Structure & Schema
Myth:Restructuring your page gets it cited where it was not cited before.
Evidence:In a controlled replay of 103 human-verified cases where two retrieved sources supported the same fact, a structured rendering of the target raised its citations by +0.50 per answer (95% CI +0.20 to +0.84). The effect on whether the target was cited at all was +4.5 points and inconclusive (95% CI -1.4 to +10.4).
This September 2026 preprint uses one agent (GPT-5.4) and one retrieval provider in frozen replay, so it cannot show whether a live page edit changes retrieval. The study was powered to detect admission effects of roughly 8.5 points or larger.
Sriram Selvam & Anneswa Ghosh, arXiv · 2026 · Academic
Channels: chatgpt
Structure & Schema
Myth:Aggregated round-ups can rank in AI answers.
Evidence:In most consumer verticals they dominate: 80% of CPG/retail AI answers cite at least one neutral reviewer or aggregator (Foglift 2026 benchmark - 375 buyer-intent responses, 25 verticals, 5 engines). The exception is tech SaaS, where vendor first-party content is cited 92.7% of the time.
Structure & Schema
Myth:Traditional SEO keyword placement is sufficient for AI engines.
Evidence:Adding numeric statistics, authoritative quotes and fluent structural edits boosts visibility in generative engines by up to 40%.
Structure & Schema
Myth:AI models prefer short, simple pages.
Evidence:Pages with high quote density and numeric stats are cited 2-3x more often than thin content.
Structure & Schema
Myth:You need more listicles to rank in AI search.
Evidence:Listicles genuinely earn AI citations: Wix's AI Search Lab found them the #2 cited format for informational intent after articles (~45%), across ChatGPT, AI Mode and Perplexity. Format fit beats format volume.
Structure & Schema
Myth:It doesn't matter where in the page the answer lives — the model reads everything.
Evidence:44.2% of LLM citations originate in the first 30% of a document's body text. Lead with the answer.
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