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In practice
The divergence is measured, not anecdotal: cross-engine citation overlap sits in the low teens — the same question, asked of different engines, cites different sources — because each system pairs a different retrieval index with different trust heuristics and different training biases. The observed patterns for software categories: ChatGPT-class engines lean on the practitioner layer — Reddit threads, YouTube, forums — where products are argued from use; Perplexity-class engines lean on the structured layer — comparison sites, e-commerce and listing pages that answer “which one” in tabular form; Gemini-class engines lean on the authority layer — established publications, institutional references, documentation.
The strategy consequence: the placement map is per-engine — for each engine the buyer uses, enumerate what it currently cites for the category’s prompts (the measurement layer does this automatically), and place where that engine’s diet leads: community presence where the practitioner layer decides, placements on comparison roundups where the structured layer decides, coverage and authority signals where the institutional layer decides. The unifying layers underneath: every engine still requires crawl access, indexation and extractable content — the fundamentals are universal even when the diets diverge.
The maintenance: engines’ source preferences shift with their updates, so the per-engine map is refreshed on the monitoring cadence — last year’s Reddit-heavy mix is an observation, not a law.
See also: AI answer engines, Platform-specific citation sources, Mention-to-citation workflows.
Related service: Brand mentions.
