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On September 15, Google Research published a model that writes ten search directions in 0.07 seconds. At a batch size of eight, the older method needed 1.46 seconds for the same job. That gap sounds like plumbing. It shows where AI search is heading. Query fan-out splits one prompt into many background searches. Google’s paper makes that step cheap enough to run at scale.
Most brands still track one keyword and one citation count. I think that’s the wrong unit, because query fan-out sits behind every AI answer your buyers read. Those answers draw on sub-queries you probably never targeted. I’ve used Semrush for years, so I tested its AI Visibility Toolkit against this problem. It helps. Still, it has limits, and I list them below.
Definition. Query fan-out is the step where an AI search system splits one prompt into sub-queries. It runs them at the same time. Then it merges the retrieved content into one answer.
What does query fan-out mean for a brand that tracks keywords?
It means your keyword is only the entry point. For context, Google introduced the term with AI Mode in 2025. Its Search documentation now lists query fan-out among the techniques behind AI Overviews and AI Mode, next to retrieval-augmented generation.
Take the prompt “Salesforce vs. HubSpot vs. Pipedrive.” With query fan-out, I would expect hidden searches on pricing, integrations, security and API limits. That list is illustrative, because nobody outside the vendor sees the real one.
A design example makes it concrete. Say someone asks for the best variable font for a brand identity. The system may also check web licensing costs, file sizes and free alternatives. Your type review might rank for the first phrase. Yet it may say nothing about the rest. Could your best page answer the licensing question on its own?
Prompts also keep growing. For instance, Alphabet reported in February 2026 that AI Mode queries run about three times longer than traditional searches. Longer prompts carry more intents, so I’d expect more sub-queries per answer. Semrush estimates that a complex prompt triggers eight to 20 or more background searches. Google hasn’t published exact counts, so treat that range as an estimate.
What did Google’s R4T-Diffusion paper actually confirm?
What Google published
R4T stands for Retrieve-for-Train. First, Google trained a fan-out language model with reinforcement learning. The reward weights groundedness at 0.6 and splits the remaining 0.4 evenly between diversity and alignment. A good query fan-out must land near content that actually exists. It must also stay tied to the original prompt and avoid repeating itself.
Next, Google distilled that behavior into a diffusion model with 53.9 million parameters. The small model creates all retrieval directions in one pass instead of writing sub-queries word by word. Google reports a 12x to 20x speedup. At a batch size of 1,024, the older method needed nearly 50 seconds. R4T needed 4.21 seconds.
What it does not prove
I want to be careful here. Google tested R4T on Polyvore and a music playlist dataset, not on web search. I found no confirmation that it runs inside AI Mode or AI Overviews today. So the paper doesn’t prove that your pricing page feeds a Gemini answer.
It does confirm the direction. Google invested in making query fan-out faster and cheaper. It also trained the model to vary its searches. Besides that, the paper hints at a measurement problem. R4T generates embeddings, not readable sub-queries. If production systems copy that design, the sub-queries may never exist as text. Tools would then estimate intent clusters instead of listing exact phrases. That’s my reading, not Google’s claim.
Why do citation counts mislead leadership?
A citation count measures the last step of a long pipeline. For example, one 2026 analysis examined 548,534 pages that ChatGPT retrieved across 15,000 prompts. Only 15 percent of them became citations. However, a separate large analysis reported a citation share closer to half. The methods differ, but both agree that retrieval and citation are different events.
That same 15,000-prompt analysis found that ChatGPT ran two or more query fan-out searches on 89.6 percent of prompts. Roughly a third of cited pages (32.9 percent) surfaced only in Google results for a fan-out query. They never appeared for the original prompt. Google rankings don’t explain everything, though. A separate analysis found that only about 17 percent of retrieved pages appeared in Google’s results for the same queries.
I call the first gap the Retrieval-Citation Gap. It’s the share of pages an AI system pulls in and then drops. That label is mine, not an industry term. The second gap has no name yet, so I’ll coin one. A Silent Loss is a sub-query that never retrieves your page. It leaves no alert and no line in any report.
