Structure & Schema
Myth:Question-style H2s (AEO) boost LLM answer inclusion.
Evidence:They help, as multipliers: in a 2M-citation statistical analysis, structured headings, FAQ sections and TLDR/BLUF blocks showed consistent positive effects on citation - on top of content alignment and authority, not as substitutes.
Headings will not rescue a weak page: AI-perceived domain authority was ~6x more influential than the strongest page-level feature.
Discovered Labs · 2026 · Vendor Benchmark
Channels: chatgpt · claude · perplexity
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:Semantic meaning is all LLMs care about when pulling citations.
Evidence:Structural feature engineering — heading hierarchy, information chunking and visual formatting — independently raises citation rates by 17.3%.
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