# Query fan-out: what it is and how it affects your store

> What query fan-out is, how AI Overviews, AI Mode and ChatGPT use it, and what it means for your product pages.

URL: https://clione.ai/glossary/query-fan-out/
Lang: en

_Fan-out_ is an engineering word: one input spread across many outputs. In [AI search](/glossary/ai-search/), the input is your question and the outputs are the searches the system launches on its own to answer it. For a store, the consequence is concrete: your [product page](/glossary/product-page/) can end up cited in the answer to a question it doesn't fully answer, because it answers one part of it well, and it can be left out even if it ranks for the original question.

## How it works

Google defines it in its guide to generative AI features: a set of simultaneous, related queries that the model generates to ask for more information and bring back more results to answer the person's question. It gives an example: for "how to fix a lawn full of weeds", the fan-out queries might be "best weed killers for lawns", "remove weeds without chemicals" and "how to prevent weeds in a lawn".[^google-ai-guide]

Both of Search's AI features use it. [AI Overviews](/glossary/ai-overviews/) and [AI Mode](/glossary/google-ai-mode/) can run several related searches across subtopics and data sources to build the answer, and while they generate it their models identify more pages that support it, so **they can show a wider and more varied range of links** than a classic search.[^google-ai-features] When it introduced AI Mode in May 2025, Google put it this way: under the hood, AI Mode breaks your question into subtopics and issues **a multitude of queries at once on your behalf**. Its deep research mode, Deep Search, takes the idea further and can run hundreds of searches to write a cited report.[^google-io-2025]

Fan-out queries retrieve pages from the index, and the model writes from them. That second part, retrieving and then generating, is what is called [RAG](/glossary/rag/) or [grounding](/glossary/grounding/), and Google does it with its usual ranking systems.[^google-ai-guide] Fan-out decides what gets searched; the rest of the system decides what gets read and what gets cited.

### Outside Google

ChatGPT doesn't use the name, but it describes the same move. When it searches through outside providers, it usually **rewrites your question into one or more focused queries** and, after reviewing the first results, can send more specific ones. Its example: for "what's new in CCR8 drugs for cancer?", the first query might be "CCR8 immunotherapy drug development 2025", and the next a more specific one about a particular drug and a conference. It can add your approximate location and, with memory on, what it remembers about you.[^openai-help-search] That way "good restaurants near me" can become "top restaurants San Francisco".[^openai-help-search]

Bing shows site owners a reflection of those queries. Its AI Performance report, in [Bing Webmaster Tools](/glossary/bing-webmaster-tools/), shows the **grounding queries**: the key phrases the AI used when retrieving the content it ended up citing. It is a sample, not the full record.[^bing-ai-performance]

### What not to do with this

The temptation is to manufacture a page for every possible sub-query. Google rules it out in the same guide: its systems understand synonyms and the general meaning of what is searched, and **creating separate content for each variant, fan-out queries included, mainly to manipulate AI answers**, breaks its policy against [scaled content abuse](/glossary/scaled-content-abuse/).[^google-ai-guide] The useful move is the opposite: make the page you already have answer each part of the question well.

## In a store

A **fictional, teaching example**. The sub-queries in this table are a hypothesis written to explain the idea: they are not real queries from Google or any assistant, none of which publish theirs. The question: "I'm looking for a camera backpack I can take as a carry-on that will hold up in the rain."

| Hypothetical sub-query                     | What page would answer it       | What fact would need to be on your page                 |
| ------------------------------------------ | ------------------------------- | ------------------------------------------------------- |
| carry-on luggage size limits for airlines  | The airlines' pages             | The backpack's outside dimensions, in text              |
| waterproof camera backpack                 | Product pages and roundups      | What protection it has: fabric, zippers, rain cover     |
| how many lenses fit in a 20-liter backpack | Guides and reviews              | The interior layout: compartments and what fits in each |
| lightweight camera backpack                | Product pages that state weight | The empty weight, with its unit                         |

A page that only says "ideal for travel" answers none of the four. One that gives the dimensions, the weight and the rain protection **can be the source for two or three parts of the answer**, even if it never shows up for the whole question.


That you can find out which fan-out queries Google runs for a question. Google describes the technique and gives an example,[^google-ai-guide] but it doesn't publish the queries it generates, and its [generative AI report](/glossary/generative-ai-performance-report/) in [Search Console](/glossary/google-search-console/) counts impressions by page, country, device and date, not sub-queries.[^gsc-genai-report] A tool that shows you "Google's fan-out queries" for your topic is showing you its own estimate.


## How to check it

1. **Write your customer's full question and break it up yourself** into the three or four doubts that make it up. Label the list a hypothesis, because it is one.
2. **Find the fact on your product page that answers each doubt**, in text and with its unit. If it is missing, add it to the page you already have instead of creating another.[^google-ai-guide]
3. **Ask the full question in AI Mode** and note which pages it links and which part of the answer each one supports, with the date.[^google-ai-features]
4. **If your site is in Bing Webmaster Tools, open AI Performance** and read the grounding queries for your cited pages.[^bing-ai-performance]
5. **Check the Search Console [generative AI performance report](/glossary/generative-ai-performance-report/) for which pages earn impressions** in AI Overviews and AI Mode. If a product page shows up that you didn't expect, it probably answers part of some question.[^gsc-genai-report]

## Related concept: RAG

The two go together and are often confused. _Query fan-out_ is the step of **deciding what to search**: multiplying one question into several queries.[^google-ai-guide] RAG is the whole scheme of **searching and then writing** from what was found; fan-out is one way to enrich its first half. A system can use RAG with a single query and no fan-out at all, and fan-out is useless without a model that reads what comes back.

To keep going, the [Google AI Mode](/glossary/google-ai-mode/) entry explains where this technique is most visible, and the [AI search reading path](/learn/ai-search/) puts the other terms in order.