How Language Models Work (in Plain English) › How the Model Makes Predictions

Once trained, here's what happens when you give the model some text:

You provide a prompt (the starting text):

The model processes it:

Internally, the model converts your prompt into tokens and runs them through its neural network (a complex mathematical structure with billions of parameters). Think of it as a very sophisticated pattern-matching machine.

The model predicts the next token:

Based on everything it learned during training, the model calculates probabilities for what token should come next. It might determine:

• "The" $\to$ 35% likely
• "It's" $\to$ 20% likely
• "Because" $\to$ 15% likely
• "Sky" $\to$ 10% likely
• ... and thousands of other possibilities with lower probabilities

The model picks a token:

Usually, it selects a high-probability token (though not always the absolute highest; a bit of randomness keeps responses interesting). Let's say it picks "The."

The model repeats:

Now the prompt is "Explain why the sky is blue in simple terms: The" and the model predicts the next token again. Maybe it picks "sky." Then "appears." Then "blue." And so on, building up a complete response one token at a time:

The model keeps generating tokens until it decides it's done (often by predicting a special "end" token) or hits a length limit.