AI-generated text primarily relies on large language models (LLMs) trained extensively on vast datasets of text and code. These models, often employing a transformer architecture, learn intricate patterns, grammar, and contextual relationships within the data. When prompted, the AI breaks down the input into tokens and then iteratively predicts the most statistically probable next token based on the preceding sequence. This process involves calculating probabilities for potential subsequent words or phrases, leveraging the deep understanding of language acquired during its training. By repeatedly selecting the next most likely token, the model constructs coherent and contextually relevant text, effectively mimicking human writing styles and knowledge. The ultimate output is a sequence of words designed to fulfill the user's prompt based on the patterns it has learned. More details: https://epi-us.com