100-Query Field Test Proves Perplexity Demands 30% Higher Semantic Density Than Google

Field data proves Google-calibrated content lacks the semantic weight required to trigger LLM citations, reveals Amaury Duval.

An analysis of 100 queries confirms generative engines demand heavier lexical optimization to extract and rank entities.

The audit maps exact density deficits across major models compared to Google's baseline.

Gemini requires a +6% increase in keyword occurrences, Claude needs +9%, and ChatGPT demands +16%.

Search-integrated models enforce stricter thresholds: Bing requires +20%, while Perplexity tops the chart at +30%.

Separately, LLMs filter this semantic saturation through a strict temporal flow.

Optimization concentrated in footers or FAQ sections kills visibility.

The models calculate meaning density in sequential tiers, heavily weighting the first 100 to 200 words.

This parser behavior forces early-page semantic saturation and punishes late-document keyword dumps.