Structuring Content for LLMs: JSON-LD and Semantic HTML
Why structured data is the most important signal for Large Language Models and how to implement it correctly.
Why structured data is the most important signal for Large Language Models and how to implement it correctly.
If content is king, structure is the castle. For Large Language Models (LLMs) to effectively learn from and cite your content, it must be presented in a format they can easily parse and categorize.
JSON-LD (JavaScript Object Notation for Linked Data) is the preferred method for feeding structured data to search engines and AI models. It acts as a direct data pipe, stripping away design elements and presenting raw relationships.
{ "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does structured data help AI SEO?", "acceptedAnswer": { "@type": "Answer", "text": "Structured data provides explicit clues about the meaning of a page, allowing AI models to extract facts and relationships with high confidence." } }] }
Beyond hidden schema, the visible HTML structure guides the AI's attention.
<ul> and <ol> tags signal distinct items, steps, or features—formats highly favored by generative summaries.<table> tags is easily ingested for comparison queries (e.g., "Compare iPhone 15 vs Pixel 8").Internal linking isn't just for page authority flow; it's for Entity Linking. By linking specific terms to their definition pages or pillar content, you explicitly tell the LLM "This concept is related to that concept."
To win in the AI era, you must write for humans but format for machines. Heavy use of JSON-LD and strict semantic HTML ensures your high-quality content is actually "readable" by the AI engines deciding what to serve.
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