How ChatGPT Actually Works: Tokens, Attention, and Next-Word Prediction Explained

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ChatGPT does not truly understand language — it repeatedly predicts the most plausible next token based on patterns learned during training. Words are represented as mathematical vectors in a geometric space, allowing the model to capture relationships between concepts like similarity and analogy. A mechanism called attention determines which earlier words in a conversation influence each new prediction. The model is first trained on vast text data as an autocomplete system, then refined using human feedback to behave like a helpful assistant. Quirks such as hallucinations, forgotten context, and inconsistent outputs all stem directly from this underlying token-prediction architecture.

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