Generative Engine Optimization (GEO)

How it works

Answer engines favour clear definitions, FAQ-structured Q&A, schema.org markup, and crawlable plain-text corpora (robots allowances, llms.txt). They cite sources they can parse confidently.

Why it matters

A growing share of research happens inside AI assistants that never show ten blue links. If you aren't citable, you're invisible in that channel — regardless of your Google rank.

How do AI answer engines decide which pages to cite?

The engines do not publish their ranking rules, so any answer here is partial. What is documented is the access side: each engine fetches pages with a named crawler (for example OAI-SearchBot for ChatGPT search, PerplexityBot for Perplexity, and Googlebot for Google's AI Overviews, which draw on the ordinary search index), and a page blocked for that crawler in robots.txt cannot be retrieved for that engine.

Beyond access, the pages that tend to get quoted are the ones where a single passage answers the question on its own: a definition in the first paragraph, a number with its unit and date, a clear statement of what something does and does not do. A sentence that depends on "as mentioned above" is hard to lift out of context, so it is less useful to an engine assembling an answer.

What can you check on your own site this week?

Fetch your robots.txt and confirm it does not disallow the crawlers of the engines you care about, including by a broad rule such as disallowing everything except Googlebot. Confirm your key pages return 200, carry a self-referencing canonical and are not marked noindex. Check that each important page opens with a direct answer rather than a paragraph of scene-setting.

Then pick ten questions your buyers actually ask and run them in ChatGPT, Perplexity and Google. Record which sources each one cites today. That list is your baseline, and repeating the same ten questions each month is a more honest measure of progress than any single score. The cold email benchmarks page and the deliverability learning hub are examples of pages written to answer one question per section.

Does llms.txt make a site more likely to be cited?

There is no public evidence that any major engine ranks or cites pages because a site has an llms.txt file. It is a proposed convention: a plain-text index of a site's important pages, meant to make grounding easier for tools that choose to read it. Publishing one is cheap and harmless, but it does not replace crawl access, indexing or clear writing.

Autocloz publishes an llms.txt and an llms-full.txt generated from the same content modules as its sitemap, so the file cannot list a page the site does not serve. That is an accuracy measure, not a ranking claim. The free tools and glossary pages are the kind of self-contained, question-led content the approach is designed around.

How is GEO measured when the engines give site owners little reporting?

Mostly by sampling. At the time of writing, Google Search Console does not appear to offer a separate report that isolates AI Overviews, so any clicks from them are hard to separate from ordinary search clicks there; check Google's current documentation before relying on this. ChatGPT and Perplexity offer site owners no equivalent console. What you can measure directly is referral traffic from those engines in your own analytics (for example visits from chatgpt.com or perplexity.ai), plus the repeated question sample described above. Both numbers are small for most sites. Report them as measured, including when they are close to zero, rather than inferring visibility from a tool's score.

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