What tokenization is
In the context of AI and language, tokenization is the process of breaking text into smaller units called tokens. A token might be a word, part of a word, or another small piece of text. Language models do not work with raw text directly; they work with these tokens. Tokenization is therefore the step that turns human-readable text into the units a model can process.
Why text is broken into tokens
Models need their input in a consistent, manageable form. Raw text is a continuous stream of characters, which is not directly convenient for a model to handle. By splitting text into tokens, tokenization provides discrete units that the model can take in one by one. This breaking down of text into pieces is a necessary preparation step before a language model can work with it.
What a token can be
A token is not always a whole word. Depending on the approach, tokens may be whole words, parts of words, or other fragments of text. The choice of how to split text affects how many tokens a piece of text becomes and how the model sees it. In all cases, the goal is to represent the text as a sequence of units the model can process.
How tokenization affects models
Because models operate on tokens, tokenization shapes what the model actually receives. The way text is divided into tokens influences how the model processes and understands it. For example, the number of tokens in a piece of text can matter, since models often work with sequences of a limited length. Tokenization is thus an important behind-the-scenes factor in how language models function.
Tokenization in language models
Tokenization is a standard first step in how language models handle text. Before a model can read or generate text, that text is tokenized into units the model understands, and its output tokens are turned back into text. Understanding tokenization clarifies how the gap between human-readable text and the units a model processes is bridged.
Why it matters
Tokenization is a fundamental step in how AI language models work. Understanding what it is - breaking text into smaller units a model can process - clarifies how text is prepared for models. For anyone interested in how language AI works, it is a valuable concept to understand.
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See also: What is a large language model (LLM)?, What is a word embedding? Explained.
Sources
Published date reflects the original event date (2026-11-14). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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