What is LLM SEO?
LLM SEO is optimising to be retrieved, cited, and recommended by large language models — the same objective as GEO, framed around the model rather than the engine.
LLM SEO is the practice of optimising your content and brand presence so that large language models retrieve, cite, and recommend you. This article explains what the term covers, how it differs from the neighbouring acronyms, and what makes it distinct in practice.
The term describes the same objective as generative engine optimization, framed around the model rather than the product built on top of it. That framing is useful for one reason: it makes clear that there are two separate places you can be present. One is the retrieval layer, where the model fetches live documents and cites them. The other is the model's own parameters, where associations learned during training determine which brands it names when nothing is retrieved at all.
The two paths into an answer
This distinction is the most practically important idea in LLM SEO, because the two paths respond to completely different work.
Retrieval-based answers happen when the assistant searches, reads pages, and synthesises. These are influenced by conventional findability plus extractable formatting, and they can change within days of a page being recrawled. Perplexity, ChatGPT with browsing, and AI Overviews mostly work this way.
Parametric answers happen when the model responds from what it already knows. Ask a model to name tools in a category with browsing disabled and you get a list drawn from training data. These are influenced by how widely and consistently your brand is discussed across the public web, and they change on the timescale of model releases — months, not days.
A brand that is well optimised for retrieval but absent from training data will be cited when the assistant searches and forgotten when it does not. A brand that is strong parametrically but has a weak site will be named without a link. You want both.
What influences the parametric side
- Volume and consistency of third-party mentions. Being described the same way across many independent sources builds a stable association.
- Presence in the sources models are known to train on heavily, including large reference sites, technical documentation, community forums, and news.
- Clear, unambiguous category language. A brand consistently described as "an AI visibility platform" is easier for a model to place than one described five different ways.
- Longevity. Associations accumulate, which is why incumbents are over-represented in parametric answers and why new entrants must lean harder on retrieval.
What influences the retrieval side
- Server-rendered, crawlable content that AI crawlers can fetch without executing JavaScript.
- Passages that answer a question completely within a few sentences, so an extracted chunk stands alone.
- Coverage across the fanned-out variants of a question rather than a single head term.
- Recency, since several engines visibly prefer recent sources for anything that could have changed.
How to work on LLM SEO
- Test both paths. Run your key questions with browsing enabled and again with it disabled, and compare which brands appear. The difference tells you which side you are weak on.
- Fix retrieval first, because it is faster to influence and it feeds the training data that shapes the parametric side later.
- Audit how the web describes you. Search for your brand plus your category and read what the top third-party sources actually say, because that language is what models absorb.
- Correct the record where it is wrong. Outdated descriptions on high-authority pages propagate into answers long after you have changed.
- Publish the reference material your category lacks. Definitional and comparison content is disproportionately reused, and being the origin of a widely repeated framing is the strongest parametric position available.
- Measure mention rate per engine on a fixed cadence, and treat month-over-month trend as the signal rather than any single answer.
A note on the term itself
LLM SEO, GEO, AEO, and AI SEO all get used for overlapping work, and no standard body has settled the definitions. If someone uses LLM SEO to mean something narrower — usually just the parametric side — the distinction above is the one worth clarifying, because it changes what you would actually do.
Common questions about LLM SEO
- Is LLM SEO different from GEO? They describe the same objective. LLM SEO frames it around the model, which is useful because it makes the retrieval-versus-training distinction explicit. GEO frames it around the product built on the model.
- Can I influence what a model was trained on? Not directly, and not retroactively. What you influence is what the public web says about you between now and the next training run, which is why third-party mentions matter more than your own pages for this path.
- How do I know which path I am failing? Run your category question with browsing enabled and again with it disabled. Absent in both means work on everything; absent only with browsing on means a retrieval problem; absent only with it off means a reputation problem.
- Do smaller models behave differently? Yes. Smaller and older models have weaker, noisier brand associations, so they lean harder on retrieval when it is available. Track engines separately rather than blending.
- Does the same work apply across models? The retrieval side transfers well, because fetchability and extractability are shared requirements. The training side is model-specific and shifts with each release.
- How often should I re-check the parametric side? Quarterly, and after any major model release. It does not move on a weekly timescale, so weekly measurement mostly generates noise.
Tip: the disabled-browsing test is the cheapest diagnostic in this whole field. Two minutes of asking a model to name the tools in your category, with no web access, tells you exactly what it believes about you before any of your recent work is taken into account.
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