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What are the most common LLMSEO mistakes?

TL;DRThe most common LLMSEO mistakes are burying answers in long introductions, skipping schema markup, writing vague content, and ignoring AI-specific metrics.

Direct answer

The most common LLMSEO mistakes include burying answers below lengthy introductions, omitting QAPage schema markup, writing vague or promotional content that LLMs cannot extract, bundling multiple questions onto one page, and failing to track AI-specific metrics. Many teams also make the mistake of treating LLMSEO as a one-time project instead of an ongoing optimization discipline.

Key facts

  • Burying the answer below a long introduction prevents LLMs from finding and extracting it during retrieval
  • Skipping QAPage schema removes explicit signals that help LLMs identify what each page answers
  • Vague or promotional language causes LLMs to skip your content in favor of more factual competitors
  • SEORav's AEO Checklist catches these common mistakes automatically and provides specific fix recommendations for each page

Treating LLMSEO as classic SEO with extra steps

The largest mistake is assuming that what worked for Google rankings will automatically earn AI citations. LLMSEO requires structural changes (extractable answer blocks, self-contained sentences, current dates) plus off-site signals that classical SEO often ignores. Teams that only add FAQ schema to existing content and expect AI citations typically see minimal lift. The full playbook covers on-page restructure, schema, freshness, original data, and active community presence.

Ignoring off-site presence

ChatGPT pulls about 40% of citations from Reddit, and Wikipedia appears in roughly 48% of certain query classes. Teams focused only on their own site miss the platforms AI engines actually quote most. Sustained, genuine participation in Reddit, niche forums, and category-specific communities drives more LLM mentions than additional on-site content. A single upvoted Reddit answer can drive citations for 18+ months because of how training data gets harvested.

Practical takeaway

Split your LLMSEO effort 60% on-site, 40% off-site. On-site: restructure top pages, add schema, refresh dates. Off-site: monthly Reddit participation in niche subreddits, pitch original data to one industry newsletter, and ensure Wikipedia citations are accurate where your brand or research is referenced. Audit weekly which sources AI engines quote in your category and reverse-engineer placement on those.