LLM SEO
LLM SEO is the practice of optimizing content and brand signals so large language models can better understand, retrieve, and cite your site in AI-generated answers.
Start here
- Structure your content with clear headings, lists, and short answer blocks to help models extract information.
- Add relevant schema markup (FAQPage, Article, etc.) for extra context.
- Ensure technical accessibility: crawlable, renderable, and not blocked by robots.txt.
- Build topical authority through internal linking and consistent entity naming.
- Monitor your AI citations over time – they can change without warning.
Here's the quick-start I'd give a friend who's new to this.
What I'd do first
- Structure your pages for extraction. Use clear question-based headings (H2, H3), bullet lists, and short answer paragraphs right after the heading. Models love finding a direct answer near a relevant heading.
- Add relevant schema markup. Article, FAQPage, HowTo, or Product schema when appropriate. It gives models extra context they can't always infer from plain text.
- Be consistent with entity naming. If you're the only site that calls something by a specific name, link to an authoritative source or use internal links to define it. Confused names confuse models.
- Build topic clusters with solid internal linking. Models draw on contextual relationships. A cluster of interlinked pages around a core topic signals authority.
- Check your crawl budget and JS rendering. AI retrieval systems often use a simplified crawler. If your content relies on heavy JavaScript or is blocked by robots.txt, you might be invisible. Test with a tool like Search Console's URL inspection.
Plain-English take
LLM SEO is about making your content easy for AI models to read, trust, and quote. Think of it as writing a really good Wikipedia-style entry for your brand – clear, factual, and well-structured. You're not just optimizing for Google's algorithm anymore; you're optimizing for any AI that might answer a user's question by pulling from the web. That includes ChatGPT, Gemini, Perplexity, and even Google's own AI Overviews. The good news? Most of the fundamentals – clear structure, schema, entity clarity – are already good SEO. You just need to be more intentional about how an AI would parse your page.
When it actually matters
LLM SEO matters most when:
- Your content answers specific, factual questions (e.g., "how to change a tire" or "polar bear migration patterns") – models often cite these verbatim.
- You operate in a space where AI-generated answers are already common – think health, finance, tech tutorials, or product comparisons.
- You want brand visibility even when users skip traditional search results. Your site might be cited in an AI summary rather than clicked through, but that citation still builds authority and trust.
- You're in a competitive niche where traditional SEO is saturated. A well-structured page that an AI loves can leapfrog older, less structured content.
It matters less if your content is purely opinion, entertainment, or heavily paywalled. Models tend to cite open, factual content first.
What I got wrong
- Thinking LLM SEO is just keyword optimization. I used to stuff question phrases into headings and hope. But models care more about context and structure than exact-match phrases. A single, well-written paragraph under a clear heading outperforms three repetitive keyword-stuffed ones.
- Ignoring crawlability for AI systems. I assumed if Google could crawl my JS-heavy page, so could AI models. Then I noticed my content was missing from Perplexity. I fixed it by validating with Search Console and ensuring key content was in the initial HTML.
- Publishing generic content without unique data. Early on I wrote a lot of overview pieces that synthesised other sources. Models quickly learn to cite the original source, not the summary. Now I make sure every page has at least one original data point, expert quote, or unique angle.
- Assuming citations are permanent. I celebrated a ChatGPT citation – only to see it vanish four months later when the model updated its retrieval. LLM SEO is an ongoing process, not a one-and-done fix. Monitor your citations and adapt.
Next step
Sources
Primary documentation is linked directly. Anything commercial is marked nofollow.
- Google Search Central — Most authoritative source for crawlability, structured data, indexing, and SEO fundamentals that still underpin LLM visibility.
- Google AI features documentation — Useful for understanding evolving search features such as AI Overviews and how Google describes content discovery.
- OpenAI Help — Relevant for how ChatGPT-style systems surface or use web content and what site owners can control.
- Anthropic Docs — Helpful for understanding Claude-related product behavior and limits, where applicable.
- Perplexity Help Center — Useful for understanding citation-based answer behavior in a major AI answer engine.
Notes from Callum Bennett.