LLM SEO

How does LLM SEO work?#

LLM SEO works on two routes into a model’s answer: its training data, which shapes what it says without searching, and live retrieval, which decides what it cites when it does search — each with different levers and very different timescales.

Most advice covers only the second column, because it’s the one you can test this week. But the first column decides what a model says about your category whenever it doesn’t search. OpenAI, Anthropic and Google each let you opt out of training separately from search, so blocking training crawlers is a trade-off, not a free privacy win.

LLM SEO vs GEO: what's the difference?#

LLM SEO and GEO describe nearly the same work, but LLM SEO puts the model at the center — including what it says from training data with no search at all — while GEO focuses on being retrieved and cited inside generated answers.

The labels multiply because the field is young: AEO, GEO, LLMO, AI SEO. The tactics overlap almost entirely. The distinction worth keeping is the training-data route: a GEO checklist rarely asks whether a model knows your brand when it doesn’t search, and an LLM SEO checklist should.

How do you influence what an LLM knows about your brand?#

Influencing what an LLM knows about your brand means being described consistently and often across the public web it learns from — your own pages, reviews, forums, videos and press — allowing the training crawlers you’re comfortable with, and then waiting for the next model.

• Brand mentions, especially on YouTube

  Observed

  Across 75,000 brands, Ahrefs found YouTube mentions correlated most with AI visibility (about 0.74) and branded web mentions next (0.66–0.71), while backlink counts correlated at about 0.19. See brand mentions.

• Training crawlers allowed

  Official

  OpenAI, Anthropic and Google document GPTBot, ClaudeBot and Google-Extended as training controls, separate from search. Disallow them and future content stays out of future models.

• Consistent entity facts

  Our read

  The same name, category, pricing model and founding facts on your site, profiles and directories plausibly help a model tie you to the right topics — no vendor documents how. See entity SEO.

Correcting a model’s mistakes follows the same logic. If ChatGPT states an old price or a feature you dropped, publish the correct fact clearly on your own site and in the places that discuss you. The next search-triggered answer can pick it up within weeks; the model’s memory changes only when a new model ships. See AI hallucination.

How do you measure LLM SEO?#

Measure LLM SEO with a fixed panel of buyer prompts run on a schedule across ChatGPT, Claude, Gemini and Perplexity, recording whether each answer mentions you, cites you and describes you accurately — then confirm the effect in AI referral traffic.

1. Build a prompt panel of 25–50 real buyer questions — category, comparison and problem prompts, not your brand name. See prompt tracking.
2. Record mentions and citations separately. A Semrush and Growth Memo study found 87% of ChatGPT’s brand appearances carried a link but only 21.4% of Gemini’s — count links alone and you miss most of Gemini.
3. Score accuracy. Is the price, category and positioning right? A mention with a wrong fact is a problem to fix, not a win.
4. Track AI referral traffic from chatgpt.com, claude.ai, perplexity.ai and gemini.google.com in analytics.

Common mistakes with LLM SEO#

The most common LLM SEO mistakes are optimizing only for live search while ignoring what models already say, blocking training crawlers by reflex, and trusting single screenshots or shortcut files like llms.txt.

Related terms#

• GEOAI search & GEOGenerative engine optimizationThe practice of making content easy for AI answer engines such as ChatGPT, Perplexity and Google AI Overviews to retrieve, quote and cite — where SEO competes for a ranked link, GEO competes to be a named source inside the generated answer.Read the entry
• How LLMs answerLLM training dataThe body of text — web crawls, books, code, licensed and forum content — that a language model learns from before release, and it shapes what the model says about a brand without searching, in a way that can’t be edited until the next model is trained.Read the entry
• LLMHow LLMs answerLarge language modelA neural network trained on vast amounts of text to predict the next word, which lets it write fluent answers — the technology behind ChatGPT, Claude and Gemini, whose knowledge is fixed at training time unless it retrieves fresh sources.Read the entry
• RAGHow LLMs answerRetrieval-augmented generationA technique in which an AI system first retrieves relevant documents from an index and then gives them to a large language model to write its answer, so the response can cite current sources instead of relying only on what the model memorized in training.Read the entry
• Authority & off-siteBrand mentionsAny reference to a brand’s name on another website, forum, review site or video, with or without a link, and in large studies mentions correlate more strongly with visibility in AI answers than backlinks do.Read the entry
• AI search & GEOAI visibilityA measure of how often, how prominently and how favorably a brand appears in answers from AI systems such as ChatGPT, Perplexity, Gemini and Google AI Overviews for the questions its buyers ask — the AI-search counterpart to search rankings.Read the entry