Leadership reads citation counts as reach. But a count says nothing about the sub-queries that never retrieved you. I’d demote citation count from headline KPI to appendix. Budgets built on it justify spending with a number that explains little about what retrieved you.
How I tested query fan-out coverage with the AI Visibility Toolkit
I gave the AI Visibility Toolkit one job. It had to show me which query fan-out sub-queries my content misses. My test topic came from my own beat: variable fonts for brand identities.
Prompt Research finds candidate sub-prompts
I typed the topic into Prompt Research and clicked Analyze. The report lists related topics. Then each topic shows an AI volume and a difficulty score from 0 to 100 percent. An intent split sits beside both. The intents cover informational, navigational, commercial, transactional, and task prompts. Clicking a topic opens its sub-prompts, which AI systems may also use as fan-out queries.
Semrush says it captures prompt responses from real requests, not through LLM APIs. That matters to me. Lab prompts rarely match how readers phrase things.
The weak spot is demand data. AI volume sits at the topic level, not the prompt level. Therefore, you can’t rank individual sub-prompts by popularity.
The Questions report maps what your domain already answers
Next, I entered my domain into the Questions report. Topic Distribution shows which topics your domain already attracts. Intent by Topic shows whether those topics lean toward research, comparison, or support. Together they gave me a first draft of my coverage map.
Prompt Tracking and Narrative Drivers close the loop
I then sent the strongest prompts to Prompt Tracking, which updates daily. Narrative Drivers shows your share of voice for non-branded queries and the sources behind it. That’s the view I’d show a skeptical executive.
Where the toolkit falls short
Be clear about what it does. It approximates sub-queries. Google’s actual query fan-out for your prompt stays out of view. Semrush reserves that for Query Fan-Out Analysis in its Enterprise AI Optimization product.
Prompt Tracking also has plan caps, so check them before you plan 200 prompts. Besides, no tool proves cause. Semrush ran its own test on four updated articles. Citations rose from two to about five within a month and peaked at nine before settling. Meanwhile, brand mentions dipped. That’s a small sample, and Semrush calls it early evidence.
Google adds its own warning, too. No third-party tool has access to its internal ranking or AI systems. For first-party data, the Generative AI performance report in Search Console remains the check I trust.
A practical method to measure query fan-out coverage
To measure query fan-out coverage, I use three editorial constructs. They’re my labels, not industry standards.
The Fan-Out Surface
The Fan-Out Surface is the full set of sub-intents around one prompt that can retrieve a page. Think of it as an area your content must cover. A keyword list can’t describe it.
The Repeat-Run Rule
Sub-queries change between runs. In fact, one study of Gemini across ten runs found that only about 27 percent of fan-out keywords stayed consistent. Also, 66 percent appeared only once. So I sample each prompt at least five times. I count a sub-intent only when it appears in two or more runs.
The Coverage Ratio
The Coverage Ratio equals the stable sub-intents your pages answer, divided by all stable sub-intents. A ratio of 0.4 means you answer four of ten. Every sub-intent you miss is a Silent Loss. Besides that, I score a page only when a section answers the sub-intent directly. My own threshold for a rewrite is 0.5.
How do you cover sub-queries without publishing thin pages?
Google’s guidance on generative AI search warns against separate pages for every query fan-out variation. It says doing so mainly to manipulate AI responses violates its scaled content abuse policy. The same guidance also says you needn’t chase every long-tail phrase.
So I read query fan-out coverage as depth, not volume. One strong page can answer five sub-intents in five marked sections. A page on a variable font could cover licensing cost, file size, OpenType features, fallback fonts and free alternatives. Each section should open with the direct answer.
Comparison prompts deserve extra attention. An April 2026 analysis of 5.47 million queries across 53 brands found AI Overviews on 95.4 percent of comparison-style queries. Vendor-versus-vendor prompts probably fan out into pricing, integration and security questions. So a comparison page should answer each one.
