AI citation readiness: write pages AI engines cite

1. Retrievability (~50%). Can the engine fetch the page? Is the page chunkable into clean passages? Is the answer in the first paragraph, or buried under three modal pop-ups?

2. Authority (~30%). Is the domain cited *by other sites* the engine already trusts? Does the page itself cite primary sources? Is the publisher named and consistent across the site (Organization schema, About page entity clarity)?

3. Specificity (~20%). Does the page contain a dated, specific, named-entity fact that exactly answers the query? Vague marketing copy does not get cited even from a high-authority domain.

• Atomic-answer lede — the first paragraph is the answer to the page's implicit question. AI engines extract this verbatim.
• Q&A-formatted H2s — match user-prompt syntax. "What is X?", "Why does Y happen?", "How do I Z?".
• Short paragraphs (1–3 sentences) — chunked retrieval prefers short units.
• Bulleted lists for "X ways to" / "types of" — Perplexity preferentially cites list content.
• Tables for comparisons — AI engines extract them cleanly.
• Dated, specific facts — "As of January 2026, X..." cites better than vague claims.
• Inline citations to primary sources — engines prefer content that *itself* cites.
• FAQPage schema + DefinedTerm schema — explicit signals of citable structure.

AI engines crawl your page, chunk it into ~300-token passages, embed each passage, and store the embedding. At query time the engine retrieves the top-k most similar passages and asks the LLM to synthesize an answer grounded in those passages. The first passage of your page is over-represented in retrieval because (a) it usually contains your title and main claim and (b) most retrieval algorithms weight position. If your first paragraph is "Welcome to our blog!" the engine never reaches the actual answer. If your first paragraph IS the answer, you win the citation.

• Cite official sources (RFCs, vendor docs, peer-reviewed research, government data) inline with hyperlinks.
• Date every claim ("As of May 2026...").
• Name the author with a Person schema linked to the Article.
• Maintain consistent Organization schema site-wide.
• Get external citations — when other sites link to you with descriptive anchor text, engines update their authority score for the domain.

• Keyword density. AI retrieval is embedding-based; stuffing keywords doesn't help.
• Generic LLM-generated content. Engines have classifiers that detect synthetic text and downweight it.
• Marketing puffery ("the world's best", "the leading...") — engines cite specific facts, not adjectives.
• AI-generated FAQs. If your FAQs are generic, they cite generically (i.e. not from your page).

Crawlmind's citation-tracking module runs your tracked queries daily against Perplexity, ChatGPT search, and Gemini and reports which of your URLs are cited. Pages that have all the citation-readiness signals but never get cited typically have an authority problem (low backlinks); pages cited often without the signals usually got lucky on an undifferentiated query. The combination is what compounds.