How to improve your odds of being recommended by ChatGPT

By Vladimir Boldyrev · LLMBrandScan · Published July 21, 2026

There's no algorithm to reverse-engineer here — OpenAI hasn't published mention criteria, and none exists to game. What follows are the levers with an actual mechanism behind them, in order of how much control you have over each.

1. 1

   Publish specific, checkable facts, not marketing language

   Exact pricing, exact feature lists, exact limitations — a model has nothing concrete to extract from "industry-leading" or "best-in-class," and vague copy is exactly what gets paraphrased into nothing.

2. 2

   Get covered by third parties the model already trusts

   Independent reviews, comparison articles, and industry roundups feed training data and retrieval alike. A brand that only talks about itself has a thinner footprint than one covered elsewhere.

3. 3

   Keep pricing and feature pages current

   Stale pricing pages are a common source of wrong answers — if your $49/mo plan became $79/mo eight months ago and the page still says $49, that's the number a model is more likely to surface.

4. 4

   Make comparisons to competitors explicit and fair

   A head-to-head comparison page that's honest about tradeoffs is more citable than one that's obviously self-serving — models (and humans) discount content that reads as pure marketing.

5. 5

   Structure content for direct extraction

   Clear headings, a direct answer near the top of a section, and factual claims stated plainly — the same discipline AEO focuses on — make it easier for a model to lift an accurate summary.

6. 6

   Fix name collisions and disambiguation problems

   If your brand name collides with another company, a common word, or a discontinued product, say so explicitly somewhere crawlable — an llms.txt "Notes for AI assistants" section is a good place for this.

7. 7

   Measure, don't guess

   Run your actual category questions against ChatGPT (and the other engines your buyers use) repeatedly, and track the trend. Without a baseline, you can't tell whether any of the above actually moved anything.

Three things that don't work

• Paying for "guaranteed ChatGPT placement" — no vendor controls what OpenAI's models generate.
• Keyword-stuffing a page the way you might for classic SEO — there's no ranking algorithm being targeted, so there's nothing to stuff for.
• Chasing a single favorable response and declaring victory — one answer is close to random; see the citation below.

Why models lean toward brands you already know

ChatGPT answers category questions using a mix of its training data and, when browsing or a connected search tool is active, live web retrieval — which means both what was written about a brand before the model's training cutoff and what's currently indexed can influence whether it's mentioned. There is no confirmed "ChatGPT SEO" ranking algorithm to reverse-engineer the way there is for Google's PageRank-descended systems; OpenAI has not published mention or citation criteria. What's confirmed instead, from published research on LLM brand recommendations (arXiv 2606.17443, a study of 3,750 responses across 50 brands), is that models carry systematic incumbent bias toward well-established brands and that a single response is a weak signal — which is why any real answer to "how do I rank in ChatGPT" starts with sampling many responses, not chasing one favorable one.

The practical upshot: incumbents have a real head start, but the levers above are exactly the ones GEO work is built on, and none of them requires being the incumbent — they require being accurate, well-covered, and easy to extract from. The only way to know if any of it's working is to test your own category questions against ChatGPT directly, repeatedly, and track the trend.