GEO Citability: Use JSON-LD so AI can Cite You
Citability is the metric that defines whether an LLM will cite your content or ignore it. Without JSON-LD, your post is plain text: AI engines extract entities and relationships, they don’t read prose like humans. Without structured data, you don’t exist in AI responses.
This strategy is the perfect companion for a 0kb JS architecture in Astro 6, ensuring that loading speed translates into semantic authority.
Which JSON-LD block makes your content citable?#
Inject a TechArticle block with the entities your content mentions. This allows the AI Crawler to assign your text to a specific knowledge node without ambiguity:
{
"@context": "https://schema.org",
"@type": "TechArticle",
"name": "GEO Citability: Data for the AI Index",
"description": "Data structure to optimize LLM citability.",
"dependencies": "Astro 6.0",
"about": [
{ "@type": "Thing", "name": "GEO" },
{ "@type": "Thing", "name": "Graph Entity Optimization" }
]
}Why do LLMs prefer JSON-LD over plain text?#
Modern language models (like GPT-4o or Claude 3.5) don’t just “read”; they map tokens to entities. JSON-LD acts as an “accessibility ramp” for their parsers.
While a direct BLUF answers the immediate question, JSON-LD is the authority anchor that allows the model to verify the source. This is the foundation of what we define in our guide on GEO and the new SEO for AI.
Turn your headings into “Question-Answer” pairs. If your H2 doesn’t answer something a human (or AI) would ask, you’re losing Perplexity traffic.
Structuring data doesn’t just help machines; it helps readers using assistive reading tools understand the hierarchy and real purpose of your information.