An autoregressive model predicts the next value in a sequence by learning from the values that came before it. The core idea is that what comes next is shaped by what has already happened.
In large language models, the model generates text one token at a time. Each new token is conditioned on everything that came before it. This sequential generation process is how modern AI systems produce coherent, contextually relevant text rather than random strings of words. Most of the leading language models in use today follow this approac
