Tokenization

Tokenization is how AI models digest text. Before processing language, models must break it into manageable pieces called tokens. These might be words, parts of words, or even individual characters, depending on the system's design.

Different tokenization methods affect model performance and costs. Word-based tokenization is intuitive but struggles with rare words. Character-based tokenization handles any text but creates longer sequences. Subword methods like Byte Pair Encoding balance these concerns by breaking words into common components.

The choice impacts everything from model accuracy to computational efficiency. For instance, a word like "unhappiness" might become three tokens: "un," "happy," and "ness" in subword tokenization, allowing the model to understand meaning even if it hasn't seen this exact word before. Technical terms, brand names, and specialized vocabulary often split into multiple tokens, which can affect how well models understand domain-specific content. This explains why AI sometimes struggles with proper nouns or industry jargon; they fragment into less meaningful pieces during tokenization.

For marketers using AI tools, understanding tokenization explains input limitations and processing costs. Most models have token limits per request, and complex text with unusual words or multiple languages may consume tokens faster than expected. Efficient writing can maximize what you can accomplish within these constraints.