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Behind the reply

This is an illustrative simulation with scripted answers and example values.

How did it learn?

During training, predictions are compared with examples and the model’s numerical settings are adjusted. Further training helps shape how it follows instructions and responds. Adding a chat message changes the current context; it does not ordinarily retrain those settings.

One piece at a time

The model uses the available context to predict the next text piece. A piece is selected and added to the reply, then the process repeats. This illustration uses 24 layers; models differ. Real systems usually reuse earlier calculations.

Instructions shape the reply

The app can supply instructions alongside your message. Changing them can change the style of the answer without retraining the model.

Limits of the illustration

The blocks, attention lines and probabilities are teaching examples. Text likelihood is not the probability that an answer is true. Context holds information for the current reply; learned patterns and optional app memory are separate.

Learn more about Transformers

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