LLM SEO
LLM SEO is the practice of optimizing a brand's content and web presence so that AI assistants built on large language models, such as ChatGPT, Claude, Gemini, and Perplexity, describe the brand accurately and cite it in their answers. The same work is also called Generative Engine Optimization.
LLM SEO is search optimization aimed at the models themselves rather than at a search results page. Large language models now answer product and vendor questions directly inside ChatGPT, Claude, Gemini, and Perplexity, and buyers act on those answers without visiting the underlying sites. The work covers both what a model knows about a brand from its training data and what it retrieves from the live web at answer time, because either layer can get the brand wrong or leave it out.
The field has not settled on one name. Generative Engine Optimization, usually shortened to GEO, is the most common label in tooling and research. Answer Engine Optimization (AEO) stresses the answer format rather than the model. AI SEO and ChatGPT SEO show up in job posts and agency pitches. Treat them as synonyms: whatever label a team uses, the goal is to get the brand named accurately and cited as a source when an assistant answers a relevant buyer question.
In practice the work splits into two halves. The retrieval half looks like technical SEO with different targets: pages structured so one section answers one question cleanly, schema.org Organization and Product markup, crawler access for the bots that fetch at answer time, such as OAI-SearchBot, ChatGPT-User and PerplexityBot, and presence on the sources engines actually cite, such as Reddit threads, G2 and Capterra listings, and comparison posts on third-party blogs. The memory half is slower: earning consistent descriptions of the brand across enough authoritative pages that the next model training run absorbs them.
Measurement is the practical starting point, because none of this shows up in Search Console or GA4. The baseline check is asking the engines the questions buyers ask, then recording how the brand is described and which sources the answers cite. Our July 2026 study found that 27 of 30 brands were materially misdescribed by an AI assistant answering from memory, and 13 of 30 were still misdescribed when the assistant used live web search. Tools like Discoverable automate that baseline: the free AI visibility checker runs a snapshot with no login required.