BPE-Style Tokenizers: The Small Algorithm That Decides What an LLM Can See

Tokenization is a critical yet often overlooked component of Large Language Models (LLMs). Rather than processing raw text, models use tokenizers to break down input into smaller, manageable units. Byte Pair Encoding (BPE), a data-compression algorithm originally developed by Philip Gage in 1994, has become the industry standard for this task. By iteratively merging the most frequent adjacent character pairs, BPE creates a vocabulary that balances sequence length and model capacity. This approach allows models to handle rare words and complex structures without requiring an infinite vocabulary. As the author explains, the efficiency of a tokenizer directly impacts a model's context window, inference costs, and overall performance. Because tokenization determines how text is represented, it is an essential economic and engineering unit that developers must understand to optimize LLM applications, especially when dealing with specialized data like source code.