GEO checklist: how to get your page cited by ChatGPT, Perplexity, and AI Overviews
A practical, page-level checklist for generative engine optimization. What to put on the page, in the head, in the schema, and in the site infrastructure so a language model picks it as a source when it composes an answer.
4 min readStallwart
What generative engines are actually picking
A generative answer engine composes its response from a small set of sources it selects out of the corpus it can reach. The selection is not a leaderboard; it is closer to a research assistant grabbing the two or three pages that best answer this specific question with the least ambiguity. That means the pages that win are not always the pages that rank first in classical search. They are the pages that are specific, verifiable, and structurally easy to quote.
Everything on the checklist below flows from that. If a page can hand a machine a clean, self-contained answer with a source attached, it is more useful to the answer engine than a longer, better-ranked page that only implies the answer inside three paragraphs of prose.
On-page: what the reader (and the model) sees
The body of the page carries most of the weight. These are the moves that reliably help.
- Lead with the answer. First paragraph contains a single self-contained sentence that answers the question the page is about.
- One question per page. Do not bury three unrelated answers in one URL; each deserves its own home.
- FAQ block at the end, phrased the way buyers type, with each answer a standalone paragraph.
- Cite the specifics: numbers with sources, standards with the exact name, dates on claims that decay.
- Define the jargon on the page instead of assuming the reader arrived with it.
- Use clean headings that describe what each section actually answers.
- Publish plain-language variants of anything technical, so a summary can quote them cleanly.
In the head: what the crawler sees
Structured data is the seatbelt for GEO. It does not decide whether you are picked, but its absence often decides whether you are not.
- Article and FAQPage JSON-LD on every post. Answer engines lift FAQPage answers verbatim.
- BreadcrumbList so the hierarchy is machine-readable.
- Organization schema on the site root, with a stable name and canonical URL.
- Canonical URL on every page; never move a page that already earns citations.
- Open Graph tags for accurate previews when the page is quoted in chat surfaces.
Site infrastructure: what the model's crawler expects
A well-run site makes it easier for a language model to include you. Two files do most of the work.
- A live sitemap.xml that lists every canonical URL, generated from the same data as the site so it never drifts.
- An llms.txt at the root that inventories what the site is, what it offers, and the URLs of its canonical content.
- A robots.txt that permits the crawlers you want to be cited by; blocking them silently is the most common own-goal.
- Stable, semantic URLs. /blog/how-to-x beats /post?id=1287; do not change either once cited.
- Internal links from money pages to the source page, so authority concentrates on the pages you want quoted.
Off-page: coverage the model already trusts
The GEO shift makes brand mentions and citations elsewhere disproportionately valuable, because language models were pre-trained on the trusted corpora those mentions live in. Get named on the sites and podcasts your target reader treats as authoritative, and the model will pick your page over similar ones without those references.
This does not replace on-page work. It is what compounds it. A specific, verifiable, well-structured page with three trusted mentions elsewhere gets cited far more often than an equivalent page nobody has heard of.
The short version
- Lead every page with a single self-contained answer; buried answers do not get quoted.
- FAQPage and Article JSON-LD are the seatbelts of GEO; their absence often decides you out.
- sitemap.xml, llms.txt, and stable canonical URLs are the site-infrastructure moves that matter most.
- Brand mentions on trusted sites compound the on-page work; a specific page with a few trusted references gets cited over an equivalent page without them.
Questions this raises
- How do you get cited by AI answer engines?
- Lead with a self-contained answer, structure the page with FAQPage and Article schema, publish a sitemap and an llms.txt at the root, keep canonical URLs stable, and earn coverage from trusted sites in your space. Generative engines lean on specificity, verifiability, and structural cleanliness.
- Does llms.txt actually matter?
- It matters as a canonical inventory for language-model crawlers, the way sitemap.xml matters for search-engine crawlers. Absence rarely disqualifies a site, but presence makes it easier for models to reach the right URLs and reduces the noise around what your site is.
- What schema should every article carry for AI search?
- Article for the piece itself, FAQPage for any Q&A block, and BreadcrumbList for the hierarchy. Organization schema belongs on the site root. All should carry stable IDs and match what appears on the page.
- How important are brand mentions for GEO?
- Very. Language models leaned on trusted corpora during training and continue to weight them in retrieval, so a page from a site that gets mentioned by other trusted sources is disproportionately more likely to be cited. On-page structure gets you eligible; off-page mentions get you selected.
- Do I need to write differently for AI answer engines?
- Slightly. Lead every page with a self-contained answer, keep one question per URL, and cite specifics with sources. The rest of the writing rules do not change. What changes is that a machine is reading, and it is grading you on whether it could lift a clean sentence and stand behind the source.
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