Get Cited by AI

Earning citations inside AI answers is a function of being retrievable, being recognized as authoritative, and writing content that fits the shape of an answer. There are no shortcuts, but the work is concrete.

1. Make the site retrievable

If an engine’s crawler can’t reach the page, nothing else matters.

• Allow the crawlers of the engines that matter. See list of AI crawlers.
• Verify there are no firewall, WAF, or rate-limit rules silently dropping crawler requests. See WAF configuration.
• Ensure content renders without JavaScript where possible. AI crawlers are less reliable than Googlebot at rendering JavaScript-heavy pages. See JavaScript and AI crawlers.
• Publish an llms.txt file pointing engines at the canonical content.

2. Cover the questions users ask

Citations track to questions, not topics. Pages that broadly cover a category lose to pages that answer one specific question well.

• Map the queries users ask AI assistants in the brand’s space. Internal logs, support tickets, and forum threads are the cleanest sources. Public keyword research helps less here than it does for SEO.
• Build a page (or section) for each distinct question. One page = one question = one clear answer in the first paragraph, with clear search intent targeting.
• Use the question as the page’s H1 or as a clearly visible H2 above the answer.

3. Write content in answer-shape

AI engines retrieve and cite passages, not pages. Structure rewards passages that stand alone.

• First paragraph states the claim or definition in one or two sentences.
• One claim per paragraph after that. No buried lede.
• Use lists, tables, and short paragraphs. Engines retrieve these more reliably than long prose.
• Avoid first-person plural and brand-voice flourishes in the passages most likely to be retrieved.

4. Build entity and authority signals

Engines cite sources they trust. Trust accrues from signals that map to E-E-A-T and from a strong entity graph.

• Author bios with credentials, on author pages with Person schema.
• A presence on Wikipedia or Wikidata where notability allows. See wikipedia presence.
• Press mentions on outlets the engine already cites for the topic.
• Consistent NAP and Organization schema across the site.

5. Use schema markup

Schema gives engines an unambiguous read on what’s on the page.

• Organization on the homepage.
• FAQPage for question-and-answer pages.
• Article on articles, with author, datePublished, and dateModified set correctly.
• Speakable markup on the passages most likely to be cited verbatim.

6. Measure and iterate

Citation work compounds. The signal is which queries cite the site today and which cite competitors.

• Track citations per engine for the queries that matter. See test AI citations.
• Track share of voice over time, not absolute citation counts.
• When a competitor is cited and the brand is not, read the cited passage. The gap is usually obvious — a clearer claim, a better-structured page, a stronger entity signal.

What does not work

• Buying citations or paying for placements in low-trust sources. Engines downweight these sources fast and the cost compounds.
• Stuffing answers with the brand name. AI engines strip this out and may downweight the source.
• Generating long, loosely-structured content with AI. Without sharp claims and clean structure, the page retrieves poorly even when the underlying information is correct.

Implementation example

AwesomeShoes Co. wants to earn citations for “best shoes for nurses on long shifts” where competitors currently dominate source panels. The growth manager assigns a focused citation sprint with clear technical and editorial ownership.

Implementation discussion: the SEO lead verifies crawler access and rendering, the content strategist publishes question-specific answer pages with evidence blocks, and the PR lead strengthens third-party authority signals for the same topic cluster. The analyst then compares pre/post citation share by engine to confirm which implementation steps moved outcomes.