Query Fan-Out Tool
QueryBurst's Query Fan-Out tool simulates how Google's AI Mode and AI Overviews decompose a user query into thematic sub-queries for research. Based on Google's patented "Thematic Search" methodology, it generates the same type of fan-out queries that AI systems dispatch internally — plus the reasoning behind each one, authority signals to look for, and potential sub-themes for deeper exploration.
Why Simulated Fan-Outs Are Just As Good
- They're LLM-generated — just like the real ones. Google's fan-out queries are generated by a language model. They're probabilistic, not deterministic. Same training data, same patterns, same thematic clusters.
- Real fan-outs change every time. Run the same query twice and you'll get different sub-queries. Tracking the "exact" ones is tracking noise. The themes converge — the specific wording doesn't matter.
- Any experienced SEO can predict them. Fan-out queries for "best CRM software" will include pricing comparisons, integration lists, and reviews. This tool systematises what you already know and adds the layers you don't.
- ChatGPT just removed public fan-out data. Prompt trackers that relied on exposed fan-out queries lost that signal overnight. This tool makes that irrelevant — because you never needed the "exact" ones in the first place.
Why This Is Different
- Based on Google's patents. Not a guess. Google's "Thematic Search" patent (US12158907B1) describes how search results are decomposed into thematic clusters using passage summarisation and a clustering engine. Their "Search With Stateful Chat" patent describes the broader AI Mode pipeline. Our tool simulates this methodology.
- Reasoning, not just queries. Every fan-out query comes with an explanation of why it was generated and why it's strategically important. Tracking tools never gave you this.
- Authority signals included. For each theme, you get the quality indicators, credibility markers, and content types that the system would prioritise when evaluating results.
- Free to use. Available in our SEO app with no subscription required. Also available as part of the Verify platform for site-level analysis.
Free account includes limited access to all SEO app tools · Also available in Verify
Stop tracking fan-outs. Start understanding them.
The AI search industry has spent the last year building tools to scrape, track, and chart query fan-outs — the sub-queries that AI systems like Google AI Mode and ChatGPT generate behind the scenes when constructing an answer.
Then ChatGPT removed the data from their API. Overnight, the trackers lost their signal. Panic ensued.
But here's the thing: it doesn't matter. Fan-out queries are generated by a language model. They're probabilistic. They change every time. The "exact" fan-outs for a given query at a given moment are a snapshot of noise, not a strategic asset.
What matters is the themes. And the themes are predictable — because the model learned them from the same internet we all read. Google's own patent describes a two-phase process: start with a broad search, then break into thematic sub-queries based on expected content patterns. That's what our tool simulates.
The difference is that we don't just give you a list of queries. We give you the reasoning behind each one, the authority signals the system would look for, and the sub-themes it would drill into. That's the intelligence that tracking tools never provided — because they were too busy monitoring the output to understand the input.
What You Get (Beyond Just The Queries)
Prompt trackers gave you a list of sub-queries. Our tool gives you the complete research strategy — the same intelligence that Google's AI uses to plan its answer.
Initial Broad Query
The foundational search the system would start with — the first query in the two-phase process described in Google's Thematic Search patent.
Hypothetical Content Summary
What types of information and sources the system expects to find from the initial search. This tells you what the model considers "standard" for your topic.
Thematic Sub-Queries (Priority Scored)
3–7 specific fan-out queries, each scored by priority (critical, important, or supplementary). These are the research threads the AI would pursue.
Generation Reasoning
For each theme: why this specific sub-query would emerge from the initial results. This shows the logical chain from broad search to focused research.
Strategic Reasoning
Beyond generation reasoning: why each sub-query is strategically necessary for comprehensive coverage. This is the "why it matters" layer.
Authority Signals
The quality indicators, credibility markers, and specific criteria the system would use to evaluate results for each theme.
Cite Examples
Types of content, specific publications, and source formats the system would prioritise when selecting citations for each theme.
Synthesis Instructions
How the AI should combine all research findings into a coherent, user-focused response. This reveals the answer structure the model is building toward.
DIY Version — Copy This Prompt
Want a quick version? Copy this prompt into any LLM. It'll generate the fan-out queries. For the full patent-based analysis with reasoning, authority signals, and sub-themes — use our free tool.
For the full patent-based analysis with generation reasoning, strategic reasoning, authority signals, cite examples, and sub-themes — use our free tool.
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Frequently Asked Questions
Query fan-out is the process by which AI search systems like Google AI Mode, ChatGPT, and Perplexity break a single user query into multiple sub-queries to gather comprehensive information. For example, "best personal injury lawyer in New York" might fan out into sub-queries about specialisations, fee structures, case results, client reviews, and jurisdiction experience. Google confirmed this technique at Google I/O 2025, and the mechanism is described in their "Thematic Search" patent (US12158907B1) and "Search With Stateful Chat" patent.
Real fan-outs are generated by a language model and are probabilistic — they change every time. Tracking the "exact" fan-outs for a given query at a given moment gives you a snapshot of noise, not a strategic asset. Our tool generates the same type of thematic sub-queries using the same type of model, and adds layers that tracking tools never provided: reasoning, authority signals, and sub-themes. You get the intelligence without the noise.
Yes. The tool is primarily based on Google's "Thematic Search" patent (US12158907B1, granted December 2024), which describes how Google takes the top search results for a query, generates summary descriptions for every passage using a language model, clusters those summaries into thematic groups, and ranks the themes by prominence — including how many distinct documents mention each theme. This is the fan-out mechanism: broad query in, thematic sub-queries out. We also reference the "Search With Stateful Chat" patent, which describes the broader AI Mode pipeline including snippet scoring and citation verification. The latter was covered in detail on Moz by John Iwuozor.
Yes. The Query Fan-Out tool is available for free in our SEO app with limited monthly uses. For unlimited access and the full suite of 20+ AI search optimization tools (including Answer Spy, Site Investigation, and the Retrieval Optimizer), the full platform is $59/month.
No. Fan-out queries are sub-queries that any experienced SEO can predict for their niche. What matters isn't the exact sub-queries — it's understanding what the model considers important for your topic and ensuring your content covers those factors. The fan-out tool helps you see the themes and reasoning, but the real value is in using that intelligence to optimise your content. If you want to go further, pair it with our Answer Spy tool to extract the specific decision criteria the model uses when recommending products and services.
In March 2026, ChatGPT stopped exposing query fan-out data in their API responses. Prompt tracking tools that relied on scraping this data lost that signal. Our tool was unaffected — because we simulate fan-outs using the patent-based methodology rather than scraping them from a third-party API. The themes are the same regardless of the source.