Schema Markup
SEO/AEO/GEOCode added to your pages that labels content for search engines — turning plain HTML into structured data that powers rich results, AI answer citations, and…
LLM-Friendly Markup | AI-Readable Structured Data
Structured data for LLMs is markup, formatting, and content patterns that make a website easy for large language models to parse, extract, and cite correctly. It includes schema.org JSON-LD, semantic HTML, clean heading hierarchies, FAQ blocks, and consistent metadata. Unlike structured data for Google, which focused on rich results, structured data for LLMs is about giving the model enough scaffolding to understand and quote your content without guessing.
AI models do not read your site the way a designer does. They read the HTML. A page that looks beautiful but ships as a wall of div tags with no headings, no schema, and no semantic markup is invisible to extraction logic. Meanwhile, a plain-looking page with clean h1, h2, h3 structure and proper schema gets quoted verbatim. The teams winning AI citations are the ones who invested in technical content infrastructure — and they tend to be the same teams that already win on Google. The work compounds across channels.
Start with semantic HTML — real h1 through h3 headings in document order, lists as ul or ol, tables as actual tables. Add schema.org markup using JSON-LD for the page type: Article, Product, FAQ, HowTo, Organization. Keep one primary topic per page. Put direct answers in the first paragraph under each heading. Use descriptive, consistent metadata across the title, description, and Open Graph tags. For deeper extraction, publish an llms.txt at your root pointing models at the canonical pages. Most modern frameworks like Next.js and Astro make this straightforward — but the work has to be done deliberately. Default templates rarely produce structured-data-friendly output without a content engineer involved.
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