Definition
LLM SEO emphasises the model itself: what it learned during training, what it retrieves at inference, and what cross-source consensus it sees about your brand. The discipline overlaps almost completely with GEO and AEO but frames the work around the model rather than the answer surface.
Why it matters
LLM SEO sits in the "Core concepts" layer of the AI search stack. Teams that handle it well get cited more, recommended more, and earn more of the AI-mediated revenue in their category. Teams that ignore it spend a year wondering why their content investment never moves the needle inside ChatGPT or Perplexity.
Related terms
- GEO - The practice of optimizing brand and content so generative AI engines (ChatGPT, Perplexity, Gemini, Claude, Grok) mention, recommend, and cite you.
- AEO - Optimizing content so answer engines (Perplexity, Google AI Overviews, ChatGPT Search) quote it as the direct answer to a query.
- Training corpus - The dataset an LLM was trained on. Brands that appear frequently and consistently in the training corpus are recalled by name in answers, with no live retrieval required.
- RAG - A technique where an LLM retrieves external documents at inference time and uses them to ground its answer. Powers Perplexity, ChatGPT Search, and AI Overviews.
- Mention rate - The percentage of prompts in a defined panel where an LLM names your brand. The headline metric of LLM SEO programs.
Apply it
The LLM SEO playbook ties every concept in this glossary into a single operating model. If you want to see how your brand performs across all the LLMs at once - mention rate, citation share, sentiment, rank - start with the free GEO audit or skip straight to a free Livesov account.