Type a question into AI Mode and what actually happens behind the scenes isn't one search. It's several, running at once, each one covering a piece of what you asked, with the results stitched back together into a single answer. Google has a name for this: query fan-out. It's one of the few mechanics of modern AI search that a major vendor has described in its own words rather than leaving the SEO industry to reverse-engineer it from behavior, and that makes it worth understanding precisely, not approximately.

This post separates what's actually documented about query fan-out and similar techniques from what the industry has inferred by watching these systems behave, because a lot of the content floating around on this topic blurs the two.

What Google says, in its own words

The clearest statement comes from Google's own Search blog. In a post dated May 20, 2025, Google describes AI Mode's retrieval approach directly: "AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf" (Google Search blog, May 20, 2025). That's the whole mechanism in one sentence: decompose the question, issue several searches in parallel rather than one at a time, then reason across whatever comes back.

The same post describes a more aggressive version used for Deep Search, where fan-out gets "taken to the next level," capable of issuing "hundreds of searches," reasoning across the disparate results, and producing "an expert-level fully-cited report in just minutes." The core idea scales: a quick AI Mode answer might fan out into a handful of queries, while a deep research-style request can fan out into hundreds.

Google introduced "query fan-out" publicly around its May 2025 I/O cycle, as AI Mode was expanding out of early testing and into the main Search product. It isn't a one-off research paper term; Google has continued referencing the technique in its own communications about how AI Mode and AI Overviews retrieve information since.

What Google has not published, as far as we could verify: a technical breakdown of exactly how many sub-queries get generated for a given type of question, what model decides the decomposition, or a public algorithm for how sub-answers get weighted and merged into the final response. Those specifics sit inside Google's systems. Anything you read claiming a precise number ("AI Mode always generates exactly N sub-queries") is an inference from observed behavior, not a documented fact, and you should treat it that way.

Google has since given a tighter, more formal definition of the term in its own developer documentation, in the AI features optimization guide last updated July 10, 2026: query fan-out is "a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results" (Google Search Central, AI features and your website). That same guide is worth reading directly if you're responsible for a site's AI visibility, because it makes two points that cut against some of the advice circulating in the SEO industry: Google states plainly that "structured data isn't required for generative AI search, and there's no special schema.org markup you need to add," and it specifically warns site owners off manipulative tactics, naming artificial content "chunking" and publishing an llms.txt file as examples of "hacks" it doesn't endorse. Keep both of those in mind as you read the structuring advice later in this post: organizing a page around clear sub-answers is good writing that happens to also help retrieval, not a markup trick you're applying on top of otherwise thin content.

How this differs from a single query with better ranking

It's worth being precise about why fan-out is a different mechanism from just "Google got better at understanding one search." A traditional query, even a well-handled one, retrieves one ranked set of results for one interpretation of your question. Query fan-out explicitly produces multiple separate searches, each one covering a different sub-question or facet, then combines what comes back. If you ask something like "is a heat pump worth it for a house in a cold climate," a fanned-out system might separately search for cold-climate heat pump efficiency data, installation cost ranges, available tax credits, and common failure points in low temperatures, then synthesize an answer that touches all four, even though your original question never explicitly asked about all four.

This matters for content planning because it changes the unit of competition. You're not just competing to be the best answer to the literal query someone typed. You're competing to be the best answer to each sub-question the system decides to generate on its own, for a query you may never see in your own analytics.

How other systems generate sub-queries: what's actually documented

Google isn't the only system breaking one prompt into several searches. The mechanics differ by vendor, and the amount of public documentation differs even more, so here's what's confirmed for each versus what's reasonable inference.

Gemini's grounding with Google Search

Gemini's API documentation for grounding with Google Search confirms that a single model call can execute more than one search. The official example given in the docs: a model answering a question about "UEFA Euro 2024 winner" might separately search for the winner and for "Spain vs England Euro 2024 final score" within the same API call, and Google's documentation is explicit that each of those counts as a separate billable search (ai.google.dev, Gemini API grounding documentation). The response structure documented on that page includes a search-call object containing the list of queries the model actually executed, plus a separate result object and inline citation annotations tying specific parts of the answer back to specific sources.

