Supportsoft Glossary

Tokens and Tokenization in Language Model Processing

Language models rely heavily on tokens to process text. Tokenisation involves separating text into various sections like words, subwords, or characters so they can be interpreted by computers.

Tokenisation determines how the text is processed and produced by a language model. Model performance, accuracy, and context management depend on how the tokenisation method used is developed. For instance, tokenisation can break up sentences into distinct words/segments or break them down into smaller segments, depending on the design of the language model.

For businesses, it's valuable to know what tokens are when using large language models for generating text, providing customer service and automating processes. The maximum amount of data processed at once, or the maximum number of tokens that can be used for any single request, also determines how many tokens can be used for each processed interaction.

For IT service organisations, managing tokens allows them to control the efficiency, price, and quality of the responses produced by their AI-enabled systems. If tokenisation can improve the models' ability to provide relevant responses without waste of computing resources.

Effective token management will increase an organisation's ability to provide improved performance from their language models, along with maintaining consistent quality in the way they interact with AI solutions.