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.

The Semantic Scaffold

"employee": {
  "jobTitle": "CEO",
  "name": "John"
}

There is no room for hallucination here. The relationship is explicit.

ClaimReview and TechArticle

By wrapping your claims in ClaimReview or TechArticle schema, you provide a semantic scaffold that makes it “computationally cheaper” for the model to output the truth than to make up a lie.

  • ClaimReview: Use this to fact-check common misconceptions in your industry. “Claim: AI kills SEO.” “Rating: False.”
  • TechArticle: Use this for documentation. “API Version: 2.0.”

The “Trust bit”

We believe that highly-structured sites flip a “Trust Bit” in the model’s retrieval logic. When the model encounters valid, dense Schema, it lowers its “temperature” (randomness) for that source, treating it as a reference table rather than creative writing.

Don’t just write text; write structure.

Knowledge Graph vs. Text Vector

There is a war inside the AI model between “What the text says” (Probabilistic) and “What the Knowledge Graph says” (Deterministic). Schema is your way of voting for the Deterministic side.

The “Entity Resolution” Benefit: If your text says “We offer a refund,” but your Schema says refundPolicy: none, the model encounters a conflict. In high-quality models, specific Schema properties often override vague text. By ensuring strict parity between your Schema and your Prose, you increase the “Confidence Score” of the generation.

Glossary of Terms

  • Agentic Web: The specialized layer of the internet optimized for autonomous agents rather than human browsers.
  • RAG (Retrieval-Augmented Generation): The process where an LLM retrieves external data to ground its response.
  • Vector Database: A database that stores data as high-dimensional vectors, enabling semantic search.
  • Grounding: The act of connecting an AI’s generation to a verifiable source of truth to prevent hallucination.
  • Zero-Shot: The ability of a model to perform a task without seeing any examples.
  • Token: The basic unit of text for an LLM (roughly 0.75 words).
  • Inference Cost: The computational expense required to generate a response.