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3
Embeddings
Each token ID gets converted into a list of numbers — called an embedding vector — that captures something about its meaning. Think of it like giving each word a unique address in a giant map of meaning. Words with similar meanings end up with similar addresses, so "dog" and "puppy" are neighbors, while "dog" and "democracy" are far apart.
Common misconception: Embeddings are not hand-crafted definitions. The model learned these number-coordinates entirely from training data by observing which words appear in similar contexts billions of times. No human ever wrote "this is what dog means."
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