A quick tokenizer explainer shows why LLMs often miscount
vista8 · x · 2026-07-24
Why models still miscount
This short explainer is about tokenizers—the text-splitting layer that turns human language into model tokens. The key takeaway is that many “bad counting” failures in large language models come from how text is tokenized, not from simple arithmetic alone.
It’s framed as a daily AI lesson, meant to help readers understand why model outputs can look wrong even when the underlying system is behaving as designed.
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