One question to an AI answer sets off many hidden searches. Fan-out queries are those background searches: related angles run at the same time, then merged into one reply. Semrush's guide to query fan-out says a page that covers more of those angles is more likely to be referenced.
What Are Fan-Out Queries
Semrush's guide defines the process as splitting a user query into multiple sub-queries, collecting information for each, and merging the relevant parts into a single response. It says the same step applies to ChatGPT and to Claude as well as to AI Mode, the conversational search Google runs inside Search. Google coined the term for AI Mode, and the guide says the industry uses it for the same step in those assistants.
The guide quotes Google's head of Search at the 2025 I/O keynote calling it "our query fan-out technique" and saying "it issues a multitude of queries simultaneously on your behalf."
How Fan-Out Works
Semrush's guide breaks that pass into five parts, and walks them with one prompt: "What's the best laptop for a college student who needs long battery life and does some video editing?"
- Analysis reads the intents in the prompt. This one holds three: college use, a long battery, and video editing.
- Decomposition issues the sub-queries. The guide's split is "best laptops for college students," "laptops with long battery life," and "video editing laptop requirements."
- Retrieval searches indexes, including a search engine's, for pages that address any of those sub-queries, and it may run further searches from what the first round returns.
- Scoring ranks what came back by how well it addresses each sub-query.
- Synthesis writes the answer from the highest-scoring material.
Semrush's guide says Google has published no exact count: a simple prompt might run a single sub-query, and a complex one, especially with deep research mode turned on, can fire off anywhere from 8 to more than 20.
Types of Fan-Out Queries
Semrush's guide lists six kinds: reformulation, implicit need, comparative, recency, contextual variation, and next step. Its examples, in that order, pair a prompt with a sub-query: "set up a Google Business Profile" with "create a Google Business listing"; "wheelchair-friendly tourist attractions" with "tourist attractions with elevator access"; "standing desk options" with "electric vs manual standing desks"; "f1 race dates" with "f1 race dates 2026"; "gyms with childcare" with "gyms with childcare in [user's city]"; "how to register a trademark" with "trademark registration lawyer."
The guide says these strings are synthetic and probabilistic: generated on the fly, different on the next run, and mostly carrying little or no search volume of their own.
Are Fan-Out Queries Long-Tail Keywords
Semrush's guide separates fan-out queries from long-tail keywords. A long-tail keyword is a phrase a person has typed into a search box. A fan-out query is generated so the answer can draw on a wider set of pages.
Why Fan-Out Matters
Context, intent, and subtopics do more of the matching than an exact keyword. A complex or vague question on AI Mode or on Perplexity gets a better answer when several angles can be pulled at once.
Semrush's guide describes a Google summary for "best phone for concert videos" that recommends Samsung and Apple and cites a page from Digital Camera World. The guide says none of the recommended brands' pages rank first. On Google, that summary is the summary box at the top of the results page. Counting whether an answer mentions your brand, on a fixed set of prompts, is its own cadence once the pages exist.
ChatGPT Fan-Out: What Gets Saved and What Drops
Semrush's guide teaches the five-part pass as a stable process to optimize for. Notes from people measuring ChatGPT show the fields used to watch it moving.
On 29 August 2026, a public report said OpenAI was streaming fan-out queries over SSE (a stream that pushes each update as it arrives) instead of storing them in the conversation. In some conversations the queries were gone, and for some the drop started after the 20th. DataForSEO, the data provider in use on that check, "may not have adapted yet," and the same note still read the drop as a real change in how ChatGPT exposes or stores those queries after the 20th.
A check of three brands that day described ChatGPT presence as tracking the model's fan-out queries more than the typed prompt. Order in the list showed when the model needed to widen the question and when it needed to verify a fact. The check flagged freshness patterns, and dates in the content needed to be used with care. Reputation ran past the main site to group sites and social profiles, any of which can be used in the answer. Consistent information across languages can favor a local page or another market, including on a project that stays in one country.
The three-brand check used the official API on model 5.5 with web search. The person who ran it has also seen DataForSEO return only one fan-out query, and says the official API's fields changed at model 5.6, so an extraction built for 5.5 has to be adapted. A separate note says the browser websocket on the ChatGPT site (the live connection the site itself uses) returns different information from the official API, and that this path is what someone at the keyboard sees. On model 5.6 instant, which that note calls the free mode, that note reports one fan-out query per prompt, with a guess that this saves resources, and a snippet in the style of a 202-character meta description.
A July 2026 note describes 5,460 ChatGPT answers pulled through DataForSEO's LLM Scraper, which types the question into the ChatGPT screen and reports the model value that comes back: gpt-5-5 on every row, and still gpt-5-5 eight days after model 5.6 had appeared. In that note, fan-out query fields were present on nearly every row, alongside zero Google star cards (the blocks with a star rating and a review count). Its re-pull on 10 September 2026, seven calls to the same endpoint with the same settings, produced six successes. Each success had an empty model field, star cards for 10 to 16 clinics, and an empty fan-out field. The note includes no notice from DataForSEO, so compare the raw fields across the two pulls ahead of the table of supported models.
A comment on 30 September 2026 says: "Retrieval Rank is what happens when ChatGPT makes fan-out queries and gets an ordered list of results back." Which search engine supplies a list like that belongs with how ChatGPT grounds an answer.
If a vendor field that held fan-out queries on the last pull is empty on this one, compare the raw response with the earlier pull before you treat the blank as a real absence.
How to Find Fan-Out Queries
Semrush's guide names tools that run a target prompt and surface sub-queries it might generate. Prompt Research in its AI Visibility Toolkit approximates them. Its enterprise analysis shows Google's actual fan-out queries at scale. Queryfanout.io, a free replicator, runs the expansion in the browser. The ChatGPT Query Fan-Out Tool, a Chrome extension, surfaces candidate follow-ups.
Strike the sub-queries a page of yours already answers. The same prompt can produce a different list on the next run, so follow the intents that keep returning.
How to Cover Each Sub-Intent
Semrush's guide turns the surviving list into pages. Build a pillar page (one broad overview of the core topic) and cluster pages for the subtopics, and give each fitting fan-out query a section that stands alone: a plain definition when the concept is new, descriptive headings, and a list or a table when the facts are list-shaped. The retrieval also reads the wider web, so add a directory listing, press on a new finding, and a useful reply where the angle is already discussed. Semrush's guide says to repeat that coverage pass every quarter.
On the laptop prompt, strike whichever of college use, long battery, and video editing a page already answers, and write a section that can stand alone for each intent that remains. Read the live Google results for each sub-query, and write the prompt and the date beside the pages listed, because the next run can split differently. qequals returns the live Google results for that sub-query, and the suggestion strings for a seed, on the call you make.
When you fetch those results through an API (a request your code sends), send one call at a time, pause before the next, skip a sub-query you have already saved, and leave a failed call blank.
Pick one prompt you want an answer to cite. Write the sub-intents it splits into, strike every one a page of yours already answers, and fill the gaps that remain.
