What is LLM SEO (LLMO)? Definition and how it works

LLM SEO, also called LLMO (large language model optimization), is the umbrella term for everything you do to shape how large language models know, describe and recommend your brand, both from what they learned in training and from what they find when they search the web.

It is the practitioners’ catch-all. Where AEO and GEO describe specific goals (being the answer, being cited), LLM SEO names the whole discipline, and it is the label most likely to include the model’s own memory of your brand.

The two layers of LLM SEO

A language model can talk about your brand from two places:

• Model-level knowledge. What the large language model absorbed from its training data. It stops at the model’s knowledge cutoff, carries no sources and only changes when a new version is trained.
• Retrieved knowledge. What the model finds when it searches the web at answer time. It can be current, it usually comes with source links, and it changes as soon as the pages it retrieves change.

Many answers mix both. An assistant may decide a question does not need a search and answer from memory, or it may search and still frame the results with what it already believes about your category.

Why LLM SEO matters

If a model learned an outdated description of your company (an old price, a discontinued product, a category you left), it can repeat it in answers that cite no source at all, so there is no page to fix and no link to check. Classic SEO has no equivalent: a search engine shows your current page, not its memory of it. LLM SEO makes you look at both layers, because the fix for each one is different.

How to apply LLM SEO

• For model-level knowledge: be described clearly and consistently in the kinds of sources that are widely published and referenced in your field (industry media, reference sites, respected reviews). Repetition of the same accurate facts across many independent sources is what a model can learn from.
• For retrieved knowledge: the usual GEO and SEO work. Crawlable pages, answer-first content, up-to-date facts and presence on the third-party pages engines cite.
• For both: decide which AI crawlers you allow. OpenAI, for example, documents separate crawlers for training and for search in its bots documentation; see GPTBot. You can test your rules with the AI crawler checker.

How to measure LLM SEO

Start with how often the model names you, then split it by whether the answer came with sources. That tells you which layer is doing the work:

Mention rate = answers that mention the brand ÷ answers analysed × 100

Suppose you run 100 prompts and your brand is named in 30 answers, a mention rate of 30%. If 20 of those 30 answers came with no web sources, two thirds of your visibility rests on what the model already knows, so third-party coverage and consistent facts are your main lever. If most came with sources, focus on the pages being cited.

Common LLM SEO mistakes

• Expecting instant changes. Updating your site does not update a trained model. It only changes what a search-enabled answer can find.
• Blocking every AI crawler by default. It may keep your content out of the answers you want to appear in. Decide crawler by crawler.
• Relying on one file or trick. llms.txt, schema or a single “AI page” will not outweigh what the rest of the web says about you.
• Testing in your own logged-in chat. Personal history and settings change answers. Use neutral, repeated checks.

How Mencoro helps with LLM SEO

Mencoro asks ChatGPT, Perplexity, Google AI Overview and Google AI Mode the prompts you choose, with no account, history or personal context, and records the brands named and the sources returned. ChatGPT is asked with web search on, but the model decides whether to search: when it answers from what it already knows, there are no sources, and your mentions still count. So you can see, answer by answer, whether a mention came with sources or from the model’s memory. Mencoro’s Coverage works like the mention rate above, calculated over the checks that returned an answer. The AI brand monitoring feature tracks it over time, and the methodology page explains every figure. For a quick one-off check, try the free ChatGPT rank checker.

Related glossary terms

• Generative engine optimization (GEO): getting cited and recommended in generated answers.
• Answer engine optimization (AEO): becoming the direct answer to a question.
• Knowledge cutoff: the date after which a model learned nothing new in training.
• Large language model (LLM): the kind of model behind ChatGPT and other AI assistants.