Language models, like those used in AI, process and generate text using tokens, which are units of text smaller than words but larger than characters. The way text is divided into tokens is determined by the model’s training on large datasets. Tokenization is a key step in processing text for language models, as it allows them to more efficiently encode, process, and generate coherent text. By analysing the probability of different tokens following a given sequence, the model can predict the most likely next token and generate text that mimics natural language patterns.