LLM SEO: how to get your brand cited by AI
LLM SEO is the practice of optimising a website so large language models can find, understand and cite it. It covers three things: the technical access a model needs to read your pages, the content structure that makes a passage quotable, and the entity signals that let a model connect a citation back to your brand.
It is the umbrella term for work that also goes by two narrower names. Answer engine optimization is the half aimed at extracted answers — AI Overviews, featured snippets, voice results. Generative engine optimization is the half aimed at models that compose an answer and cite sources. Most of the work serves both.
Is LLM SEO the same as SEO?
No, though they share a foundation. Classic SEO optimises for a ranked list of links, where the reward is a click. LLM SEO optimises for inclusion in a generated answer, where the reward is being named. A page can rank first for a keyword and never appear in the AI answer to the same question — because ranking and extraction are judged on different things.
| Classic SEO | LLM SEO | |
|---|---|---|
| Reward | A click | A citation or a named mention |
| Judged on | Relevance and authority of the page | Whether a clean passage answers the question |
| Read by | A crawler building an index | A retrieval layer feeding a model |
| Brand matters | Indirectly, through links | Directly — the model has to know you exist |
| Feedback loop | Rankings and clicks in analytics | Nothing, unless you measure it deliberately |
That last difference is the practical one. When an AI answer omits you, no analytics tool reports it. There is no impression, no position and no click to be missing. You only find out by asking the engines directly, on purpose.
How do LLMs decide what to cite?
Three gates, in order. Failing any one of them removes you from the answer.
- Access. Can the model or its retrieval layer fetch the page at all? If GPTBot, ClaudeBot, PerplexityBot or Google-Extended are disallowed in robots.txt, nothing else matters.
- Extraction. Is there a clean, self-contained passage that answers the question? A model will not assemble an answer from four scattered sentences when another page has it in one.
- Attribution. Is your brand recognisable enough to name? Models routinely use facts without crediting a source they cannot identify as an entity. This is where consistent naming, Organization schema and third-party mentions do their work.
Most sites fail the second gate, not the first. In our scan of 174 B2B SaaS sites, only 1.7% blocked any AI crawler — but 90% had no FAQ schema and 98.9% had no Article schema, which is the markup that tells a model where an answer begins and ends.
How to do LLM SEO
- Open the doors. Confirm robots.txt allows the AI crawlers you want, and publish an llms.txt describing what your site is and which pages matter.
- Answer first. Put the direct answer in the opening sentence under each heading, and shape headings as the questions people actually ask.
- Mark up the answers. FAQPage schema for genuine questions, Article schema for author and date, Organization schema so a citation resolves to a brand.
- Write self-contained passages. Anything that opens with “this means…” cannot be lifted alone. Define terms on first use.
- Be mentioned elsewhere. Entity recognition is built from sources you do not control. Reviews, directories, comparisons and press all feed it.
- Measure the answer, not the markup. Ask the engines your buyers’ questions on a schedule and record who gets named.
What should an LLM SEO tool check?
An LLM SEO tool should cover two halves, and most cover only the first.
- The audit half — crawler access, structured data, answer-first content, llms.txt, heading structure. This tells you whether a model can cite you. Our AI visibility checker does this free, in seconds, with no signup.
- The measurement half — asking real buying questions of real engines on a schedule and recording which brands appear. This tells you whether a model does cite you, which is the only number that reflects the outcome.
An audit that always passes tells you nothing about whether you are being named. A tracker with no audit tells you that you are absent without telling you why. You want both, and you want them connected.
How DigiJaws does LLM SEO
The free scan audits the structural signals that decide extraction and hands you the files to fix them — llms.txt, JSON-LD, robots rules, answer-first rewrites. The Prompt Observatory then does the measurement half: your buyers’ questions, put to the engines every week, with the brands named for each one recorded so you see movement rather than guessing. That is AI brand monitoring, and it starts at $49 a month.
We run it on ourselves and publish what comes back. Asked three buying questions in our own category on 10 September 2026, the engines named Semrush in all three and DigiJaws in none. We would rather show you a real zero than an invented chart.
Frequently asked questions
What is LLM SEO?
LLM SEO is the practice of optimising a website so large language models such as ChatGPT, Claude, Gemini and Perplexity can find, understand and cite it. It covers the technical access models need to read your pages, the content structure that makes passages quotable, and the entity signals that let a model connect a citation to your brand.
Is LLM SEO the same as SEO?
No, though they share a foundation. Classic SEO optimises for a ranked list of links, where the reward is a click. LLM SEO optimises for inclusion in a generated answer, where the reward is being named or quoted. A page can rank first for a keyword and still never appear in the AI answer for the same question.
How do LLMs decide what to cite?
In practice, three things: whether the model or its retrieval layer can fetch the page, whether a clean self-contained passage answers the question, and whether the source is recognisable enough as an entity to be worth naming. Blocked crawlers remove you from consideration entirely, buried answers make extraction unreliable, and weak entity signals mean a model may use your facts without crediting your brand.
What is an LLM SEO tool?
An LLM SEO tool checks whether AI systems can read and quote your pages, and ideally whether they actually do. The audit half looks at crawler access, structured data, answer-first content and llms.txt. The measurement half asks real questions of real engines and records which brands were named. Most tools only do the first.
How is LLM SEO different from AEO and GEO?
LLM SEO is the umbrella term. Answer engine optimization is the part aimed at extracted answers such as AI Overviews and featured snippets. Generative engine optimization is the part aimed at models that compose an answer and cite sources. Most work serves both, which is why the terms are often used interchangeably.
Does blocking AI crawlers hurt LLM SEO?
Yes. If GPTBot, ClaudeBot, PerplexityBot or Google-Extended are disallowed in robots.txt, those systems cannot fetch your pages and cannot cite them. In a scan of 174 B2B SaaS sites only 1.7% blocked any AI crawler, so this is rarely the problem, but it is worth confirming because it is absolute when it happens.
How long does LLM SEO take to work?
Structural fixes such as schema, answer-first rewrites and crawler access can change what an engine extracts within days to weeks, because retrieval re-reads pages frequently. Entity recognition, which determines whether a model names your brand unprompted, moves far more slowly and depends on being mentioned across sources you do not control.