What is LLM SEO?
LLMO, LLM optimization
LLM SEO is search optimization aimed at large language models rather than ranked links. It focuses on what models retrieve and repeat: your own pages, and the third-party sources that mention you, so that assistants such as ChatGPT, Gemini and Claude describe and recommend your brand accurately.
LLM SEO optimizes for being named and cited in AI answers, not only for ranking links.
It covers your own pages and the third-party sources that models retrieve and learn from.
Much of classic SEO still applies, because many AI engines search the web before they answer.
How LLM SEO works
LLM SEO works on the two ways a large language model learns about your brand. The first is training data, which shapes the model's parametric knowledge. The second is retrieval, where the assistant searches the web at answer time and reads pages before it responds.
Each route needs different work:
Training data: consistent facts about your brand across your site, reviews, directories, media and forums, built up over time.
Retrieval: pages that AI crawlers can access, that answer specific questions clearly and that are easy to quote.
Third-party sources: coverage in the publications and communities that engines already cite for your category.
The goal is not a position on a results page. It is being named, described accurately and cited when someone asks an assistant a question your brand should answer.
LLM SEO vs traditional SEO
LLM SEO and traditional SEO share most of their foundations but measure success differently. Traditional SEO targets rankings, clicks and traffic from a list of links. LLM SEO targets mentions, citations and the way an answer describes you, often with no click at all.
Unit of success: a ranking position versus a mention or citation inside an answer.
Query shape: short keywords versus longer, conversational prompts with context.
Sources: your own pages versus a mix of your pages and the third-party sites that mention you.
Measurement: rank trackers and analytics versus repeated prompt runs across engines.
You do not have to choose. Engines such as ChatGPT search and Microsoft Copilot rely on web search results, so pages that rank well are more likely to be retrieved. Strong SEO is usually the starting point for LLM SEO, not a competitor to it.
LLM SEO, GEO and AEO
LLM SEO, generative engine optimization and answer engine optimization describe overlapping work, and people often use them interchangeably.
GEO is the term most used in research and industry for optimizing visibility in generative AI answers. AEO grew out of optimizing for featured snippets and voice assistants, and now also covers AI answers. LLM SEO, also called LLMO, puts the emphasis on the models themselves and how they retrieve and repeat information.
Honestly, the differences are mostly about emphasis and audience. A team doing LLM SEO and a team doing GEO will usually track the same prompts, fix the same crawl issues and pursue the same third-party coverage. Pick the term your stakeholders understand and focus on the work. AI search optimization is the umbrella term that covers all of them.
How to start with LLM SEO
Starting with LLM SEO means measuring where you stand in AI answers, then fixing the biggest gaps first.
Track real prompts: list the questions buyers ask assistants in your category and run them across engines.
Check access: make sure AI crawlers can reach and render your key pages, using robots.txt rules for AI and clean HTML.
Answer specific questions: publish clear pages on use cases, comparisons and pricing.
Earn third-party coverage: target the sites engines already cite for your topics.
Keep facts consistent: use the same names, prices and claims everywhere.
Measure again: rerun the same prompts and compare mentions and citations over time.
AI answers vary from run to run, so judge progress on trends across many prompts, not single answers.
How KIME turns LLM SEO data into tasks
Actions turns the gaps in your AI visibility into a prioritized task list. KIME creates tasks from what keeps your brand from being cited, sorted into Editorial, Affiliate, Content, Own Website and Validation work. Each task explains its reasoning, shows which prompts it could affect and links to competitor examples. The data behind it comes from tracking your prompts across ten AI engines, by country and language.
Frequently asked questions
Is LLM SEO different from GEO?
LLM SEO and GEO describe largely the same work: improving how your brand appears in answers generated by large language models. GEO is the more common term in research and industry, while LLM SEO stresses the models themselves. Both involve tracking prompts, making pages accessible and quotable, and earning mentions on third-party sources that AI engines cite.
Does traditional SEO still matter for LLMs?
Yes. Many AI engines search the web before they answer, and they tend to retrieve pages that search engines already rank and trust. Crawlability, clear page structure, internal links and backlinks still help. LLM SEO adds work on top, such as tracking AI answers, writing for extraction and earning mentions on sources that engines cite.
How long does LLM SEO take to show results?
It depends on which route you influence. Changes that affect retrieval, such as a new page that answers a question clearly, can show up once engines crawl and use it. Changes to a model's trained knowledge only appear when the model is retrained or updated. Track the same prompts over weeks and months to see real trends.