Point
The length of an embedding does not depend on the number of tokens.
Why? Because an embedding typically has a fixed dimension (e.g., 768 or 1536 numbers) – regardless of whether you embed a single word or an entire paragraph. The model “compresses” the content into the same vector length, just like a photo can always be 1024x1024 pixels, no matter how much or how little is happening in the image.
What Depends on What
- What depends on tokens: Computational effort and context processing when generating the embedding (more tokens = more to process).
- What does not depend on tokens: The size/dimension of the embedding itself.
🔹 In Short
- Tokens = “How much text?” (counting unit)
- Embeddings = “What does the text mean?” (vector of fixed length)
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