Deciphering Fan-out and Implicit Queries in ChatGPT & Gemini
Master Generative Engine Optimization. Discover how Implicit and Fan-out Queries shape AI search and how the queries ChatGPT and Gemini use impact visibility.
In 2026, the term "keyword" has been replaced by more complex mechanical processes in AI Search. To understand why your brand appears (or disappears) in AI responses, you must look at how models like ChatGPT and Gemini handle Fan-out Queries, the engine behind modern Generative Engine Optimization (GEO).
What are Fan-out Queries?
A Fan-out Query occurs when an AI model takes a single user prompt and "fans it out" into multiple parallel search operations. Unlike a traditional search engine that looks for one set of results, an AI acts as an orchestrator.
If a user asks, "What are the best energy-efficient heaters available in Bucharest?", the AI doesn't just search that phrase. It executes a fan-out strategy:
- Search A: "Top-rated energy-efficient heater models 2026"
- Search B: "Current electricity prices in the UK vs. heater consumption"
- Search C: "Local retailers in London with in-stock heating appliances"
Fan-out vs. Implicit Queries
While Implicit Queries represent the intent (the hidden questions), Fan-out Queries represent the execution. AI visibility tools now focus on these because they reveal exactly which "sub-topics" a brand must dominate to win the final recommendation.
1. How Fan-out Logic Changes Across Models
ChatGPT and Gemini do not "fan out" in the same way. Their internal branching logic is the primary reason for different rankings.
- ChatGPT (SearchGPT Logic): Often fans out toward authoritative reviews and "social proof" (Reddit, specialized tech blogs) to find a consensus.
- Gemini (Google Ecosystem Logic): Frequently fans out toward its own Knowledge Graph and Google Shopping data, prioritizing structured product data and local business listings.
The GEO Strategy: To be visible, a brand must ensure it is the "answer" to multiple branches of the fan-out. If you only optimize for "pricing" but miss the "sustainability" branch, the AI may pick a competitor who covers both.
2. Location-Aware Fan-out: The Bucharest vs. London Split
Geography is the strongest filter for fan-out behavior. When an AI detects a local intent, it triggers specific Local Fan-out Queries.
- Currency & Specs: For a user in France, the AI will fan out to find prices in EUR and check for EU-standard compliance.
- Hyper-Local Citations: In the fan-out process, the AI specifically targets local domains (e.g., .fr domains, local news outlets). If your content is only on global .com sites, you will fail the "local availability" branch of the fan-out.
3. The API vs. Web Interface Gap in Fan-out
A major pitfall in AI visibility tracking is relying on API data.
- Limited Fan-out in APIs: Standard API calls often perform a "shallow" search or no search at all to save latency and cost.
- Deep Fan-out in Web Interfaces: The consumer-facing versions (what your customers use) perform "Deep Fan-out," searching 5-10 sources simultaneously.
Genezio's Advantage: By simulating Real Web Conversations, Genezio captures the full fan-out effect, ensuring you see the same citations and local retailers that a real user sees.
4. Stability and Visibility
Because fan-out queries are dynamic and can change based on the model's "mood" (stochastic nature), visibility is never 100% stable.
- Run Variability: In one instance, the AI might fan out to a YouTube review; in another, to a technical manual.
- Aggregated Metrics: This is why Visibility is calculated over multiple runs. Genezio aggregates these fan-out queries to tell you: "In 80% of conversations, your brand is the primary recommendation."
Summary: Optimizing for the Fan-out Era
| Strategy | Actionable Step |
|---|---|
| Dominate the Fan-out | Create content that covers all "sub-queries" (Price, Specs, Reviews, Local Stock). |
| Technical GEO | Use Schema.org to make your data "branch-friendly" for AI crawlers. |
| Local Authority | Get mentioned on local .ro domains to win the geographic fan-out branch. |
| Monitor Drift | Use Genezio to see if your brand is losing visibility in specific fan-out queries. |
How Genezio Tracks Fan-out Performance
Genezio is designed for this multi-branch reality. We don't just track if you "ranked"—we analyze the entire fan-out structure:
- Branch Analysis: We identify which specific fan-out queries (e.g., "value for money" vs "technical specs") are leading to your brand or your competitors.
- Geo-Specific Simulation: We execute fan-out searches from specific local IPs to ensure the AI's internal search is hitting local Romanian databases and retailers.
- Cross-Model Visibility: Compare how effectively you capture the fan-out logic in Gemini versus ChatGPT.
Ready to map the fan-out queries for your brand? Try Genezio today.
