What is LLM SEO? Definition and how it differs from classic SEO

It is a practice, not a score. The thing you measure afterwards — how often you actually turn up in those answers — is AI visibility. This page is about the work; that page is about the readout.

What an LLM does with the web that a ranking algorithm does not

A search engine retrieves and orders. It hands back a list of documents and lets the reader pick. A language model retrieves, reads, and then writes a new document that may or may not name where the parts came from. That one difference is the whole discipline.

Two channels get your brand into that written answer, and they behave nothing alike.

Training data. Everything the model absorbed before it shipped. Ahrefs' LLMO guide points out that Wikipedia is "almost always the largest source of training data" in public datasets — which tells you how the channel works. You cannot optimise it in a quarter. It responds to being written about, across the web, over years.

Retrieval. The live half. Search Engine Land's guide splits the tools accordingly: some assistants answer from what they memorised, while retrieval-augmented ones — Gemini, Perplexity, AI Overviews, ChatGPT with search on — run queries against a live index and cite what comes back. This is the half that behaves like SEO, because for the retrieval-augmented tools the index in question is largely Google's or Bing's.

That split explains the two experiences people report. A model that names your competitor and not you, from memory, is a training-data problem, and it is slow. A model that cites three blog posts and none of them is yours, on a query you rank for, is a retrieval problem — and it is one you can work on this month.

LLMO, GEO, AEO, AI SEO — one idea, several names

They are the same practice. LLM optimization, generative engine optimization, answer engine optimization, AI search optimization: different vendors coined different labels for optimising toward AI-generated answers rather than a list of blue links. Do not let anyone sell you four services.

Ahrefs' Ryan Law goes further and argues the whole category is a rebrand: "The things that contribute to good visibility in search engines also contribute to good visibility in LLMs", and, more bluntly, "If you want to increase your presence in LLM output, hire an SEO." That is close to right, and the useful nuance is in the small residue of things that genuinely do behave differently.

What actually differs

Being quotable, not just rankable. A model lifts passages. A page organised as clear claim–evidence blocks, with a definition that stands on its own, gives it something liftable. A page that buries the answer under six hundred words of preamble gives it nothing. Search Engine Land cites research finding that ChatGPT cites content with a sequential heading structure nearly three times more often — structure is doing real work here.

Freshness carries more weight. Ahrefs analysed 16.975 million cited URLs across ChatGPT, Perplexity, Gemini, Copilot and AI Overviews and found the average age of a cited URL was 1,064 days against 1,432 days for organic results — about 26% fresher. Worth reading the caveat in the same study: at 2.9 years average age, "like traditional search, AI assistants still prefer citing long-lived content." Freshness is a tiebreak, not a licence to republish everything monthly.

Unlinked mentions count. For a link-based ranking system, a brand name in plain text is worth nothing. For a model learning entity associations, it is a data point. Reddit threads, comparison posts, podcast transcripts and roundups all feed the association even when nobody links.

Consistency across the web matters more. A model builds a picture of your brand from many sources. If your positioning, category and product names read differently on your site, your directory listings and your social profiles, you are handing it three conflicting descriptions and it will average them.

What does not change

Everything structural. Google is unusually direct about this: "There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary." What it does require is mundane — the page must be indexed, eligible for snippets in ordinary Search, and meet the technical requirements. If you block snippets with nosnippet or max-snippet, you also remove yourself from the AI features. There is no separate AI submission process, and anyone selling you one is selling nothing.

So the base layer holds: crawlable pages, genuinely helpful content, internal links, a coherent topic, real sources. The retrieval half of LLM SEO is largely SEO with a stricter demand for clarity.

What nobody can promise

There is no ranking to check. Answers vary between users, between sessions and between the same prompt asked twice, and no tool has access to the systems that produce them. Anyone quoting you a guaranteed position in ChatGPT is quoting a number they cannot see. The honest goal is to make your pages the ones most likely to be retrieved and easiest to quote, then measure the outcome by sampling — which is what the AI visibility page covers.

How Structura handles this

Structura works the content half, and only the content half. Each post it writes leads with a definition-shaped answer to the query, uses a sequential heading structure, cites external sources drawn from the authority domains that already rank for the topic — and verifies every one of those URLs before publishing, so the page is not quoting something dead. It ships Article and FAQ structured data, links the post into the rest of the site, and keeps publishing on a schedule, which is the only reliable way to keep an archive fresh.

That covers retrievability and quotability. It does not cover the other half of LLM SEO: being talked about. Unlinked brand mentions on Reddit, in roundups, in comparison posts and on Wikipedia come from PR, community presence and having a product people discuss. No publishing tool can manufacture those, and any that claims to is describing spam.

Structura also does not measure whether models mention you. It connects to Google Search Console and reports classic performance — impressions, clicks, positions, pages worth improving. For the AI answer side you need a separate tracker. The AI SEO page sets out exactly which parts of this Structura does.

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