> ## Documentation Index
> Fetch the complete documentation index at: https://docs.qwairy.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Query Fan-Out

> Query Fan-Out gives you visibility into the real web searches triggered by Large Language Models (LLMs) through the mechanism known as Query Fan-Out.

When an LLM answers a user query and needs to verify or supplement information, it performs multiple web searches in the background. Qwairy captures and analyzes these searches.

This feature helps you understand:

* What **LLMs search on the web** in your domain
* Where your **brand is missing** in the sources they consult
* Which **competitors appear** in those search results
* What **content opportunities** you should prioritize to influence LLM responses

<iframe src="https://www.youtube.com/embed/PTc4QmPMr0o" title="YouTube video player" frameborder="0" className="w-full aspect-video rounded-xl" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen />

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# 🌐 What is Query Fan-Out?

**Query Fan-Out** is the process where an LLM sends several web search requests during the generation of a response.\
Whenever a model needs additional information, it “fans out” multiple queries to search engines like Google, Bing, or DuckDuckGo.

Examples of queries triggered by LLMs:

* *“Best online design tools for collaborative projects 2025”*
* *“Top design collaboration tools for remote teams”*
* *“Figma alternatives for UX teams”*

Qwairy monitors these outgoing web queries, groups them, and helps you understand what models are researching about your field or brand.

More information:\
➡️ [https://www.qwairy.co/guides/geo-101-glossary/query-fan-out](https://www.qwairy.co/guides/geo-101-glossary/query-fan-out)

# 🎯 Purpose of Query Fan-Out

Query Fan-Out helps you answer three essential questions:

### **1. What topics are LLMs researching in my industry?**

You see the exact queries, their frequency, and their priority level.

### **2. Is my brand present in the sources used by LLMs?**

Qwairy checks if your brand appears in the results returned for these queries.

### **3. Which competitors dominate these searches?**

You see what brands are consistently mentioned, even when you are not.

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# 🧭 Navigating Query Fan-Out

From the main navigation:\
**Monitor > Insights > Query Fan-Out**

Each item represents a **real web query executed by an LLM**.

### Each query includes:

#### **• Usage**

How many times this query was fired by LLMs.

#### **• Competitor count**

Example:\
`10 competitors` = 10 brands appear in the results sourced by the model.

#### **• Brand Absent**

Your brand is not detected in the relevant results for this query.

#### **• Providers**

Which LLM triggered the query:

* ChatGPT
* Claude
* Gemini
* Other

#### **• Topics & Tags**

Automatic categorization by subject and intent.

#### **• Associated prompt**

The user prompt that caused the LLM to perform this search.

#### **• Actions**

* **Open on Google** — check the actual search result
* **Add to Prompts** — add this query to Prompt Tracking
* **Create Content** — generate a content brief to fill the gap

# 🏆 Use Cases

### ✅ **Find missing content opportunities**

Identify topics where LLMs search but your brand is not present.

### ✅ **Understand why LLMs recommended competitors**

See which sources they rely on and where competitors dominate.

### ✅ **Prioritize SEO and content investments**

High-priority queries reflect real LLM behavior — not just keyword volume.

### ✅ **Improve your visibility in AI answers**

By influencing the content LLMs retrieve, you indirectly influence their responses.