Fan-Out Queries · How-To Guide

How to find ChatGPT's hidden fan-out queries.

When you ask ChatGPT a question that needs current information, it doesn't just fire off a single search and call it a day. It takes your prompt, breaks it apart into multiple angles, and sends separate web searches for each one. These are called fan-out queries, and they determine which websites end up in ChatGPT's answer - and which get ignored entirely.

What Are Fan-Out Queries?

When ChatGPT searches the web, it generates 8 to 15 separate sub-queries from a single user prompt. Each sub-query targets a different intent, attribute, or comparison angle. The final answer is synthesized from all of them - meaning your brand needs to rank across multiple sub-queries, not just one.

How Fan-Out Works: A Visual Breakdown

Here's what happens behind the scenes when a user asks ChatGPT a single question. The model decomposes it into multiple search angles:

"Best CRM for small businesses?"

• SUB-QUERY 1: "best CRM software smallbusiness 2026 reviews"
• SUB-QUERY 2: "CRM pricing comparisonsmall teams"
• SUB-QUERY 3: "HubSpot vs Salesforcevs Pipedrive"
• SUB-QUERY 4: "CRM features contactmanagement automation"
• SUB-QUERY 5: "best free CRM toolfor startups"

Your website might rank on page one of Google for "best CRM for small business." But if it doesn't show up for the specific sub-queries ChatGPT actually sends, your brand could be completely absent from the AI-generated answer. That's the fan-out gap.

Watch: How Query Fan-Out Works

If you prefer a visual walkthrough, this explainer from Mike King at iPullRank breaks down exactly how query fan-out works across AI search - why one prompt becomes many searches, and what it means for your content strategy:

• Query Fan Out - What it is, How it Works, and What You Should Do About it for AI Search Strategy - YouTube

The Hard Way: 5 Steps in Browser DevTools

There is a way to see exactly which queries ChatGPT sends when it searches the web. It involves intercepting your browser's network traffic using Developer Tools. Here's the step-by-step process:

1. Send a prompt that triggers web search: Open ChatGPT and type a question that requires current information - something like "What are the best project management tools for remote teams in 2026?" Wait for the full response to load.

2. Copy the conversation ID from the URL: In the URL bar you'll see something like chatgpt.com/c/68f1007d-7e08-832b-... Copy the first alphanumeric segment (e.g. 68f1007d). You'll need it as a filter.

3. Open Developer Tools → Network tab: Right-click anywhere on the page and select "Inspect", or press Ctrl+Shift+I (Windows) / Cmd+Option+I (Mac). Click the Network tab at the top of the DevTools panel.

4. Refresh the page and filter by conversation ID: With the Network tab open, refresh the page. Dozens of requests flood in. Paste the conversation ID into the filter box to isolate the relevant one.

5. Search the response for search_model_queries: Click the filtered request, open the Response tab, and press Ctrl+F to search for "search_model_queries". If web search was triggered, you'll find the exact queries ChatGPT issued as web searches.

What You'll Find in the JSON Response

If the prompt triggered web search, the JSON payload will contain a search_model_queries field with every query ChatGPT issued - typically between 3 and 15 separate searches.

Example JSON Response

{
  "search_model_queries": [
    "best project management tools remote teams 2026",
    "Asana vs Monday.com vs ClickUp comparison",
    "project management software pricing per user",
    "remote team collaboration tools features integrations",
    "best free project management tool for startups"
  ]
}

One user prompt produced five distinct search angles - review content, brand comparisons, pricing, features, and free tiers.

Why the Manual Method Falls Apart

The DevTools trick works for a quick peek, but it completely breaks down the moment you try to use it systematically. Here are the four reasons it won't work for real SEO strategy:

• One conversation at a time: Want to analyze 50 product queries? You'll need to repeat the entire 5-step process 50 times.
• Queries change every session: Ask the same question tomorrow and ChatGPT may generate completely different sub-queries. A single snapshot tells you nothing about patterns.
• Requires DevTools expertise: Not everyone on your marketing team knows how to filter network requests and parse JSON payloads.
• No visibility into real user prompts: You can only see fan-out queries for YOUR conversations. Zero visibility into what ChatGPT searches when actual customers ask about your industry.

Browser Extensions Don't Fix the Core Problem

A few Chrome extensions and bookmarklets can automate extracting queries from the JSON response - but they share the same fundamental limitation: they only show fan-out queries for your own conversations. You're still limited to one prompt at a time, one session at a time, with zero visibility into what real users are actually asking about your industry.

Quick Comparison: Manual vs. Aiso

The Easy Way: See Fan-Out Queries at Scale with Aiso

Instead of manually inspecting your own conversations one by one, Aiso's Fan-Out Intelligence gives you access to fan-out queries generated across real ChatGPT conversations - not just yours, but from actual users asking questions about your industry, your competitors, and your product categories.

Frequently Asked Questions About Query Fan-Out

What is query fan-out in ChatGPT?

Query fan-out is the process where ChatGPT takes a single user prompt and breaks it into multiple separate web search queries - typically 8 to 15 - each targeting a different intent, attribute, or comparison angle. ChatGPT then scrapes and synthesizes the results from all of them into one answer.

How many queries does ChatGPT fan out into?

When ChatGPT triggers a web search, it usually generates between 8 and 15 sub-queries from one prompt, though the count varies by how complex and comparison-heavy the question is. Each sub-query returns its own set of web results that feed the final synthesized answer.

How do I find ChatGPT's fan-out queries?

You can reveal them manually with browser DevTools: send a prompt that triggers web search, copy the conversation ID from the URL, open DevTools → Network, refresh and filter by that ID, then search the response for the search_model_queries field. To see fan-out queries across real user conversations at scale instead of just your own, use a tool like Aiso.