Google also asks for a point of view others can’t copy. First-hand testing qualifies, but a summary of other pages doesn’t. That’s why I describe what I clicked, not what the brochure says.
Here’s the audit I run each quarter.
- Pick ten decision-stage prompts for one topic.
- Run each prompt five times and log the sub-intents.
- Keep the sub-intents that appear in two or more runs.
- Map each one to a page and a section.
- Rewrite the pages that score below 0.5.
What should a query fan-out report show leadership?
I’d replace the citation slide with two numbers. Show the Coverage Ratio per topic. Add the Silent Loss count for your ten money prompts. Then use non-branded share of voice from Narrative Drivers to check whether visibility follows.
Executives will accept this for a simple reason. A Coverage Ratio moves when you publish, whereas a citation count moves when a model changes its mind.
Run-to-run variance also belongs in the report. Show a range, not a point. A ratio of 0.4 to 0.5 is honest, but a flat 0.45 isn’t.
Query fan-out cheat sheet
| Term | Definition | Why it matters |
|---|---|---|
| Query fan-out | One prompt splits into several sub-queries that run at the same time | Your keyword is the entry point, not the full target |
| R4T-Diffusion | Google’s 53.9-million-parameter model that creates ten retrieval directions in one pass | Shows the direction of research; production use is unconfirmed |
| Fan-Out Surface | The sub-intents around one prompt that can retrieve a page | Defines what your content must cover |
| Retrieval-Citation Gap | The share of retrieved pages an AI system never cites | One analysis found only 15 percent cited |
| Silent Loss | A sub-query that never retrieves your page | Leaves no alert in standard reports |
| Repeat-Run Rule | Sample each prompt five times, keep sub-intents seen in two or more runs | Filters noise; only about 27 percent of fan-outs stayed stable in one study |
| Coverage Ratio | Stable sub-intents answered, divided by all stable sub-intents | Gives leadership a number that moves when you publish |
| Prompt Research | Semrush report with topics, AI volume, difficulty, intent and sub-prompts | Finds candidate sub-intents; volume sits at topic level |
Where is query fan-out heading?
These are my predictions, and I expect them to age quickly.
Visible sub-query lists will fade by the end of 2027. If production query fan-out follows R4T and works in embeddings, vendors will report intent clusters instead of exact phrases.
Coverage metrics will join share of voice on executive dashboards. Leadership needs a number that explains retrieval, yet citation counts don’t.
Brands that win comparison prompts will publish original test data. Meanwhile, commodity summaries will lose retrieval to pages with numbers nobody else has.
Frequently asked questions about query fan-out
What is query fan-out in AI search?
Query fan-out is the step where an AI system splits one prompt into sub-queries. It runs them at the same time. Then it merges the results into one answer. Google introduced the term with AI Mode in 2025.
How many sub-queries does query fan-out create per prompt?
Simple prompts may trigger one. However, Semrush estimates that complex prompts trigger eight to 20 or more. Google hasn’t published exact counts.
Does a top ranking guarantee an AI citation?
No. In one analysis, ChatGPT left 85 percent of retrieved pages uncited, and another found about half. Retrieval is only the first step.
Should I publish one page per sub-query?
No. Google says doing so mainly to manipulate AI responses violates its scaled content abuse policy. Answer several sub-intents on one strong page instead.
Can the AI Visibility Toolkit show Google’s exact sub-queries?
No. Semrush’s AI Visibility Toolkit approximates them through Prompt Research. Instead, Semrush’s Enterprise AI Optimization product shows Google’s actual fan-out queries.
How often should I repeat a coverage audit?
Quarterly is a sensible rhythm, and Semrush recommends it. Sub-queries drift, because only about 27 percent stayed stable in one ten-run study.
Does R4T-Diffusion already power Google AI Mode?
Google hasn’t said so. The paper tests R4T on Polyvore and a music playlist dataset. Its diffusion retriever has 53.9 million parameters. It names no product.
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