LLM SEO
Optimises your content and brand signals so large language models can better understand, retrieve, and cite your site in AI-generated answers.
If you treat LLM SEO as just keyword optimisation, you will miss the structural and entity signals that make your content citable.
Key points
- Structure content with clear headings, short answer blocks, and bullet points to help models extract answers.
- Use schema markup to clarify context for entities, products, and people on your pages.
- Maintain consistent naming for brands, people, and concepts across your site to strengthen entity recognition.
- Ensure technical accessibility: allow crawlers and avoid heavy JavaScript rendering that AI systems may not process.
A travel site restructures for AI citation
A travel site published a guide to 'best hiking boots for beginners' with standard keyword optimisation. After restructuring the page with question-based headings (e.g., 'Which hiking boot is best for wide feet?'), adding entity schema for brands like Merrell and Salomon, and linking to a pillar page on hiking gear, the site appeared in 12% of ChatGPT answers for related queries within two weeks. Referral traffic from AI tools increased by 40%.
Three ways LLM SEO differs from classic SEO
- Focus on extraction LLM SEO prioritises making content easy for models to extract answers from, not just ranking for keywords. This means using question headings and concise summaries.
- Entity signals over keywords Consistent naming of brands, people, and products helps models connect your page to the right topic cluster. Entity SEO builds that foundation.
- Measurement via citations Success is measured by citations and mentions in AI answers, not just click-through rates. Tools like Perplexity's help centre track this.
Four common mistakes that hurt AI visibility
- Blocking crawlers Restricting access via robots.txt or heavy JavaScript can prevent AI systems from reading your content. Technical accessibility remains essential.
- Thin, generic content Publishing low-originality pages without unique data or expertise reduces the chance of citation. AI models favour authoritative sources.
- Inconsistent naming Using different names for the same brand or product confuses entity recognition. Stick to one canonical label across your site.
- Ignoring structure Long paragraphs without headings or lists make it hard for models to find answer blocks. Use logical heading hierarchy and bullet points.
Three signals that matter most for citation
- Clear answer blocks Short, direct answers to common questions, placed under descriptive headings, increase the likelihood that a model will extract and cite your content.
- Topical authority Building a network of interconnected content around a central topic, using pillar pages and topic clusters, signals expertise to both search engines and AI models.
- External authority signals Being cited by high-authority sources like Forbes can strengthen your entity recognition and improve the chance of AI citation. Forbes SEO is one example of leveraging such signals.
Common questions
How does LLM SEO differ from GEO?
LLM SEO is broader, covering future training-data visibility, while GEO focuses on live citations in generative engine responses.
What tools measure LLM SEO visibility?
Practitioners use AI visibility trackers, citation monitors, and referral traffic analysis from tools like ChatGPT and Perplexity.
Is LLM SEO a replacement for classic SEO?
No, it builds on conventional content quality, technical SEO, and authority signals. Both are needed for comprehensive visibility.
Sources
- Google Search Central Official documentation on search features and AI overviews.
- OpenAI Help / documentation Guidance on how ChatGPT retrieves and cites sources.
- Perplexity Help Center Information on citation behaviour and AI answer generation.