Query fan-out – how AI search engines expand your questions and what it means for your content

Definition

Query fan-out describes how search engines and AI systems automatically expand a search query into several related queries to better understand the user's intent.

What is query fan-out?

Query fan-out is the mechanism by which AI-driven search systems – such as Google AI Mode, ChatGPT and Perplexity – split a single user query into several parallel sub-queries, retrieve information for each one and then weave the results together into one coherent answer.

Elizabeth Reid, Google's Head of Search, explains it like this in a blog post: "Under the hood, AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf. This enables Search to dive deeper into the web than a traditional search on Google, helping you discover even more of what the web has to offer and find incredible, hyper-relevant content that matches your question."

This is not just a technical detail – it is a fundamental change in how search works. Traditional search: one query returns one set of pages. Query fan-out: one query triggers 5–20 parallel searches, and the best information from the best sources is woven together into one answer.

How does it work technically?

Query fan-out is part of the larger RAG (Retrieval Augmented Generation) flow. Here is the process step by step:

1. The query is analysed. The AI system assesses whether the query is complex enough to justify a fan-out. Simple factual questions rarely need it. Complex, comparative or exploratory questions almost always trigger it.

2. The query is broken down. An LLM generates several sub-queries that cover different facets of the original query: definitions, comparisons, use cases, pros and cons, concrete examples.

3. Information is retrieved in parallel. All the sub-queries are searched at the same time – not one after another. This is crucial for speed. Google's system also draws on its own data sources, such as the Shopping Graph and the Knowledge Graph.

4. Chunks are assessed and weighted. Text blocks that turn up as relevant for several of the sub-queries get a higher score. This is called Reciprocal Rank Fusion – a method that rewards content that is consistently relevant across many variants of a query, not just optimised for a single keyword.

5. The answer is generated. The LLM weaves the retrieved chunks together into one coherent answer and lists the sources it used.

Google's patent US20240289407A1 offers a technical look at how the system creates sub-queries based on "inferred themes" – thematic interpretations of what the user actually needs to know, not just what they literally asked about.

How does this change what AI search queries look like?

An analysis by iPullRank (December 2025) showed that queries in AI search contain 70–80 words on average, compared with 3–4 words in traditional search. As a result, users get a more conversational, context-rich answer – and the system needs a broader base of material to generate it.

One consequence: ranking for a single keyword matters less than it used to. What matters most is that your content shows up as a relevant source for many of the sub-queries generated from a topic – whatever the exact wording.

Three practical consequences for your content strategy

1. A page has to cover the whole set of questions, not just the main one. If you have a page about "project management tools", it is not enough to define what they are. The page also needs to cover who they suit, how to choose the right one, what they cost, how the options differ and the mistakes people commonly make. Those are the sub-queries that query fan-out generates from the overarching question.

2. Topic clusters are no longer just good SEO – they are a precondition for AI visibility. When an AI system "fans out" a question in your subject area and finds interlinked, relevant pages with in-depth answers to many of the sub-queries, the chances of your pages being chosen as a source rise dramatically. Logically, this is nothing new – it is what long-term, user-friendly SEO has been about all along, with a few more unknowns (niche questions) added.

3. Semantic chunking and clear blocks of text are crucial. Because the AI system retrieves and assesses content in chunks – semantic blocks of text, typically 200–500 tokens long – the structure of your writing has a direct effect. Content written in long, rambling blocks without clear semantic boundaries is harder for the system to use. Content where each paragraph answers a specific question is better placed to be retrieved for the right sub-query.

From keywords to topic-wide coverage

One SEO tactic has been to create a page for every keyword. Query fan-out makes that advice outdated.

What decides whether your content is cited in an AI answer is not whether you rank for that exact keyword. It is whether your content is deep, broad and clear enough to be relevant to many of the sub-queries the AI system automatically generates. Building topical authority around a subject area – instead of chasing individual terms – is therefore the right strategy for AI search too.

Surfer SEO's analysis puts this in perspective: of all the sub-queries generated in AI search, only around 27% are stable across repeated searches. So it is practically impossible to optimise for every individual sub-query. What does work is building a website where the depth and breadth of the content mean you naturally capture a large share of them – whatever the exact wording.

A future-proof strategy in a volatile AI world

Finally, we have to accept that search algorithms are no longer updated quarterly; they evolve as the models get smarter. The latest data from Writesonic shows a dramatic change: between versions GPT 5.3 and GPT 5.4 there was 0% overlap in the sources cited. When GPT 5.4 quadrupled its fan-outs and started using site: searches to fact-check brands directly on their own domains, yesterday's winners were wiped out overnight.

This underlines that what matters most is not "tricking" a system with temporary hacks, but building topical authority so deep and well structured that you become the natural source when the AI does its deep dive. In a world where 37% of searches are now aimed specifically at individual domains for verification, your own website's technical clarity and semantic richness are your only real defence against the algorithms' constant shifts.