How LLMs Cite Sources: A Study of 1,000 AI Answers
When ChatGPT, Claude, or Perplexity answers a question, what makes them cite one source over another? We analyzed 1,000 AI-generated answers to find patterns.
This is a working summary of patterns observed across multiple AI engines and query types. The findings have direct implications for content strategy.
Patterns we observed
- AI engines disproportionately cite content from domains with established entity authority
- Pages with explicit FAQ schema were cited more often than equivalent unstructured content
- Content updated within the last 6 months was cited more often than older content
- Wikipedia and Wikidata mentions correlated strongly with citation likelihood
- Concise answers (under 80 words) were cited more often than verbose ones
- Lists and bullet points appeared in citations more than equivalent paragraph form
What this means for content strategy
Optimizing for AI citation is mostly about formatting clarity, freshness, and entity association. Long-form authoritative content still wins, but the formatting matters as much as the substance.
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Bottom line
Pick your top 5 service questions. Rewrite the answers in inverted pyramid form (conclusion first, supporting detail after). Add FAQ schema. Update them every 60 days.
Further reading: the latest LLM research on arXiv.