That's a confirmed, vendor-documented fact: Gemini can and does run multiple distinct search queries per request, and it exposes which queries it ran in the API response if you're building against it directly. What isn't documented in that same source: the decision logic for how many queries get generated for a given prompt, or whether Gemini's internal decomposition approach is the same technique Google calls "query fan-out" in AI Mode, versus a related but separate implementation. Treat those two as probably-connected, not confirmed-identical.

ChatGPT's web search tool

OpenAI's developer documentation for its web search tool confirms similar multi-query behavior without the same level of detail Google provides. The docs note that a search action "usually (but not always) includes the search queries" that were run, which confirms more than one query can happen per search action. For the agentic, reasoning-model version of search, OpenAI's documentation states the model "can perform web searches as part of its chain of thought, analyze results, and decide whether to keep searching," meaning it can run additional searches iteratively based on what the first round returned, not just in one parallel batch (developers.openai.com, web search tool guide).

What's confirmed here: multiple queries per search action happen, and iterative, decision-based follow-up searches happen for reasoning models. What's not documented: the specific method OpenAI uses to decompose a prompt into queries, whether it resembles Google's fan-out approach structurally, or any count or pattern for how many sub-queries a typical prompt generates. If you see a specific claim about "ChatGPT always runs 3 to 5 searches per question," that's an estimate from observed behavior, not something OpenAI has published.

The general pattern across RAG-style systems

Setting vendor specifics aside, breaking a complex prompt into multiple retrieval queries is a well-established technique in retrieval-augmented generation generally, often described in the broader AI engineering literature as query decomposition or multi-query retrieval. The common thread across Google's fan-out, Gemini's multi-search calls, and ChatGPT's iterative search is the same underlying idea: a single complex question rarely maps cleanly to a single optimal search query, so the system generates several narrower ones and reconciles the results. That's a reasonable generalization to draw across systems. The specific implementation details inside each vendor's product remain mostly undocumented outside what's quoted above.

Why this changes how you should think about content coverage

If a system is going to split your reader's question into several searches behind the scenes, a page that answers only the headline question and ignores the adjacent sub-questions is competing at a disadvantage it can't see. The practical shift is from "does this page rank for the target keyword" to "does this page, or this page plus its closest siblings, cover every sub-question a fanned-out search would plausibly generate for this topic."

This is also a reasonable, specific explanation for something a lot of SEOs have noticed anecdotally in 2025 and 2026: comprehensive, well-structured long-form content that covers a topic from multiple angles tends to show up as a cited source in AI Mode and AI Overview answers more often than narrow, single-angle pages targeting the same head keyword, even when the narrow page would have outranked it in classic ten-blue-links search. Fan-out is a plausible mechanical reason why: your page is more likely to satisfy whichever sub-query the system happens to generate, because it already covers more of the territory those sub-queries would touch. We're stating that as an inference consistent with the documented mechanism, not as something Google has confirmed is the exact cause of any specific ranking pattern you've seen.

Passage-level relevance matters more under this model too. A system assembling an answer from several separate searches is likely pulling specific passages or sections to satisfy specific sub-queries, not necessarily crediting your page as a whole for a single broad match. Clear section headers, one well-contained answer per section, and avoiding the practice of burying a direct answer three paragraphs into unrelated context all help a passage get matched to the sub-query it's actually answering.

How to simulate fan-out for your own topics

You don't need access to Google's internals to approximate what a fan-out system would generate for your topic. Two approaches work, and they're complementary rather than competing.

Manual decomposition. Take your target topic and write out, by hand, every sub-question a genuinely curious person would ask next if a human expert were in the room. For a topic like "should I switch to a heat pump," that list might include climate-specific efficiency, upfront cost versus a furnace, available rebates by region, maintenance requirements, expected lifespan, and noise level. Cross-reference that list against what People Also Ask and the "related searches" box actually show for your target query today, since both of those are Google surfacing real related-query data, even outside the AI Mode context specifically.

LLM-assisted simulation. Prompt a model directly to do the decomposition for you: ask it something like "if you were researching [topic] thoroughly, list every distinct sub-question you'd need answered to give a complete answer, grouped by theme." This won't replicate the fan-out algorithm itself, since it isn't the same internal function running inside AI Mode, but it's a reasonable proxy for the kind of sub-question space a thorough system is likely to touch, especially when you run the same prompt across two or three different models and look at where their lists overlap. Overlap across models is a weak signal that a sub-question is central to the topic rather than idiosyncratic to one model's training.

