How Large Language Models (LLMs) Tokenize Text: Why Words Aren't What You Think
Understanding how LLMs break language into pieces—and why it matters more than you realize
What You'll Learn
- Why language models break text into tokens instead of reading whole words
- How subword tokenization balances vocabulary size with the amount of text a model must process
- How Byte Pair Encoding (BPE) learns useful token boundaries from patterns in training data
- Why the same sentence can use very different numbers of tokens across languages, code, and rare words
- How tokenization can cause surprising failures in spelling, letter counting, and unusual inputs
- Why token counts affect context limits, processing efficiency, and the cost of using an LLM