The LLM produces a probability distribution over the likelihood of all possible next tokens. So whatever the tokens are, "ch", "ex", etc. the next one gets a probability.

During training, real life text is fed through the LLM, and rhe "correct" token is the one actually observed in the training text. Here's a recent video walkthrough in some detail, mostly aimed at providing a deeper understanding than "next token predictor function":

https://youtu.be/GlYgs6v2YfU?is=IxVMhoCCE4N4WRVK

(Start at 15:30 for the LLM specific parts)