Tokenization is the step that breaks text into small pieces, called tokens, before an AI model can work with it. It happens automatically the instant you send a prompt, turning your words into a list the model can read.
Imagine feeding a sentence through a paper shredder set to cut on word and syllable boundaries instead of random lines. ‘I love coding’ might come out as four neat strips, while a long word gets cut into a few pieces. Each strip then gets a number, and that list of numbers is what the model actually processes. Those pieces are the tokens, and the count of them is what the token limit measures.
The rules for where to cut are learned in advance from huge amounts of text, so common words stay whole and rare ones get split. This matters for cost and capacity: a tokenizer that splits a language into many small pieces uses more of the budget for the same sentence, which is why some languages run pricier than English on the same model.
It also explains some of an AI’s odd blind spots. Because the model sees tokens, not letters, a classic stumble is counting the r’s in ‘strawberry’: the word is a few tokens, and the individual letters were never really visible to it. Spelling tricks, rhymes and character-level edits trip models up for the same reason.
After splitting, each token becomes a number and later an embedding, the vector form the model actually does maths on. So tokenization is the bridge from human text to something a network can compute with.
At TopDevs we account for tokenization when we estimate the cost and limits of an AI feature, so a client gets a realistic figure rather than a surprise once real text flows through.
