The GEO content checklist: 7 things AI engines look for before they'll cite you

We've already written about how ChatGPT decides which brands to recommend: it isn't consulting a leaderboard, it's assembling an answer from whatever evidence it can find and trust. That whole analysis reduces to three levers — third-party evidence, content shaped like buyer questions, and consistent positioning. This post turns those levers into something you can execute: seven concrete checks AI engines effectively run before they'll cite you, each one actionable today. For every item you get three things: what to do, why the engine cares, and how to verify you actually did it.

1. Be named somewhere you don't control

What to do: Get your brand onto at least one independent source in your category — a review platform (G2, Capterra, Clutch, whatever fits your market), an industry directory, a forum thread where a real customer names you. Claim the profiles, complete them, and ask your happiest customers for honest reviews.

Why the engine cares: Models have learned to discount a company's own homepage — everyone's site says they're the best, so self-description is low-signal. Independent mentions are evidence that a real preference exists, not just a claim.

How to verify: Search Google for your brand name with -site:yourdomain.com. If the first page is only your own properties and social profiles, this box is unchecked.

2. Publish a real comparison page

What to do: Write "[You] vs [your main competitor]" — honestly, including the cases where the competitor is the better pick. If you have several competitors, start with the one AI answers name most often.

Why the engine cares: Buyers ask in comparison shape — "X vs Y," "alternatives to X" — and a model answers best from content already framed that way. If nothing on the web frames the comparison with your name in it, the model has nothing to reach for when that exact question arrives.

How to verify: Ask an engine with live browsing, "How does [you] compare to [competitor]?" If it retrieves your page and reflects your framing, it worked. If it improvises from scraps, you're not done.

3. Get into at least one "best of" list

What to do: Find the roundups the engines actually read — ask Perplexity "best [your category] tools" and open the sources it cites. Pitch the authors of those lists for inclusion, or publish your own honest category overview that includes competitors.

Why the engine cares: A listicle is the most cite-shaped content on the web: the shortlist is pre-assembled, so the model can lift it almost directly into a recommendation answer.

How to verify: Ask "best [category] for [your ideal customer]" and read the citations under the answer. You should recognize at least one source that names you.

4. Write one positioning sentence and repeat it everywhere

What to do: Draft a single plain sentence — category, who it's for, what's different — and use it verbatim: homepage, about page, directory listings, social bios, email footer.

Why the engine cares: Models build a consensus profile of you from everything written across the web. Ten sources describing you the same way becomes a stable fact the model repeats with confidence. Ten sources describing you differently gives it no clean sentence — and a model that isn't sure what to call you usually just leaves you out.

How to verify: Open your homepage, your LinkedIn page, and your top directory listing side by side. Could a stranger paste the same one-liner out of all three? If not, unify them this afternoon.

5. Shape your pages around real buyer questions

What to do: Turn key page headings into the literal questions buyers ask — "Who is [you] for?", "How much does [you] cost?", "How is [you] different from [competitor]?" — and answer each fully in the first sentence underneath.

Why the engine cares: A model retrieving your page at answer time lifts text that matches the shape of the question it was asked. An answer buried under three scrolls of vague value-prop language gives it nothing quotable.

How to verify: For each heading, point at the one sentence directly below it that fully answers it. If you have to say "well, it's implied across the section," rewrite it.

6. Make your site machine-legible

What to do: Add an llms.txt file that states your category and positioning in plain Markdown, add schema.org structured data, and keep pricing and "who it's for" in plain text, not just inside images or animations. Our free llms.txt generator drafts the file from your site in one click.

Why the engine cares: A model reading your site is extracting facts. Plain declarative sentences and structured data hand it exactly the facts you want repeated, in a form it parses well — instead of leaving it to guess from your hero animation.

How to verify: yourdomain.com/llms.txt returns a 200 in plain text, your schema validates, and our AI crawlability checker confirms the AI crawlers can actually reach your pages.

7. Keep the evidence fresh

What to do: Put a recurring monthly slot on the calendar: earn or publish one new mention, comparison, or list placement, and update llms.txt and your positioning sentence whenever pricing or messaging changes.

Why the engine cares: Models are shaped by frequency and recency of mention, and engines with live retrieval favor fresh pages. This month's roundup beats last year's silence — and stale evidence quietly stops working.

How to verify: Find the newest third-party mention of your brand. If you can't find one from the last quarter, this box is unchecked, whatever you did last year.

Score yourself before you start

The checklist tells you what to build. It can't tell you where you stand right now — whether the engines already name you, or confidently recommend three competitors and stop.

That's what our free scan is for. Enter your domain and we put 25 real buyer questions to ChatGPT, Claude, Gemini, and Perplexity, then show you, question by question, whether you're named, who's named instead, and the exact sentence each model returned. No signup, about a minute. Run it before you touch the checklist — so when you've worked through these seven items, you can rerun it and prove the work moved the number.