Tokenization
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
The process of splitting text into the smallest units (tokens) that a model can process. A token can be a character, a word, or a subword. Models do not understand sentences; they only understand the sequence probability of tokens.
SCAFFOLDING EFFECT
Reduce cognitive load
The granularity of information. Your understanding of the world depends on how you "tokenize." Experts can see "chunks," while novices only see "letters." The key to improving cognition is to upgrade the tokenizer in your brain, packaging complex phenomena into larger meaningful units.
Anchor fast decisions
In NLP, text is split into a sequence of tokens as the minimal semantic/statistical unit for model input (characters, words, or subwords like BPE). The mechanism is "discretization + probabilistic modeling"—the model learns conditional probabilities over token sequences, and token granularity determines capability and cost.
MINIMUM ACTION
In progress 0/4Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Tokenizationverified
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