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The process of breaking text into smaller units called tokens, which language models use as their basic unit of processing. A token is typically a word fragment — 'governance' might be split into 'govern' and 'ance'. Token counts determine AI API pricing, context window limits, and processing speed. Understanding tokenization helps teams estimate costs, set rate limits, and ensure that long documents are handled correctly by AI systems that have maximum context lengths.
Why this matters for your team
Token limits affect cost and quality: long documents get silently truncated if they exceed the context window. Test your longest real-world inputs against the model's token limit before deploying — truncated inputs produce confidently wrong outputs.
A team's AI document analysis tool hits the model's 128,000-token context limit when processing long contracts, causing truncation errors. Understanding tokenization helps them design a chunking strategy.