Glossary

Tokens

Tokens are the fundamental units of text processing used by large language models to represent sequences of characters. They function as numerical representations of words, sub-words, or individual characters, allowing models to parse, interpret, and generate human language by converting input data into a format that can be processed through mathematical vector spaces.

Tokens are critical because they dictate both the computational cost and the operational limits of AI systems. Every interaction with an LLM consumes a specific volume of tokens, which directly impacts latency, API expenses, and the model's effective context window. For practitioners, understanding tokenization is essential for managing budget efficiency and ensuring that complex prompts remain within the constraints of a model's architecture, as exceeding these limits often results in truncated outputs or the loss of critical information during processing.

In practice, tokenization varies significantly between models; for instance, a single word might be represented as one token in one architecture but split into three in another. Users should monitor token usage patterns to optimize prompt engineering, as dense, efficient text reduces overhead. When working with automated agents or long-form content generation, it is necessary to account for both input and output token counts to maintain predictable performance and avoid the performance degradation associated with approaching the maximum context length of a specific model.

Last updated: 2026-09-08