Why it matters

Everything a model reads and writes is tokenised. Tokenisation affects context limits, processing cost and, in subtle ways, which content fits into an answer. Content that is information-dense per token is more likely to survive retrieval and quotation.

How it relates to GEO

GEO’s density principle follows from token economics: engines can only include so many tokens in an answer, so retrieved content must deliver its key facts efficiently. Verbose pages waste the model’s budget; concise, dense answers get quoted.

Practical implications

Write tight: lead with the answer, keep supporting detail compact, and ensure the first paragraph carries the quotable fact. Avoid fluff that dilutes retrieval scoring. Monitor whether models paraphrase your content or quote it directly — quoting indicates density and extractability.

Examples

Two pages answer the same question. One delivers the definition in 40 words; the other wraps it in 300 words of introduction. The engine’s token budget favours the compact page, which is retrieved and quoted while the verbose page is summarised away.

Tokenization is fundamental to large language models and prompt engineering.

Frequently asked questions

Does tokenization change how I write? Only the density principle: same facts, fewer words, answer first.

Are tokens the same as words? No — tokens are sub-word units; roughly 0.7-1.5 words per token depending on language.