The Gemini Retrieval Logic
Google Gemini is unique in the AI search ecosystem because it sits on top of the world's most powerful legacy index. Unlike ChatGPT or Perplexity, Gemini uses Google's Knowledge Graph as a primary grounding layer. To be cited by Gemini, you don't just need structured data; you need Validated Authority.
The E-E-A-T Sentiment Vector
Gemini is trained to prioritize 'Safe and Authoritative' sources. It uses a Sentiment Vector calculated from billions of web signals (reviews, forums, news). If your brand has a high 'Trust Score' in Google's legacy index, Gemini is 3x more likely to cite you in its generative response.
The 'Multimodal' Extraction Hook Gemini is a native multimodal model. This means it processes images and videos as part of its retrieval logic.
- Optimized Alt-Text: Use descriptive, technical alt-text for all diagrams. Gemini uses this to understand the 'data density' of your visual assets.
- Schema Grounding: Use
VideoObjectandImageObjectschema to explicitly link your visual data to your brand entity.
Performance Benchmarks for Gemini
| Signal | SEO Priority | Gemini (GEO) Priority |
|---|---|---|
| LCP / Core Web Vitals | High | Low (Crawlers bypass UI) |
| Sentiment Score | Medium | Critical (Ranking Factor) |
| Knowledge Graph | High | Ultimate (Citation Basis) |
| JSON-LD | Basic | Advanced (Entity Mapping) |
The 'Verified Expert' Tactic
Gemini looks for clear authorship signals. It cross-references the Author field in your JSON-LD with its own internal database of experts.
Action: Ensure your authors have clean, authoritative social profiles (LinkedIn, ResearchGate) and are consistently linked across all your site's schema. This creates a 'Chain of Trust' that Gemini's retrieval agents can verify at runtime.