Neither method tells you Google's actual fan-out queries for a specific search. Nobody outside Google has that. Both get you a reasonable approximation of the sub-question territory you need to cover to be a strong candidate for whichever sub-queries actually get generated.

Mapping sub-queries to sections

Once you have a list of sub-questions, the structural fix is straightforward and doesn't require a content rewrite philosophy, just discipline in how you organize what you were already planning to write.

Give each major sub-question its own heading, phrased close to how a person would actually ask it, not in marketing language. Answer that specific sub-question directly in the first sentence or two under that heading, then add supporting detail and nuance after the direct answer, not before it. This ordering matters more under a fan-out model than it did under classic SEO, because a system extracting a passage to satisfy a sub-query is more likely to grab a clean, early, direct answer than to synthesize one out of a paragraph that opens with throat-clearing and gets to the point in sentence four.

Avoid cramming every sub-question into one undifferentiated wall of text. A page that answers six sub-questions but never marks where one answer ends and the next begins is harder for both a human skimmer and a retrieval system to parse into distinct, matchable passages. Structure is doing real retrieval work now, not just readability work, and that's different from the "chunking hacks" Google specifically disclaims in the guidance quoted above: writing genuinely separate, well-labeled answers because the content calls for it is not the same thing as artificially slicing thin content into fragments to game a retrieval system, and Google's own language draws that line deliberately. For the broader technical groundwork this depends on (crawlability, clean markup, a server that doesn't choke on a crawler), see the technical SEO checklist that actually moves rankings, and for how heading structure and markup specifically help machines parse your content correctly, semantic HTML for SEO covers the implementation details this post doesn't.

If you're earning citations already and want to know whether it's translating into anything measurable, that's a separate but related question covered in how to measure AI visibility: the KPIs that actually matter, and if the goal is specifically getting named or linked inside these AI-generated answers in the first place, how to get cited by ChatGPT and Google AI Overviews covers that groundwork directly.

Fan-out is also why "prompt volume" is a fuzzy number; see how to estimate what people actually ask AI.

FAQ

Is query fan-out the same thing as Google's "Deep Search"?

No, they're related but not identical. Fan-out is the underlying technique: breaking a question into multiple simultaneous searches. Deep Search is a specific, more intensive application of that technique inside AI Mode, capable of issuing hundreds of queries for a single complex research request rather than the smaller number used for a standard AI Mode answer.

Can I see which sub-queries Google generated for my search?

Not currently, for a consumer using Google Search or AI Mode directly. Developers using Gemini's API with grounding can see the list of search queries their own API call executed, because Google documents that as part of the response structure, but that's visibility into your own API usage, not into AI Mode's consumer-facing fan-out for a given search.

Does writing FAQ sections on my page help with fan-out?

It can, for the same reason it helped with featured snippets: a clearly labeled question followed by a direct, self-contained answer is an easy unit for a retrieval system to match against a generated sub-query. It's not a guarantee, and stuffing a page with dozens of loosely related FAQ entries to game this is more likely to dilute relevance than help it.

Does ChatGPT use the exact same fan-out technique as Google?

Not confirmed either way. OpenAI's own documentation confirms its web search tool can run multiple queries per search action and additional iterative searches for reasoning models, but OpenAI hasn't published a named technique or detailed decomposition method comparable to Google's public description of query fan-out. Treat them as similar in effect, not proven to be the same mechanism.

Should I target long-tail keywords differently now that fan-out exists?

The underlying research work (understanding what your audience actually wants to know) hasn't changed. What's worth adjusting is organizing that research into clearly separated, directly-answered sections rather than one narrow page per long-tail phrase, since a single well-structured page covering a cluster of related sub-questions is better positioned to match whatever specific sub-query a fan-out system happens to generate.

The thing to take from all this isn't a new tactic so much as a reason the old advice to "cover a topic thoroughly" finally has a mechanical explanation behind it. Build the page a genuinely thorough human expert would write, structure it so each distinct answer stands on its own, and you're building for fan-out whether or not you ever think about the term again.