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.
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.
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
Published February 15, 2026. Data references Aiso's proprietary panel of 5M+ ChatGPT conversations.