Query fan-outs explained: what AI engines search before they answer
When an AI engine answers a question with search, it often runs several narrower searches first. What query fan-outs are, why citations are won at that level, and how to turn them into a content plan.
When you ask an AI engine a question that needs fresh information, it rarely searches for your exact words. It breaks the question into several narrower searches, reads what comes back, and writes one answer from the lot. Those narrower searches are called query fan-outs.
The term isn't just industry jargon. Google uses it in its own documentation, where it says AI Overviews and AI Mode may use a query fan-out technique, issuing multiple related searches across subtopics and data sources. OpenAI describes ChatGPT search in similar terms, as rewriting a question into one or more targeted search queries. The details of how each engine does it aren't public, but the basic mechanism is documented.
That mechanism changes where the competition happens.
A worked example
Take a prompt like "what's the best project management tool for a small design agency?" The engine doesn't run that as one search. Behind the scenes it might look for things like:
- project management tools for creative agencies
- project management software pricing for small teams
- reviews of two or three specific tools it already associates with the category
- a comparison between two of them
- maybe a recent year added to keep results fresh
This list is illustrative, not captured from a real run. The exact searches vary by engine, by run, and by how the question is phrased. But the shape is typical. One question with a persona and a need becomes several searches, each aimed at a subtopic.
Why this matters for citations
The pages cited in the final answer come from the results of those fan-out searches. So you can rank well for the head query and still not get cited, because the engine never ran the head query as written. And a smaller site can get cited on a competitive prompt because it had the best page for one narrow fan-out, like pricing for small teams.
Fan-outs also tell you what the engine thinks matters. If every fan-out for a category includes "pricing" or "reviews", those are the criteria the answer will be built on. If fan-outs name specific competitors, the engine has already formed a shortlist, and you need to be in the comparison content for those names.
Step 1: collect the fan-outs behind your prompts
Start with your tracked prompt set. For each prompt, record the fan-out queries the engine ran. Don't read too much into a single run. Gather them over several runs and across engines, because they shift. What you want is the recurring pattern, not the exact wording of one search.
Step 2: cluster them
Put every fan-out from every prompt into one list and group similar ones. You'll usually see clusters forming around a few themes: pricing, integrations, specific use cases, named comparisons, reviews, how-to steps.
Count how many prompts each cluster appears under. A cluster that shows up beneath fifteen of your prompts is worth more than a cluster that appears under one, even if that one prompt looks important. Fixing it improves your odds across all fifteen.
Step 3: map clusters to pages
For each cluster, ask which page on your site answers it. There are three possible answers.
A page exists and answers it well. Check whether that page gets cited. If it doesn't, the problem is probably access or competition, not content.
A page exists but answers it badly. The pricing page doesn't say who each plan is for. The integrations page lists logos without saying what syncs. Rewrite so the answer to the fan-out sits in the first lines under a matching heading.
No page exists. This is the most common case, and it's where new content should come from. Fan-out clusters make better briefs than keywords, because you know an engine went looking for exactly this.
Step 4: watch for modifiers
Some fan-out patterns deserve their own attention.
Year modifiers, like a current year added to the query, suggest the engine is looking for fresh content. Undated pages are at a disadvantage there.
Site or platform modifiers, like searches aimed at Reddit or review platforms, tell you the engine wants third-party opinion. Your own page won't fill that slot. Offsite work will.
Competitor names in fan-outs tell you who's on the engine's shortlist. If you're not named in those searches, the comparison and alternatives content around those competitors is where to get in.
Step 5: re-check after you publish
After new pages go live and get crawled, run the same prompts and look at two things. Are your pages now cited for the fan-outs you targeted? And did the fan-outs themselves change? Engines adjust. A cluster you covered may shrink while a new one appears.
How we do this at scale
Collecting fan-outs by hand works for ten prompts. It falls apart at a few hundred across several engines and markets.
For client work we use prompt tracking in Promptwatch, which records the query fan-outs behind each tracked prompt's answer, so the collection step happens on every run without anyone copying searches into a spreadsheet. Prompts carry search volume and difficulty scores, which helps decide which clusters to tackle first, and topics and tags let us group prompts so the recurring fan-outs stand out. When a page we wrote for a cluster starts getting cited, the citation data sits right next to it.
The habit to build is simple. Before writing anything, look at what the engine searched for. Then write the page that would have been the best result for that search.