LLM SEO: how to get found, cited and recommended by AI

What is LLM SEO?

Traditional SEO aims for a high position in a list of links. LLM SEO aims for a place inside a written answer: being one of the brands named, being described accurately, and having your page cited as a source. It goes by several names, including generative engine optimization (GEO), answer engine optimization (AEO) and AI search optimization. They describe the same shift.

Why LLM SEO matters now

A growing share of research starts in AI assistants, and Google itself now answers many searches with an AI Overview above the links. That means more zero-click searches: the user gets an answer and a shortlist without visiting anyone's site. If you're not in that answer, you may never be considered, however well your page ranks further down.

How large language models find and use content

An LLM can know about your brand in two ways, and each needs a different kind of work.

1. Training data

Models learn from a large snapshot of the web, books and other text, collected before a cutoff date. Brands that were widely and consistently discussed are part of what the model "knows". You influence this over the long term, by being mentioned across many independent sources.

2. Live retrieval

For many questions, the assistant searches first and answers from what it finds, a pattern known as retrieval-augmented generation (RAG). ChatGPT with search, Perplexity, Gemini and Google's AI Overviews all do this. Here the usual SEO factors apply directly: your page must be crawlable, indexed, relevant and trusted to be retrieved, then clear enough to be quoted.

LLM SEO vs traditional SEO

LLM SEO best practices

Make your site accessible to AI crawlers

Allow the crawlers that power AI search (such as OAI-SearchBot, PerplexityBot and ChatGPT-User) in robots.txt and in your CDN or firewall settings, serve key content in the HTML, and keep pages fast. Then confirm they actually visit: server or CDN analytics show which AI bots fetch which pages.

Structure content so it can be extracted

Lead each section with a direct answer, use headings that mirror real questions, and put comparisons, specs and steps in tables and lists. Add FAQ sections for follow-up questions. Clear content structure helps both retrieval and quoting.

Add schema markup

Structured data (Organization, Product or SoftwareApplication, Article, FAQPage) states facts about your brand in a machine-readable way: what you are, what you cost, who publishes the page. It won't make a model mention you by itself, but it removes ambiguity.

Build topical authority

Cover your subject as content clusters: a main page per topic, supporting pages for subtopics, and comparison pages, linked together, using the semantic keywords and natural language your audience uses. Depth on a topic beats a scattering of unrelated posts.

Earn brand mentions, not just links

LLMs learn associations from text. Being named, even without a link, in reviews, comparisons, news, podcasts and communities strengthens the association between your brand and your category. Branded search volume tends to rise with it.

Keep facts fresh and consistent

Content freshness matters for retrieved answers. Keep prices, features and positioning current on your site and third-party profiles, and use one consistent description of your brand everywhere.

Cover Bing, not just Google

Several AI assistants use Bing's index for retrieval. Verify your site in Bing Webmaster Tools and submit your sitemap.

Consider an llms.txt file

llms.txt is a proposed standard: a Markdown summary of your site and its most useful pages for AI models. Support is still limited, so treat it as a quick extra rather than a strategy.

How to measure LLM SEO

You can't measure LLM SEO with rankings. Track a fixed set of prompts on each engine, on a schedule, and look at:

• Visibility: the share of answers that name your brand.
• Share of voice: your mentions compared with competitors'.
• Position and sentiment: how early you're named and how you're described.
• Citation tracking: which sources each answer cites, and whether your pages are among them.
• AI crawler activity: whether AI bots fetch your pages, and which ones.

Measure in the real apps where possible. As our checks above show, API models and consumer apps often name different brands, and your customers use the apps.

An LLM SEO checklist

1. 1Crawl access. AI search crawlers allowed in robots.txt and your CDN, key content in the HTML.
2. 2Prompt list. 20 to 50 real customer prompts across category, comparison and brand questions.
3. 3Baseline. visibility, share of voice and cited sources on each engine.
4. 4Source gaps. the sites cited when you're left out, and a plan to be included on them.
5. 5Content. answer-first, well-structured pages and comparison pages for your key topics.
6. 6Structured data. Organization, product and FAQ schema on the pages that matter.
7. 7Freshness. a review cycle for prices, features and dated content.
8. 8Tracking. the same prompts checked on a schedule, reviewed monthly.

Frequently asked questions

Is LLM SEO the same as GEO?

Largely, yes. LLM SEO, generative engine optimization (GEO) and answer engine optimization (AEO) all describe optimizing for AI-generated answers. The terms come from different communities but cover the same work.

Does traditional SEO still matter?

Yes. AI assistants that search the web retrieve pages much like search engines do, so crawlability, relevance and authority still decide whether your pages are found. LLM SEO adds new goals and measurements on top.

Can I optimize for ChatGPT's training data?

Only indirectly and slowly: by being discussed accurately and often across independent sources before future models are trained. Retrieval-based answers respond much faster to your work.

What tools do I need for LLM SEO?

A way to track answers on each AI engine over time, a way to see which sources they cite, AI crawler analytics, and your usual SEO toolkit for research and content.

Related

• How to rank in ChatGPT
• GEO tools
• AI visibility tool
• llms.txt generator