Definition
What is query fan-out?
Query fan-out is the process by which an AI search engine breaks one prompt into several related searches, runs them, and writes one answer from what they return. Google describes it as running several searches across subtopics and data sources at once. A buyer asks one question; the engine may look up pricing, reviews and integrations before it replies.
Google described the technique when it launched AI Mode in March 2025: related searches run at the same time across subtopics and data sources, with the results brought together into one answer[1]. Two months later it added that fan-out takes AI Mode deeper into the web than a traditional search, and that a Deep Search mode can issue hundreds of searches for a single question[2]. Its documentation for site owners says AI Overviews and AI Mode may both use fan-out, which surfaces a wider and more varied set of supporting links than a classic search[3].
Other engines search in a similar way and publish less about it. OpenAI says ChatGPT rewrites a prompt into targeted search queries and can follow up with narrower ones[4]. Perplexity says its Deep Research mode runs dozens of searches across hundreds of sources[5].
For a brand, the sub-questions matter more than the prompt, because they decide which pages the engine reads and so which brands it can name. On “best project management tool for creative agencies”, an engine might also check which tools have client approval workflows, what the software costs per seat and whether it connects to design and proofing apps. A brand that no page connects to client approvals is unlikely to be named in that part of the answer.
Comparison
Fan-outs, People Also Ask and keywords come from 3 different places
Query fan-outs come from the AI engine, People Also Ask questions come from Google's results page, and keywords come from what searchers type. A fan-out shows what an engine checks before it answers. People Also Ask shows questions related to a search. A keyword shows how often a phrase is typed. Each one feeds a different part of a content plan.
| Concept | Who produces it | Where it shows | What it tells you | Use it for |
|---|---|---|---|---|
| Query fan-out | The AI engine, while it builds an answer | Behind the answer, as searches the engine runs | What the engine checks before naming a brand | Briefing pages that AI engines retrieve |
| People Also Ask | Google, as related questions | A box on the results page | Questions related to the search | FAQ entries and supporting sections |
| Keyword | Searchers, measured by volume | Keyword research data | How often a phrase is searched | Ranking topics by demand |
Google describes People Also Ask as a group of questions related to the original search, each of which expands into a short answer from a page, and suggests site owners use those related questions to choose topics[6]. In Search Console, a People Also Ask item only earns an impression when someone expands it[7]. The box is written for the person reading the results page.
A fan-out has a different reader: the engine itself. Its sub-questions are written to retrieve pages, so they read like searches, often narrower than the prompt. In the sample data below, one fan-out of the agency prompt compares two brands head to head, “loopwork vs cadence for creative teams”, a search the buyer never typed.
Keywords still size demand. A phrase with no search volume can matter as a fan-out, and a high-volume keyword can be absent from every fan-out of your prompts. Use keywords to rank topics by demand, People Also Ask to find the questions searchers raise, and fan-outs to see what engines check before they recommend anyone.
Example
One sample prompt produced 8 recurring fan-outs, and the brand was named behind 3 of them
Query fan-outs for one prompt can be ranked by how often engines generate them. In this sample, the prompt about project management tools for creative agencies produced eight recurring sub-questions, led by client approval workflows and price per seat. Loopwork was named in the answers behind three of them, which marks the topics where engines already connect it to the question.
| Fan-out | Occurrences | Loopwork named |
|---|---|---|
| which project management tools have client approval workflows | 284 | Yes |
| how much does project management software cost per seat | 231 | Yes |
| best pm tool for agencies with under 20 people | 198 | No |
| does it integrate with design and proofing tools | 176 | Yes |
| project management software with built-in time tracking | 154 | No |
| loopwork vs cadence for creative teams | 141 | No |
| can clients comment without a paid seat | 96 | No |
| which tools support retainer and utilisation reporting | 74 | No |
Read the table from the top. The two most frequent fan-outs, client approvals and price per seat, are covered: when engines check those points, they find Loopwork. The next one, tools for agencies under 20 people, is the first gap. The comparison with Cadence is the sharpest signal of all, since engines run it and the answers that follow leave Loopwork out.
For Loopwork's content team, the first two fan-outs call for upkeep and the next ones for new pages: a section for small agencies, a comparison with Cadence written from Loopwork's side, a note on guest access for clients.
Occurrences matter more than any single fan-out. A sub-question that comes back hundreds of times across runs and engines is part of how engines approach the category, while one that appears once is noise. The count also sets the order of work, since a gap on a fan-out generated a few dozen times can wait.
Method
A fan-out list becomes a content brief in 4 steps
Query fan-outs become a content brief when they are ranked by occurrence, checked against the pages you already have, and turned into headings and FAQ entries for the gaps. The first three steps need a fan-out list and a spreadsheet. The fourth keeps the brief current as engines change the questions they ask.
-
Step 1
Rank the fan-outs by occurrence
Group near-duplicates, then sort the fan-outs by how many answers generated each one. Work from the top, where a sub-question recurs across runs and engines.
-
Step 2
Check your coverage
For each fan-out, find the page on your site that answers it in its first lines. Mark the fan-outs with no page, and the ones where the answers leave your brand out.
-
Step 3
Turn the gaps into headings and FAQ entries
Write each uncovered fan-out as an H2 or an FAQ question, in the engine's wording, and answer it in the first 40 to 80 words below. Add the fan-outs that recur most to your tracked prompts.
-
Step 4
Track the fan-outs over time
Run the same prompts again at a fixed interval and compare the lists, since engines change the questions they check. The Fan-Out API returns the fan-outs of a prompt set ranked by occurrence, with a flag for brand presence. See the Fan-Out API.