Tokens

Tokens are the basic building blocks of text that AI language models process, encompassing parts of words, complete words, punctuation marks, or special characters. Tokenization refers to breaking human language into these units so AI models can interpret and manipulate text mathematically.

Tokens do not directly correspond to word count. In English, roughly 1 token equals about 0.75 words, though this ratio varies depending on the tokenizer and language used. Longer words, symbols, and non-English text often consume more tokens.

Understanding tokens is essential for working with AI systems because models have limits on the number of tokens they can process, pricing is frequently based on token usage, context windows are measured in tokens, and API constraints often rely on token counts.

For content creators and GEO strategies, being mindful of tokens is important because it affects how much information AI can handle at once, impacts the cost of AI-powered tools, and influences how thoroughly AI can analyze extensive documents.

AI models employ different tokenization methods, such as byte-pair encoding (BPE), WordPiece, and SentencePiece. Optimizing content for AI involves clear, concise writing that minimizes token usage, while technical terminology or repetitive phrasing can increase token counts unnecessarily.