LLM SEO meaning: what it is, in plain words, and what you can change

What LLM SEO means

LLM SEO means making your brand easy for AI assistants to find, understand and name. The goal is to be mentioned or cited when someone asks an assistant a question you could answer.

SEO, search engine optimisation, is the old work of ranking in Google’s list of links. LLM SEO borrows the name for a newer place people look for answers. Instead of ten blue links, the buyer gets a written answer that names two or three products and links a few pages. If you are not in that answer, you are not in the running, and nothing in your analytics tells you so.

The same work goes by other names: AI SEO, answer engine optimisation (AEO) and generative engine optimisation (GEO). The names come from different people at different times, and our page on AEO, GEO and AI SEO compares them. Our plain guide to AI search optimization covers the whole job. This guide is about the meaning behind the LLM version of the name, because that word points at how the systems actually work.

What the LLM part means

LLM stands for large language model. It is the kind of AI system that writes the answers in ChatGPT, Gemini, Claude, Perplexity and DeepSeek.

Google’s machine learning glossary defines a large language model as “at a minimum, a language model having a very high number of parameters”, and more informally as “any Transformer-based language model, such as Gemini or GPT”. In plain words, it is a program trained on a very large amount of text to predict what words come next. That is how it writes a fluent answer.

Two facts about these models explain the whole field. First, a model only knows what it read, either during training or at the moment you ask. Second, it does not look things up in a ranked list the way a search engine does. It writes an answer, and the pages it cites are the ones it used, or chose to show, while writing.

The two ways a model learns about you

A model learns about your brand from its training data, and from pages it searches for when a question is asked. You can influence the second much more directly than the first.

The second way has a name: retrieval-augmented generation, or RAG. Google’s glossary describes it as grounding a model’s output “with sources of knowledge retrieved after the model was trained”. One of the reasons it lists for using RAG is “enabling the model to cite sources”. When an assistant shows you links under its answer, this is usually what happened.

The providers keep these switches apart on purpose. OpenAI’s crawler page says a site can allow OAI-SearchBot “to appear in search results while disallowing GPTBot” to keep its content out of training. Anthropic’s crawler page says that blocking Claude-SearchBot “may reduce your site’s visibility and accuracy in user search results”. Perplexity’s crawler page says PerplexityBot “is not used to crawl content for AI foundation models”.

So the first meaning of LLM SEO is practical and small. Make sure the search robot of each assistant can read your site. A site that blocks them can still be named from what the model learned in training. But OpenAI, for one, says a site that blocks OAI-SearchBot will not be shown in ChatGPT search answers, and nothing new on the site reaches the model until it is read again.

You cannot edit what a model learned in training. You can change the page it reads when the question is asked.

How LLM SEO differs from SEO

The groundwork is the same, and Google says so. What changes is the prize, the way you measure it, and a few habits that stop working.

Google’s guide to optimizing for generative AI features says that “optimizing for generative AI search is optimizing for the search experience, and thus still SEO”. That is true for Google’s own AI answers, which are built on its search index. Other assistants run their own search, but the groundwork of a readable, useful page carries over.

The GEO paper is the clearest test so far. Its authors rewrote pages in nine ways and measured how much each answer drew from them. Adding citations, quotations and statistics gave a relative gain of 30 to 40% on their main measure, and keyword stuffing gave “little to no improvement”. The full paper is on arXiv.

Clicks are the other big change. A Pew Research Center study of 900 US adults’ browsing in March 2025 found that people clicked a traditional result in 8% of Google visits that showed an AI summary, against 15% of visits without one. They clicked a link inside the summary in 1% of visits that had one. That is one study, of Google only, but it shows why traffic alone is the wrong way to judge this work.

What LLM SEO work looks like

It is a short list, and most of it is ordinary good publishing. The order matters, because the later steps are wasted if the first one fails.

1. Let the search robots in. Check robots.txt and your firewall for OAI-SearchBot, PerplexityBot, Claude-SearchBot and Googlebot. Our crawler access check explains what to look for.
2. Decide about the training robots separately. GPTBot, ClaudeBot and Google-Extended are a choice about training, not about being cited today.
3. Write down the questions buyers ask before they choose, in their words, not your product’s.
4. Ask those questions in the assistants and save every answer, with its links and the date.
5. For each question you lose, open the pages that were cited and see what they say that yours does not.
6. Rewrite or add a page that answers the question plainly near the top, with sources and numbers behind its claims.
7. Earn mentions on the other sites the assistants cite, such as reviews, comparisons and lists, by giving their authors real facts.
8. Ask the same questions again later and compare, remembering that answers vary from run to run.

A worked example

The brand and the details in this example are made up to show the two routes. They do not describe any real company or any real answer.

Say a bakery supplier changed its delivery area last spring, and now serves three more cities. A buyer asks an assistant which suppliers deliver to one of the new cities. If the assistant answers from training alone, it may leave the supplier out, because the model was trained on pages written before the change. Nothing the supplier writes today fixes that answer until a new model is trained.

If the assistant searches, the story changes. Its search robot can find the supplier’s delivery page, read the new list of cities, and cite it. That only works if the robot is allowed in, the page names each city in plain text rather than in a map image, and the page ranks for the question in that assistant’s search. Those three things are what LLM SEO work actually changes.

Agentcites is built around the middle of this list. Today it asks ChatGPT, Perplexity, Gemini and DeepSeek up to 50 buyer questions about each website every week, keeps every answer word for word, and writes a draft page aimed at a question you lost. The method page says how answers are counted, and the plan costs $149 a month per website.

Words you will meet

LLM SEO has its own small vocabulary. Each term below is defined in plain words.

The last term needs a caveat. The llms.txt proposal is a suggested standard, not a rule any assistant must follow. Google’s guide says Google Search ignores llms.txt, so it “will neither harm nor help” there. It may help other tools, especially coding agents reading documentation.

What LLM SEO cannot promise

Nobody can promise a place in an AI answer. The providers do not publish how their models choose, and the same question can get a different answer tomorrow.

Google’s guide warns that no third-party tool “has access to our internal ranking or AI systems”. Treat any vendor that claims otherwise with care, including us. Answers also vary from one run to the next, so a single good answer is not proof that anything changed, and a single bad one is not proof that it failed. What can be promised is narrower: that your site is readable, that your page answers the question, and that you can see what the assistants said, week after week, and judge whether it moved.

Google’s common crawlers page is a good place to start reading for yourself. It is short, it is written by the people who run the system, and it settles most arguments about what a robots.txt line does.