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:
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:
hidden sub-queries fired from a single ChatGPT prompt
web pages scraped and synthesized into one answer
head keyword you rank for ≠ visibility across every sub-query
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:
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.
Tip: The question must require live data. Generic knowledge questions won't trigger web search.
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.
Tip: This ID is unique to each conversation and lets you isolate the right network request.
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.
Tip: If this is your first time in DevTools, it can look overwhelming - but you only need the Network tab.
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.
Tip: You're looking for a "fetch" or "xhr" type request in the filtered results.
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.
Tip: If the field isn't there, ChatGPT answered from training data alone - no web search happened.
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.
{
"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" ]}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:
Want to analyze 50 product queries? You'll need to repeat the entire 5-step process 50 times.
Ask the same question tomorrow and ChatGPT may generate completely different sub-queries. A single snapshot tells you nothing about patterns.
Not everyone on your marketing team knows how to filter network requests and parse JSON payloads.
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
Here's how the manual DevTools approach stacks up against using Aiso's Fan-Out Intelligence:
| Capability | DevTools / Extensions | Aiso |
|---|---|---|
| See fan-out queries | Your conversations only | Real user conversations at scale |
| Technical skill needed | DevTools & JSON parsing | None - dashboard interface |
| Analyze multiple prompts | One at a time, manually | Hundreds, automatically |
| Track changes over time | Not possible | Built-in time tracking |
| Competitor visibility | Only if you test their queries | See who ranks for each sub-query |
| Team access | Individual browser only | Shared dashboards & exports |
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.
Stop Spinning Up DevTools. Start Seeing the Full Picture.
The DevTools method is useful to prove to yourself that fan-out queries exist. But if you want to use this data strategically - to inform content creation, close visibility gaps, or monitor how AI search evolves for your industry - you need something that works at scale.
That's what Aiso does. No DevTools. No JSON parsing. No spinning up individual conversations one prompt at a time. Just the data you need to make your brand visible where AI search is actually looking.
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.
What is search_model_queries?
search_model_queries is the field in ChatGPT's network response that contains the exact list of web search queries the model issued for a given prompt. If the field is present, web search was triggered; if it's missing, ChatGPT answered from its training data alone without searching the web.
Does Google AI Mode use query fan-out too?
Yes. Query fan-out is not unique to ChatGPT. Google's AI Mode and AI Overviews, plus Gemini and Perplexity, all decompose a single query into multiple related searches before synthesizing an answer. Optimizing for the sub-queries - not just the head term - is what makes a brand visible across AI search.
Further Reading
- Query Fan-Out: Everything You Need To Know - Surfer SEO
- What Is Query Fan-Out & Why Does It Matter? - Semrush
- ChatGPT and Gemini Query Fan-Out: How AI Searches the Web - Aiso Blog
- Query Fan Out - What It Is, How It Works, and What to Do About It - iPullRank (video)
Published February 15, 2026 · Updated June 5, 2026. Data references Aiso's proprietary panel of 5M+ ChatGPT conversations.