Tokens

Tokens are the fundamental units of text that AI language models process, representing pieces of words, whole words, punctuation, or special characters. Tokenization is the process of breaking down human language into these smaller components that AI models can understand and manipulate mathematically.

The number of tokens differs from word count: generally, 1 token equals approximately 0.75 words in English, though this varies based on the specific tokenizer used. Complex words, special characters, and non-English languages often require more tokens.

Understanding tokens is crucial for working with AI systems because most models have token limits for inputs and outputs, pricing is often based on token usage, context windows are measured in tokens, and API rate limits frequently use token counts.

For content creators and GEO optimization, token efficiency matters because it affects how much content AI systems can process at once, influences the cost of AI-powered applications, and determines how comprehensively AI systems can analyze long-form content.

Different AI models use different tokenization methods: byte-pair encoding (BPE), WordPiece tokenization, and SentencePiece tokenization are common approaches. When optimizing content for AI systems, consider that concise, clear writing typically uses fewer tokens, technical jargon may require more tokens, and repetitive content wastes token allocation.