LLM SEO notes from running daily measurement against ChatGPT, Google AI Mode and Gemini. What moved the number and what did not.
First, the framing that made everything else make sense: you cannot optimise a language model the way you optimise for a search index. There is no console, no sitemap submission, no index status. What you can change is what exists about you on the open web, because that is the material these systems retrieve from.
LLM SEO is mostly changing the corpus, not the page.
What moved the number
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Appearing on sources the models already cite. Highest leverage by a wide margin and the one almost nobody treats as an SEO task. The trusted domain list in any category is short, usually under thirty, and stable enough to work through deliberately.
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Self-contained answer passages. Question stated plainly as a heading, complete answer in the next two or three sentences, no dependency on surrounding paragraphs. Retrieval operates on passages. Writing for extraction beat writing for flow every time we tested it.
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Consistent naming and description. Three different descriptions of your product across the web produce three weak associations instead of one strong one. Cheap to fix, and it fixes a class of problem where the model names you but describes you wrongly.
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Plain structured facts. Pricing, comparison points, specifications, definitions. Tables and direct statements got picked up far more reliably than the same information narrated.
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Community presence. Not gaming it, actually being useful where the problem gets discussed. Community threads are cited heavily across most categories we sample.
What did nothing
Keyword density in any form. This is search-era thinking applied to a system that does not work that way.
Publishing volume. We have measured sites with hundreds of pages ranking for nothing while one-page and two-page sites hold position 1 for the same category's head terms. Page count is not the lever.
llms.txt. Costs nothing, add it if you like, and we have not been able to detect the major engines reading it in any of our sampling.
The measurement caveat that matters most
Answers change on rephrasing alone. If your question set is not fixed, you are measuring model variance and calling it progress. Fix twenty to forty questions, run them on a schedule, record whether you were named and which domains were cited instead. The second list is the useful one.
Honest expectation setting: this is slow, because the main lever is third-party mentions and those move at the speed of editorial calendars, not publishing calendars.
We build Mentionry, which runs that loop and turns the cited-domain list into actual pitches. Posting the method because it works regardless of tooling.
Source: r/Mentionry · by /u/f3425