Just as a grounding wire directs excess electricity safely to earth, Schema.org markup directs model inference safely to the truth.
In the chaotic world of unstructured text, hallucinations thrive. “The CEO is John” might be interpreted as “The CEO dislikes John” depending on the sentence structure. But Structured Data is unambiguous.
"employee": {
"jobTitle": "CEO",
"name": "John"
}
There is no room for hallucination here. The relationship is explicit.
In the rush to build “AI-Powered” search experiences, engineers have hit a wall. They built powerful vector databases. They fine-tuned state-of-the-art embedding models. They scraped millions of documents. And yet, their Retrieval-Augmented Generation (RAG) systems still hallucinate. They still retrieve the wrong paragraph. They still confidently state that “The refund policy is 30 days” when the page actually says “The refund policy is not 30 days.”
Why? Because they are feeding their sophisticated models “garbage in.” They are feeding them raw text stripped of its structural soul. They are feeding them flat strings instead of hierarchical knowledge.
For twenty-five years, the primary metaphor of SEO was “Indexing.” The goal was to get your page into the database. Once indexed, you competed for rank based on keywords and links. It was a game of lists.
In the age of Generative AI, the metaphor has shifted fundamentally. We are no longer fighting for a slot in a list; we are fighting for Grounding.
Grounding is the technical process by which an AI model connects its generated output to verifiable external facts.