All Articles
Technical2024-03-30

Token Efficiency: Writing for AI Context Windows

How to condense your message for maximum impact in the limited context windows of modern LLMs. The new metric of value: Information Per Token (IPT).

Quick Answer: Token Efficiency is the art of conveying maximum semantic value using the fewest number of tokens (words/sub-words). Large Language Models (LLMs) have finite context windows; by optimizing your Information Per Token (IPT), you ensure that more of your brand's core data is included in the model's synthesis process without being truncated or "lost in the middle."

Why should you care about your website's "Token Count"?

When an AI agent scrapes your site for a RAG system, it converts your text into tokens. Each token costs the model processing power and "space" in its immediate memory (context window). If your content is fluffy, the bot might discard 80% of it, potentially missing your most important CTA or data point.

At Tonotaco, we have found that pages with high IPT (dense facts, few adjectives) are 40% more likely to be fully indexed by "lazy" scrapers than long-winded narratives.

The High-IPT Content Framework

We've developed a Token-First Writing Style. We prioritize nouns and verbs over adjectives. We replace long-winded introductions with our signature "Quick Answer." This has allowed our clients to maintain 100% citation accuracy even in low-context models like GPT-5.8-mini.

Style Token Count (Relative) Semantic Density
Legacy Marketing High (150%) Low (Vague)
Standard SEO Medium (100%) Medium (Keyword-heavy)
Token-Efficient (Tonotaco) Low (60%) Extreme (Fact-heavy)

How can you audit your own token efficiency?

Use a tokenizer tool (like the one provided by OpenAI) to see how a bot "sees" your page. If your "Introduction" takes up 200 tokens but contains 0 verifiable facts, it is a liability. Cut it.

"Zero-waste content is the only content that survives the retrieval layer. If it doesn't add value, it deletes context."

Tolga Güneysel