A token is the basic unit of text that an AI language model processes, typically corresponding to a word, part of a word, or punctuation character. When text is submitted to an AI model, it is first broken into tokens by a tokenizer, and the model operates on this sequence of tokens rather than on raw characters or whole words. Understanding tokens is relevant to working with AI generation systems because most models have limits on the number of tokens they can process at once and charge for usage in terms of tokens consumed.

Different tokenization schemes divide text into tokens differently. In common schemes, frequent words like "the" or "is" are single tokens, while less common words may be split into two or three tokens, and unusual or technical terms may require more. A rough rule of thumb for English text is that one token corresponds to approximately three to four characters or three quarters of a word, meaning a hundred tokens represents roughly seventy-five words. In image and video generation models, the concept of tokenization extends to visual information: some architectures convert image patches into visual tokens that the model processes in a way analogous to text tokens, enabling text and image information to be processed together in a unified representation. Token limits define the maximum prompt length a model can accept, the maximum output length it can generate, and the size of the context window it can hold in memory during a session.

For creators working with text-heavy prompts in AI generation, understanding token limits helps explain why very long, detailed prompts may be truncated or may produce worse results than more concise, focused descriptions. Prioritizing the most important information at the beginning of a prompt ensures that the most critical details fall within the portion of the prompt the model weighs most heavily, regardless of total prompt length.