By Mehdi HassanWhat is LLM SEO?
LLM SEO is search optimization aimed at large language models instead of, or as well as, a traditional results page. The goal is that when someone asks an AI assistant a question in your category, the model knows your company, describes it correctly and, when it searches the web, cites your pages.
An LLM learns about you in two ways. Some knowledge comes from training data, which is fixed until the next model version. The rest comes from live retrieval, where the assistant searches the web and reads pages before answering. LLM SEO covers both, which is why it involves your site and what others write about you. It's listed with its sibling terms in our SEO and AEO glossary.
Is LLM SEO different from AEO and GEO?
LLM SEO is not meaningfully different from AEO and GEO. The three names come from different camps and stress different surfaces, but the work they recommend is the same, and it rests on ordinary SEO. Google's own guidance says optimizing for AI features is just SEO.
Term | Named after | What it stresses |
|---|---|---|
LLM SEO / LLMO | The model (ChatGPT, Claude, Gemini) | Being known and cited by assistants |
The answer engine | Writing direct, quotable answers | |
Generative engines | Visibility inside generated responses | |
SEO | The search engine | Crawlability, relevance, authority |
Our advice: pick one name internally and stop chasing acronyms. If you want the full playbook under one label, read the answer engine optimization guide.
What does LLM SEO involve in practice?
LLM SEO in practice comes down to four things: pages machines can read, answers machines can quote, an identity machines can recognize, and mentions that confirm it.
- Server-render your content. Many AI crawlers don't execute JavaScript. A marketing site built as a client-side app can look empty to them; see fixing a React SPA for SEO.
- Allow the right bots. Check robots.txt lets Googlebot, OAI-SearchBot and PerplexityBot crawl the pages you want cited.
- Answer first. Open each section with the direct answer, then the detail.
- Be a clear entity. Consistent name, description and profiles. See what is entity SEO.
- Earn mentions. Engines cite sources that others also reference.
- Sources cited per answer, ChatGPT vs Gemini (Semrush AI Visibility Index)
- ~15 vs ~3
- Share of AI-referred sessions from ChatGPT in TripleDart's B2B study
- 72%
- llms.txt files that got zero requests (Ahrefs)
- 97%
What does LLM SEO look like on a real B2B site?
On a real B2B site, LLM SEO mostly shows up on the homepage and pricing page, not in a special AI section. TripleDart's study of 28 B2B SaaS companies in Q1 2026 found homepages took 31.9% of LLM citations and 81.7% of AI-attributed leads.
Example: what an assistant can read on our own site
Our site is built in Next.js and server-rendered, so an assistant that fetches a page gets the full text in the HTML, not a blank shell waiting for JavaScript. The pricing section states fixed numbers in plain sentences: a $6,000 landing page, an $8,000 website for up to six pages, a $6,000 monthly retainer.
If someone asks an assistant what a studio like ours charges, there's a quotable fact to retrieve. We don't claim any specific assistant cites it for any specific prompt. The point is structural: a page with no stated facts gives the model nothing to say about you.
How do you check whether LLM SEO is working?
You check LLM SEO with a mix of referral data and manual prompt checks, because no tool shows you every AI answer. Set up a GA4 channel group for referrals from chatgpt.com, perplexity.ai, claude.ai, gemini and copilot. Once a month, run the 20 to 30 questions your buyers really ask and note whether you're named, and what is said. Add "how did you hear about us?" to your forms, because many AI-influenced buyers arrive later through branded search, which you'll see more clearly in entity SEO for startups.
