The core difference here is that traditional SEO is about indexing; LLM retrieval is often on-demand. If a model fetches your page in real-time and can't parse the meat of the content immediately, you've lost that interaction. There is no "crawl budget" to save you.
1. High-Leverage Structured Data #
JSON-LD via Schema.org is the most reliable way to feed facts directly to an AI without forcing it to guess based on your CSS layout. To improve your visibility in AI-generated answers, prioritize these specific types:
Article: Essential for establishing authorship and date modified.FAQPage: This is a goldmine for AEO (Answer Engine Optimization) because it maps directly to the Q&A format these models use.HowTo/Product: Critical for technical or e-commerce sites.
Here is a practical example of an
FAQPage
implementation:
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "Does Perplexity cite websites in its answers?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Yes, Perplexity typically cites the sources it retrieves from directly in its response."
}
}
]
}
2. Deploying an llms.txt File #
The emerging standard for LLM accessibility is the llms.txt
file. Think of it as a robots.txt
but specifically designed for models. It lives at your root (/llms.txt
) and provides a curated, Markdown-formatted map of your most important pages.
The goal is to provide a clean entry point so the model doesn't have to guess which pages are actually useful.
> One-sentence description of what you do, written the way you'd pitch it in a boardroom.
## Docs
- [Getting Started](https://yourdomain.com/docs/getting-started): Setup and installation
- [API Reference](https://yourdomain.com/docs/api): Full endpoint documentation
## Blog
- [How AI Search Engines Retrieve Content](https://yourdomain.com/blog/ai-search-retrieval): [Technical breakdown of ChatGPT, Gemini, and Perplexity retrieval]
## Optional
- [Changelog](https://yourdomain.com/changelog): Recent updates
When shipping this, keep the H1 as the very first line and avoid listing every single URL. A curated list is far more effective for a model's context window than a raw sitemap. This is a simple, beginner-friendly deployment that significantly lowers the friction for AI agents trying to understand your site's architecture.
Next MoE Capacity Factor: Why Your Tokens are Being Dropped →