Byte-pair encoding (BPE) repeatedly merges frequent symbol sequences into reversible, lossless subword tokens. It produces shorter sequences, handles text absent from tokenizer training data, and helps generalize recurring language patterns such as the English “-ing” ending. section 2.8.3
BPE matters operationally because tokenization affects:
- Context: overly short input may lack useful context, while overly long input may reduce response coherence. section 2.8.3
- Cost and performance: smaller token sequences and efficient tokenizers support enterprise model decisions. section 2.8.3
- Interoperability: tokenizers are designed for corresponding language models and their pretraining or fine-tuning workflows. section 2.8.3
Counting tokens with tiktoken
Use tiktoken to select an encoding, encode a prompt, and count the resulting tokens. The example prompt “I have a white dog named Champ” produces seven tokens with cl100k_base.
