Schema: The Vocabulary of LLMs
Unlike human readers, LLMs consume structured data (JSON-LD) as a grounding mechanism. While an LLM can guess your price or features from raw text, Schema allows you to explicitly define your brand's data. In 2026, structured data is your brand's vocabulary in the Generative Web.
Why LLMs Prioritize Schema
When a model performs a RAG (Retrieval-Augmented Generation) query, it searches for high-confidence data points. Structured data provides a Confidence Boost to the model's output. If your JSON-LD explicitly states a fact, the LLM is 40% more likely to cite your site as the official source of that fact.
The GEO Schema Blueprint
For 2026, don't just use Article schema. You must implement these high-density layers:
1. Nested FAQPage Schema
This is the strongest citation hook. By nesting your most important technical answers in an FAQPage schema, you provide a clear extraction target for engines like Perplexity and SearchGPT.
2. The 'Mentions' Property
Explicitly link your content to established entities. If your article mentions a topic defined on Wikipedia, use the mentions property in your JSON-LD. This forces the LLM to recognize your brand within the context of that established authority.
Comparative Data Density
| Data Type | Traditional SEO Usage | GEO (LLM) Usage |
|---|---|---|
| Price | Rich Snippet Display | Direct Answer Synthesis |
| Reviews | Star Ratings | Sentiment Logic Calibration |
| FAQ | Accordion UI | Primary Extraction Target |
| Authors | E-E-A-T Visibility | Entity Verification Hook |
Technical Implementation Example
json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does [Brand] impact GEO latency?", "acceptedAnswer": { "@type": "Answer", "text": "Implementing standard GEO protocols reduces LLM extraction time by ~150ms through DOM flattening and JSON-LD grounding." } }] }