LLM SEO: How Language Models Learn About Your Brand, and How to Influence It

1. Training data

Language models are trained on large collections of text gathered up to a cutoff date. Whatever was widely and consistently said about your brand before that date shapes what the model "knows" when it answers without searching. A company that launched or rebranded after the cutoff may be unknown or described with old facts.

2. Live retrieval

When an AI product searches the web, as ChatGPT search, Claude with search, Perplexity and Google's AI features do, it fetches current pages and builds the answer from them. Here your current website and the pages about you matter directly, and crawler access decides whether you are in the running at all.

Many commercial questions trigger retrieval in AI search products, but not every answer does, and not every product searches by default. A good LLM SEO program works on both paths.

Blocking training crawlers does not remove you from AI search, according to OpenAI and Google; see our AI crawlers guide for each company's rules.

• Find out which path produced the error. If the wrong fact appears with web search off but not on, it comes from training data; if it appears with search on, look at which sources were cited.
• Publish the correct facts plainly on your site. Pricing, products, locations, leadership and what you do, stated clearly on pages crawlers can reach.
• Correct the sources being cited. Update your profiles on major directories and review sites, and ask publishers to fix outdated articles.
• Keep facts consistent everywhere. Conflicting descriptions across sources are a common cause of confused answers.
• Recheck on a schedule. Retrieval-based answers can change within weeks; training-based ones only change with new model versions.

Run a fixed set of buyer questions with web search on, and record whether your brand is recommended, listed or absent and which sources are cited. Separately, ask a few brand questions with search off to see what the models carry from training. Our AI visibility guide has the full method, and our AI citations analysis shows what 2,311 ChatGPT citations looked like in practice.

This is the measurement layer of our AI SEO program, alongside the work that moves it.

What is LLM SEO?

Optimizing how large language models such as ChatGPT, Claude and Gemini find, understand and describe your brand. It overlaps almost entirely with GEO, AEO and AI SEO; the LLM framing puts more emphasis on what models already know about you from training.

Can I get my brand into an AI model's training data?

Not directly. Models are trained on large collections of public web content gathered up to a cutoff date. What you can do is make sure accurate, consistent information about your brand is widely available, and decide whether to allow training crawlers such as GPTBot and ClaudeBot.

Why does ChatGPT describe my company wrongly?

Usually because the model is relying on outdated or inconsistent information, or because it did not retrieve a current source. Publish clear, current facts on your own site, correct major third-party profiles, and make sure search crawlers can reach you so live answers can use up-to-date pages.

Does blocking training crawlers hurt my AI visibility?

Not for live AI search, as long as search crawlers are allowed. OpenAI and Google both say their training controls do not affect search. It may mean future models know less about you from training, which matters most for answers given without web search.

Is LLM SEO different from GEO?

Mostly in emphasis. GEO focuses on how generative engines assemble answers from retrieved sources; LLM SEO also considers what models carry from training. In practice, the same program covers both.

• OpenAI: Overview of OpenAI crawlers
• Google Search Central: Google common crawlers (Google-Extended)
• Anthropic: How Anthropic crawls the web

Every High Salience engagement starts with the Category Salience Brief: your commercial questions run across Google and the AI surfaces, competitors side by side, and a ranked list of what to fix first.