Tokenisation
Tokenisation - How AI Reads Text
Neural networks work with numbers. They cannot read the word "hello" the way you do. Before any language model can process text, it must be broken into small numerical pieces called tokens. This seemingly simple step has profound consequences for how AI understands - and misunderstands - language.
Why Can't AI Just Read Characters?
The simplest approach: treat each character as a token. "Hello" becomes ['H', 'e', 'l', 'l', 'o'] - five tokens.
The problem? Words become absurdly long sequences. A 500-word essay might become 2,500+ character tokens. Since Transformer models scale quadratically with sequence length, this is computationally brutal. Worse, individual characters carry almost no meaning - the model must learn that 'c', 'a', 't' together mean a furry animal.
Word-Level Tokenisation
The opposite extreme: each word is one token. "The cat sat" becomes ['The', 'cat', 'sat'] - compact and meaningful.
But this creates a different problem: the vocabulary explosion. English alone has hundreds of thousands of words. Add misspellings, technical jargon, and code, and the vocabulary becomes unmanageable. Any word not in the vocabulary becomes an unknown [UNK] token - a dead end for understanding.
If a model using word-level tokens encounters "ChatGPT" for the first time and it is not in the vocabulary, it becomes [UNK]. How might this affect the model's ability to discuss new technology?
The Sweet Spot - Subword Tokenisation
Modern language models use subword tokenisation, which sits between characters and words. Common words stay whole ("the", "and"), while rare words are split into meaningful pieces ("un" + "believ" + "able").
This gives us a manageable vocabulary (typically 32,000โ100,000 tokens) while handling any text - even words the model has never seen before.
Byte Pair Encoding (BPE) - Step by Step
BPE is the algorithm behind GPT models. Here is how it builds a vocabulary:
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