How to get your site cited by ChatGPT and Perplexity
Being cited inside an AI answer is not luck and it is not the same game as ranking on Google. It comes down to a specific set of things a retrieval system and a language model can each work with. Here is what those things are, and how to check whether your own site has them.
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When you ask ChatGPT or Perplexity a question, two separate systems run in sequence. First, a retrieval step searches an index (its own, or a live web search) and pulls in a shortlist of candidate pages that seem relevant to the query. Second, the language model reads that shortlist and decides which facts to use in its answer, and which page or two to name as the source. You only get cited if you clear both steps: you have to make the shortlist, and then you have to be the page the model trusts enough to quote.
Most sites that never get cited fail at the first step, not the second. They are simply invisible to the retrieval system, either because a crawler cannot reach them or because their content does not match how the query is actually phrased. Fix the retrieval problem first.
Make sure you can be retrieved at all
Each AI engine crawls the web with its own bot: GPTBot for ChatGPT, PerplexityBot for Perplexity, ClaudeBot for Claude, Google-Extended for Google’s AI features. Every one of them respects robots.txt independently of the rules you set for Googlebot. A site that blocks AI crawlers, deliberately or by an old boilerplate robots.txt rule nobody has revisited, cannot be cited by that engine no matter how good the content is.
An llms.txt file helps at the same layer. It is a plain-text file at the root of your domain that lists your most authoritative pages in one place, similar in spirit to a sitemap but written for a model rather than a crawler queue. Not every engine reads it yet, but the ones that do use it as a direct hint about which pages matter most, which is valuable when your site has hundreds of pages and only a handful are genuinely citable.
Write so the model can quote you
Clear entities, not pronouns
A model attributing a claim to your page needs to know, in that sentence, what the claim is about. “It reduces load time by simplifying the request chain” is harder to lift into an answer than “Climb AI’s Site Audit module reduces load time by simplifying the request chain.” Name the product, company, person or place plainly, especially in the first sentence of a section, rather than relying on a pronoun that only makes sense next to three prior paragraphs.
Specific, quotable claims
A sentence with a concrete number, date, mechanism or named comparison is easier to extract than a paragraph of general description. This does not mean inventing statistics, it means stating the specific facts you do have plainly instead of burying them in qualifying language.
FAQ-style structure
Question-and-answer formatting maps almost directly onto how a person phrases a prompt to an AI engine. A page with a clearly headed question followed by a direct two-or-three sentence answer is close to plug-and-play for a model composing its own response.
Signals models weight beyond the text itself
- Structured data. FAQPage, Article and Organization schema give a model a machine-readable shortcut to facts it would otherwise infer from prose, and reduce the chance it misreads a claim.
- Author credentials. A named author with a bio and relevant expertise is a trust signal for AI retrieval the same way it has long been for Google’s own quality guidelines.
- Freshness. When two pages say roughly the same thing, a visible, genuine last-updated date can be the difference in which one an engine chooses to trust.
- Consistency across the web. If your own site, your Wikipedia-style listings and your social profiles all describe the same entity the same way, that consistency reinforces the model’s confidence in the claim.
- A crawlable, fast, working page. None of the above matters if the page itself times out, redirects incorrectly, or is behind a login wall.
A six-point citation-readiness check
| Check | Why it matters |
|---|---|
| Is GPTBot, PerplexityBot and ClaudeBot allowed in robots.txt? | If a crawler cannot fetch the page, nothing else on this list matters. |
| Does the page state its main claim in the first two sentences? | Retrieval systems weight the opening of a page heavily when matching it to a query. |
| Is there an llms.txt file pointing at your best pages? | Gives engines a direct index instead of making them infer importance from crawl patterns. |
| Does the page carry FAQPage, Article or Organization schema? | Structured data is a machine-readable shortcut to facts a model would otherwise have to parse from prose. |
| Is there a named author with a bio and credentials? | A credited author is a trust signal for citation, the same way it is for Google’s own quality guidelines. |
| When was the page last updated, and does it say so visibly? | Freshness is a factor in which of several similar pages an engine chooses to trust. |
Citations are a metric you have to check for, not one Search Console reports
Search Console will tell you nothing about whether ChatGPT or Perplexity cited you last week, because that answer never generated a click for it to log. The only reliable way to know is to actually ask each engine a handful of questions your customers would ask, and check whether your site is named. Doing this by hand across six engines, on a regular schedule, does not scale past a page or two. That is the specific gap the GEO Agent is built to close: it runs this citation check across ChatGPT, Perplexity, Claude, Gemini, Grok and DeepSeek on a schedule, scores how citable your pages are today, and generates the fix files, llms.txt, schema, FAQ sections, author bios, for the gaps it finds. For the broader context on how this discipline relates to classic ranking work, see SEO vs GEO, and the full plan details are on the pricing page.
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