Query Fan-Out Generator
What is AI search really searching for when someone asks about you?
Type one question and see the hidden sub-queries an AI engine would run before answering it, each grouped by the type of search it performs and why it fires.
Why use the Query Fan-Out Generator?
AI engines judge your page against questions you never targeted. This tool makes those hidden questions visible and labels what each one is doing, so you can see exactly which branches your content already covers and which ones it leaves to a competitor.
Labelled by sub-query type
Every result is tagged with the kind of search it is, so you learn the pattern instead of just reading a list.
Plain-language output
No API keys, no jargon. Real search strings you can compare against the headings on your own page.
Free, instant, no signup
Type a question and get results in seconds. Nothing to install, no account, no credit card.
How does the Query Fan-Out Generator work?
Type the question a buyer would ask and the tool models the sub-queries an AI engine would run behind it, then groups them by what each search is doing. Results come back in seconds, with no signup and nothing to install.
Type the question your buyer asks
Use the phrasing a real customer would type into ChatGPT or Google AI Mode, not a keyword.
We model the fan-out
The tool predicts the sub-queries an AI engine would run behind that question, across six documented types.
Check your page against the list
Every sub-query your content does not answer is a branch a competitor can win instead of you.
Which sub-query types does it generate?
- Reformulation: The same question asked in different words, to catch pages that phrase it another way.
- Specification: A narrower version with a constraint added, like a budget, team size, or use case.
- Comparison: Head-to-head and "alternatives to" searches that pull in competitor round-ups.
- Follow-up: The question you would ask next, searched before you have asked it.
- Generalization: A broader background search that gives the engine context for the specific answer.
- Validation: Reviews, pricing, and credibility checks the engine runs to sanity-check its answer.
Common issues & solutions
⚠The sub-queries look generic and not specific to my topic.
✓This usually means the question was too broad. A short input like "marketing" gives the model almost nothing to specialise on. Type the full question a buyer would actually ask, such as "best email marketing tool for a small ecommerce store", and the sub-queries get far more specific.
⚠I ran the same question twice and got slightly different results.
✓That is expected, and it mirrors the real behaviour. Repeated results within 24 hours are served from cache so they will match, but real engines rewrite their fan-out per session anyway. Treat any single run as one plausible sample, not a fixed list to optimise against.
⚠My page already ranks first for the main question, so why does this matter?
✓Because ranking first does not mean you win the AI answer. That answer is assembled from several sub-queries you never competed in, so a page ranked lower that covers more of those branches can get cited instead of you. Coverage and ranking are separate problems.
⚠The tool rejected my input as not a real question.
✓The generator only handles searchable questions and topics. Gibberish, a single stop word, or an instruction written at the tool will be refused rather than answered with invented sub-queries. Rephrase it as something a person would genuinely type into a search box.
When should you use the Query Fan-Out Generator?
Use it any time you are deciding what a page should cover: briefing a new article, auditing one that ranks but never gets cited, planning a topic cluster, or showing a client why a single keyword no longer describes the job.
Who is the Query Fan-Out Generator for?
Anyone deciding what a page should say before they write it: founders covering their own category, content marketers turning one question into a full outline, and agencies making the hidden half of AI search legible to a client.
Indie SaaS founders
See what AI engines really search for when someone asks about your category, without needing an SEO background.
Content marketers
Turn one target question into a briefed outline where every heading maps to a sub-query an engine actually runs.
SEO & GEO agencies
Make the invisible part of AI search visible in a client deck, and justify depth over thin keyword pages.
Why does query fan-out matter for your brand?
The answer a buyer reads is assembled from searches you never saw and never optimised for, so a page can rank first for the typed question and still be absent from the AI answer above it. Every sub-query your content does not cover is a branch some other site gets cited for. We break down the full mechanism in what query fan-out is and how AI search splits one question into many.
Frequently asked questions
What is query fan-out?
Query fan-out is when an AI search engine splits your single question into several smaller searches, runs them at the same time, and writes one answer from the combined results. Google named the technique publicly in May 2025 when it launched AI Mode. The sub-queries are never shown to you.
Are these the exact queries Google or ChatGPT actually ran?
No. No engine publishes its fan-out queries, so nothing can capture them directly. This tool models the sub-queries a modern engine would plausibly generate, based on the documented sub-query types. Use it to find coverage gaps in your content, not as a log of real engine activity.
What do the sub-query types mean?
Each result is tagged with the job that search is doing: reformulation rephrases your question, specification narrows it, comparison pulls in rivals, follow-up predicts what you would ask next, generalization adds background, and validation checks reviews and pricing. Together they show the shape of a real fan-out.
How many sub-queries does a real AI engine generate?
It varies by engine and question complexity. iPullRank has counted 15 to 20 sub-queries in the expansion stage for a single example query, while Google says its Deep Search mode can issue hundreds of searches for one request. Simple factual questions may trigger almost no fan-out at all.
How do I use these sub-queries in my content?
Treat them as candidate headings on one thorough page rather than separate thin pages. Answer the follow-up and comparison branches directly, keep each section self-contained so it can be lifted out and cited alone, and add real comparison tables where the branches are competitor searches.
Does this tool tell me if AI engines mention my brand?
No. This tool only shows which sub-queries a question would likely expand into. It does not check whether ChatGPT, Gemini, or Perplexity actually name your brand in their answers. For that, Lead Rescue runs your real buyer questions across all three engines every day.
Knowing the questions is step one. Getting named is the goal.
Lead Rescue runs your real buyer questions across ChatGPT, Perplexity, and Gemini every day and tells you which ones actually mention and cite your brand.
Check your AI visibility free →