LLM SEO means shaping your content so a language model wants to repeat it: specific, well-structured and backed by mentions elsewhere on the web. It has almost nothing to do with keyword density and everything to do with being an easy, trustworthy answer for a model to lift. The main levers are authority signals, answer-shaped content and machine-readable structure.
Quick links
- What is LLM SEO?
- How do LLMs actually choose what to cite?
- What moves the needle: the real levers
- Is LLM SEO the same as AEO or GEO?
- Where to start this week
- FAQ
What is LLM SEO?
LLM SEO is the work of making a website legible and citable to large language models, so it gets named when someone asks ChatGPT, Perplexity, Gemini or Copilot a question your business could answer.
It is a newer name for a problem that has existed since AI chat interfaces started answering questions directly instead of handing back ten links. The searcher no longer opens your page, skims it and decides you’re credible themselves. A model does that filtering first, on their behalf, and either names you or doesn’t.
That changes what “optimisation” means. Traditional SEO rewards a page that ranks. LLM SEO rewards a page that survives being chopped into fragments, checked for a clean answer, and quoted alongside two or three competitors. A page can rank well on Google and still never get cited by a model, because the two systems are asking different questions: Google mostly asks “is this relevant and popular”, a model asks “can I safely repeat a fact from this in one sentence”.
Buyers researching the term are usually AEO-curious: they’ve heard the category exists and want to know what actually moves it. The short version is that it isn’t a checklist you finish once. It’s closer to how you’d run digital PR or technical SEO: ongoing, structural, and measured by whether you get mentioned, not just whether you rank.
How do LLMs actually choose what to cite?
Most AI search products run a retrieval process, not a lookup: they expand your question into several related searches, pull candidate pages for each, then decide sentence by sentence what is worth quoting.
This is why the overlap with classic search rankings is smaller than most people assume. Ahrefs analysed 15,000 long-tail queries in mid-2025 and found that only 12% of URLs cited by ChatGPT, Gemini and Copilot also appeared in Google’s top 10 for the same query. Perplexity was closer to Google’s rankings, at 28.6%, but still a long way from a direct copy. Google’s own AI Overviews behaved differently again, pulling most of their citations from pages already ranking on the first page, which makes sense given they sit inside the same index.
A few mechanics explain the gap:
- Query fan-out. A model rarely searches your exact question. It breaks it into sub-questions and merges results from all of them, so a page that answers one narrow angle well can get pulled in even if it never ranks for the original phrase.
- Chunking, not whole-page ranking. Retrieval systems split pages into passages and score each one. A page with one buried, well-written paragraph can outperform a longer page where the answer is diluted.
- Different indexes, different engines. ChatGPT’s search leans on Bing’s index rather than Google’s, and Perplexity runs its own crawler. A page invisible to one engine’s crawler can still be fine for another.
If you want to check where you currently stand before changing anything, our guide to auditing your own site for AI search walks through the same tests engine by engine.
What moves the needle: the real levers
Four things consistently affect whether a model cites you: how the content itself is written, whether machines can parse it as data, whether the crawler can reach it at all, and how often you’re mentioned elsewhere.
Answer-shaped content
Write the answer before you write the explanation. A model extracting a passage favours the paragraph that states a fact cleanly over the one that builds up to it. Use the question a buyer would actually type as a heading, put a direct one or two sentence answer immediately underneath, and keep the supporting detail specific: prices, timeframes, named services, real numbers. Vague, hedged writing is hard for a model to lift with confidence, and low confidence means it moves to the next source.
Structure a machine can parse
Schema markup labels what a page is about in a format models and search engines read directly rather than infer. Organization, FAQPage and Article are the ones that matter most for a typical business site, and they cost little to add on most modern platforms. It’s one part of the wider job of building a site that’s optimised for AI rather than bolted onto after the fact, alongside clean headings and server-rendered content the crawler doesn’t have to guess at.
Authority and mentions beyond your own site
This is the lever most sites under-invest in. A team from Princeton, Georgia Tech and the Allen Institute for AI built a 10,000-query benchmark specifically to test what changes citation odds, and found that adding statistics, direct quotations and cited sources to a page could lift its visibility in generative answers by up to 40%, far more than classic SEO signals like keyword placement moved the needle. The pattern holds off-site too: consistent, credible mentions of your business across other people’s content (comparison posts, roundups, press, directories) build the kind of consensus a model looks for before it decides your name is safe to repeat.
Crawler access, checked properly
None of the above matters if the crawler never sees the page. Confirm GPTBot, ClaudeBot, PerplexityBot and Google-Extended aren’t blocked in robots.txt, and check that your key content exists in the raw HTML rather than being injected by client-side JavaScript after the page loads. This is a five-minute check that catches more sites than it should.
Is LLM SEO the same as AEO or GEO?
They describe overlapping work under different names, and no one in the industry has fully agreed which term wins. Pick whichever your buyers search for and mean the same thing underneath.
AEO, Answer Engine Optimisation, is usually the broadest of the three: it covers being cited across any answer surface, including Google’s AI Overviews as well as standalone chat engines. GEO, Generative Engine Optimisation, is the term that came out of the academic research cited above and tends to get used specifically for optimising content for generative chat engines. LLM SEO is the newest and most search-friendly of the three, and it’s often used by people who found the category through a Google search rather than an industry conversation, which is a reasonable way to arrive at it. We’ve written a fuller explainer on what AEO actually covers if you want the longer version, and on what an AEO agency does day to day if you’re weighing up whether to bring in outside help.
The practical answer: don’t get precious about the label. The underlying work, being structured, specific, well-sourced and reachable by crawlers, is the same regardless of which of the three terms shows up in your search history.
Where to start this week
Start by finding out where you already stand, then fix the cheapest structural gaps before anything else.
- Ask the engines your own buyer questions. Type the phrases a customer would use into ChatGPT, Perplexity and Gemini, and note whether you’re named, whether the details are right, and who gets cited instead of you.
- Check crawler access on your most important pages. robots.txt, then a quick view-source check that your key content is in the raw HTML.
- Pick one high-intent page and rewrite the top of it. Real question as a heading, direct answer in the first two sentences, specifics instead of vague claims.
- Add the mentions you’re missing. A handful of genuinely relevant third-party pages that name your business consistently will do more than another page on your own site.
Traffic from AI sources is growing fast enough that this is worth doing now rather than filing under “eventually”: Adobe Analytics recorded AI-referred traffic to US retail sites up 138% year on year in May 2026, continuing a trend that started well before that. Whatever the number looks like in your sector, it isn’t shrinking.
Find out if AI already knows who you are
We'll run the buyer questions your prospects are actually asking through ChatGPT, Perplexity, Gemini and Google, then send back a plain-English report on where you stand and what's stopping you being cited. No obligation, and if you're already in good shape, we'll say so.
- Whether AI engines already name you, and what they get wrong
- The structural gaps most likely to be costing you citations
- A prioritised list of fixes, not a 40-page report nobody reads