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Writing a paragraph so AI search cites it comes down to answer shape: “about the topic” and “answers this query” are different tests. Peec ran 12 open rerankers on the same two passages for the query “what are the best AEO tools” (full guide, Aug 2026): a product description scored near zero on strict models (MiniLM 0.0019%, BGE-large 0.20%), while a direct shortlist — “The best AEO tools in 2026 are…” — scored above 99% on every model. Every model scored the direct-answer version higher. What follows for content (Aug 2026 findings):
  • The cheapest high-impact edit is one direct-answer sentence in the first two lines of each query-targeting section — answer first, evidence and nuance after.
  • Match intent to answer shape: a shortlist with criteria for “best X” queries, a concise definition for “what is X”, like-for-like criteria for “X vs Y”, ordered steps for “how to”, an exact dated value for price/status questions.
  • Missing concepts matter more than word order. Cover the decision criteria and entities the intent requires (audience, price, year, alternatives) — repetition is not coverage.
  • Make sections locally self-contained: name the subject explicitly instead of “it/this tool”, keep the answer next to its evidence — whatever window a system cuts is judged alone.
A model score is not a citation guarantee — authority, freshness, diversity rules, and the generating LLM all still gate the final answer (see How AI search chooses passages). Why this mechanic favors listicles: Why listicles get cited. What not to over-do: The reranker-friendly chunks myth. Source: Rerankers for GEO/AEO: How AI search chooses passages and sources, Peec (Metehan Yesilyurt), Aug 2026.