Enterprise AI Re-Skilling & Internals
Tokenization Mechanics: BPE
Byte pair encoding turns raw text into the token stream a model actually sees, and that translation shapes cost, context limits, and multilingual behavior. Understanding BPE explains why some languages cost more, why code behaves oddly, and why prompt length is not character count.
Points clés
- BPE starts from characters or bytes and merges the most frequent adjacent pairs.
- Vocabulary size is a tradeoff between sequence length and embedding table size.
- Token boundaries rarely match words, so counts differ from intuitions.
- Rare words and non-Latin scripts often split into many tokens.
- Code and JSON consume more tokens than prose of the same length.
- Whitespace and punctuation are tokens too; formatting affects cost.
- Different models use different tokenizers; never reuse counts across providers.
- Measure token counts with the provider's own tokenizer before shipping.
Questions fréquentes
Why does my prompt cost more than expected?
Because tokens are not words; punctuation, code, and non-English text inflate counts.
Can I change a model's tokenizer?
No. The tokenizer is fixed to the model's training; you work within it.
Does tokenization affect quality?
Indirectly. Poor splits make rare terms and languages harder to learn and more expensive.
How do I reduce token count?
Shorten prompts, compress repeated content, and avoid pretty-printed JSON when compact works.
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