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How LLMs turn text into numbers: BPE tokenization explained step by step, why your choice of tokenizer shapes model quality, and building a data pipeline.
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LLMs don't see words, they see tokens, chunks of text learned by an algorithm called Byte Pair Encoding that repeatedly glues the most frequent character pairs together. A tokenizer trained on Reddit will shred "myocardial" into meaningless fragments; one trained on medical text keeps it whole. That choice ripples through everything. This article walks through BPE merge-by-merge with a toy corpus, compares how GPT-4, LLaMA-2 and BERT tokenize clinical text, then covers the data pipeline, deduplication, quality filtering, and the token-count math you should do before spending a dollar on GPUs. Working Python code for all of it.