LLM SEO

LLM SEO is the practice of optimizing a brand’s content, technical setup and entity signals so that large language models (LLMs) such as ChatGPT, Claude, Gemini and Perplexity mention the brand accurately and cite its pages when they answer questions.

The name borrows from search engine optimization because the goal is similar: be found when buyers look for something. The mechanics are different. A search engine returns a ranked list of links. An LLM returns a written answer, and your brand is either in that answer, described correctly, or it is not.

What “being in the answer” depends on

An LLM can know about your brand in two ways:

• From training data. What the model absorbed from text it saw before its training cutoff. This changes only when the model is retrained and is hard to influence directly.
• From live retrieval. Many assistants search the web before answering, read a handful of pages and build the answer from them. The pages they read are the citation sources. This layer changes much faster, and it is where most LLM SEO work has an effect.

In both cases the model is generating text, not quoting a database. If the material about you is thin, inconsistent or attached to a company with the same name, the model fills the gap with a guess (see AI hallucination).

The core levers

LLM SEO work generally falls into four layers.

1. Crawlability. If an AI provider’s crawler cannot read your pages, its assistant cannot cite them. Check that robots.txt does not block the crawlers you want, and that important text is in the initial HTML rather than injected by JavaScript (see SSR vs. CSR).

2. Entity clarity. The model needs to know which company your name refers to. A clear brand entity, consistent facts across your site and profiles, Organization structured data with sameAs links, and explicit statements about which same-name companies you are not all reduce name confusion.

3. Answerable content. Pages that put the answer in the first sentence and keep facts in short, self-contained statements are easier to lift into an answer (see answer-first writing). Covering specific long-tail questions and decision queries matters because conversational prompts are long and specific.

4. Outside confirmation. Claims confirmed by independent sources, such as registries, trade media, directories and encyclopedia entries, are more likely to be adopted than claims that appear only on your own site (see third-party evidence).

How to measure LLM SEO

Rankings do not exist in an LLM answer, so LLM SEO uses different metrics:

• Mention rate: the share of valid answers to a fixed question set that correctly mention the brand.
• First-recommendation rate: the share of answers that put the brand first when recommending options.
• Name confusion rate: the share of answers using your name that actually describe another company.
• Description accuracy: whether what the model says about you is true.

All of these depend on a fixed question set: the exact prompts, platforms, sampling method and judgment rules, frozen before the first measurement. You measure once as a baseline, make changes, and retest under identical conditions (see baseline and retest). Without that discipline, a change in the numbers can just as easily come from a model update or a reworded prompt.

LLM SEO, GEO and AEO

The terminology is still settling, and the three terms overlap heavily:

• Generative engine optimization (GEO) is the term widely used in research and by practitioners, covering the full range of work to influence generative AI answers.
• Answer engine optimization (AEO) leans toward the content side: making pages easy to extract as direct answers.
• LLM SEO is the term many marketers use when they think of this as an extension of their SEO program.

In practice, most teams use them interchangeably. AI search optimization is the umbrella that covers all three. For a direct comparison with classic search work, see LLM SEO vs. SEO.

What LLM SEO cannot do

• It cannot guarantee a mention. AI answers vary between runs, users and dates, and providers do not publish how they select sources.
• It cannot override what a model learned in training. Retrieval-layer work can correct outdated or wrong facts in live answers, but only when the assistant actually searches.
• It does not replace SEO. Many assistants rely on search indexes to find pages, so pages that are not indexed are less likely to be retrieved at all.

Results are best described as “under these conditions, in this period, we observed this change,” not as cause and effect.

Getting started

1. Write a question set of real buyer questions across category, decision, long-tail and branded types, large enough that one question flipping does not swamp the result.
2. Run it on the assistants your buyers use and record a baseline.
3. Fix the technical and entity layers first; they are cheap and they remove barriers.
4. Then build answer pages and outside evidence, and retest.

If you would rather have this done for you, see how we work as an AI SEO agency, or start with a free GEO audit that measures your current baseline.