You’ve published a solid blogpost. Your website covers your coaching niche. You rank reasonably well in Google.
But when someone asks ChatGPT to recommend a coach in your specialty, your name doesn’t come up.
The reason is usually query fan-out, and understanding it changes how you should think about content entirely.
What is query fan-out
When someone types a question into ChatGPT, Perplexity, or Google AI Mode, the tool doesn’t search for that exact phrase and hand back a list of results. It breaks the question into a series of related sub-queries, runs each one separately, retrieves content from the best sources it finds for each, and then synthesises all of that into a single answer.
Google’s Head of Search Elizabeth Reid described this publicly in October 2025: AI Mode “recognises when a question needs advanced reasoning, calls on Gemini to break the question into different subtopics, and issues a multitude of queries simultaneously.”
Google generates around 9 sub-queries per prompt on average. ChatGPT generates fewer — around 2 to 4 per prompt typically — but injects specific modifiers the user never typed, most commonly “best,” “top,” “reviews,” and the current year.
The user sees none of this. They just see the answer.
A concrete example relevant to coaching: someone asks ChatGPT “I’ve been struggling with sleep since perimenopause, who could help?”
The tool doesn’t just search for that sentence. It might simultaneously run: “perimenopause sleep problems causes,” “sleep coach for women over 40,” “hormone changes insomnia treatment,” “best perimenopause coaches online,” and “sleep coaching versus sleep therapy.”
Your page needs to be surfaced in at least some of those sub-queries to have any chance of appearing in the final answer.
Why this breaks traditional SEO thinking
In traditional search, visibility is close to binary: you either rank on page one for a keyword, or you don’t.
In AI search, visibility is probabilistic. You might rank poorly for the head term a user typed but dominate several of the sub-queries that actually determine what gets cited.
Or you might rank well for the main keyword but be absent from the sub-queries that matter most.
A Surfer SEO study of nearly 174,000 URLs found that 68% of pages cited in AI Overviews were not in the organic top 10 results. Ahrefs data shows that AI tools cite content that is on average 25.7% fresher than what traditional search surfaces. Ranking and AI citation have genuinely decoupled — winning one doesn’t reliably produce the other.
Peec AI’s analysis of 5 million fan-out queries collected across ChatGPT, Perplexity, and Grok found that ChatGPT uses Reciprocal Rank Fusion to score sources across all sub-queries combined.
Content that appears across multiple sub-queries scores materially higher than content that only surfaces for one. In other words, the more angles of a topic your content covers, the better your chances of being included in the synthesised answer.
For healthcare-related queries specifically, Go Fish Digital research found that the fan-out count is higher than most other industries — averaging 22 to 28 sub-queries per prompt, compared to 18 to 22 for e-commerce. Health coaching questions are complex, multi-part, and personal, so AI tools generate more sub-queries to answer them comprehensively. That’s more opportunities for your content to be retrieved — but also more angles you need to cover to stay in the picture.
What this means for health coaching content
The practical implication is that a single well-written post targeting one keyword is less useful than a cluster of posts covering the same topic from multiple angles — because each post in that cluster has a chance of being retrieved for a different sub-query.
Take a longevity coach writing about muscle mass.
A traditional SEO approach might produce one post: “Why muscle mass matters after 40.”
A fan-out aware approach produces several: a foundational explanation of why muscle is a longevity predictor, a post on sarcopenia and how to prevent it, a comparison of resistance training approaches for older adults, a practical guide to protein intake for muscle retention, a post specifically addressing the question a nervous first-time client would ask (“do I need to lift heavy weights to stay healthy as I age?”), and a piece on how to track whether your training is actually working.
Each of those covers a different sub-query that might be generated when someone asks ChatGPT about muscle and aging. The coach who has all of them has a much higher probability of showing up in any AI answer on the topic than the coach who has one.
Position Digital’s research found that content addressing five or more fan-out sub-intents has 3.2 times higher citation probability than single-intent pages.
One case study in the research restructured a comparison page to cover five intent clusters — research and comparison, feature-specific questions, pricing, implementation guidance, and industry-specific needs — and saw ChatGPT citations increase by 127% and Perplexity appearances by 89% within 60 days.
The content types fan-out rewards
Peec AI’s data showed that ChatGPT’s fan-out consistently adds comparison and “best of” framing to queries even when the user didn’t ask for a comparison.
This explains why listicle-style pages keep appearing in AI results — the fan-out is generating “best [thing] for [audience]” sub-queries regardless of how the original question was phrased.
For coaches, this argues for having at least some content that directly names and addresses your specific audience: “best fitness approach for perimenopausal women,” “what to look for in a sleep coach,” “how nervous system coaching differs from therapy.”
Content structure also matters to extractability. Research across 15,847 AI Overview results found that self-contained passages of 134 to 167 words achieve the highest citation rates, and that content with cosine similarity scores above 0.88 relative to the sub-query achieves 7.3 times higher citation rates.
The practical translation of that: write in clear, self-contained paragraphs that can answer a specific question on their own. Not every paragraph needs to depend on the paragraphs before and after it.
An AI extracting a passage to cite doesn’t take the surrounding context with it.
FAQ sections earn their place here.
A well-constructed FAQ at the bottom of a coaching post can directly answer several of the sub-queries that AI tools generate from the main topic, each in a short, extractable passage. They’re not flashy, but search engine research consistently finds them among the most cited content formats in AI-generated answers.
How to find out what sub-queries are being generated for your niche
There are a few practical approaches, most of them free.
The simplest is to open ChatGPT with the Surfer SEO Chrome extension installed, run a prompt relevant to your coaching niche, and look at the sub-queries generated. The extension surfaces the fan-out queries alongside the response. It’s not comprehensive but it’s directionally useful and takes five minutes.
Google’s Gemini API with grounding enabled returns a groundingMetadata object that shows exactly what Google searched to generate the response. This is first-party data and the most authoritative source for understanding what Google AI Mode is actually doing with a given query. Setting it up requires some technical comfort but it’s free.
Qforia, a free tool from Mike King at iPullRank, simulates fan-out for AI Overviews and AI Mode, showing query types, user intent, and recommended content formats. Worth running your key coaching topics through it.
The manual approach, which requires no tools, is to think through the different types of questions someone might ask on the way to deciding they need a coach like you:
- the definition question (what is X),
- the problem question (why am I experiencing X),
- the solution question (how do I fix X),
- the comparison question (X versus Y),
- the credibility question (what should I look for in an X coach), and
- the practical question (how much does X coaching cost / how does it work).
Each of those is a likely fan-out sub-query type.
Content that covers all of them for your specific niche covers the angles AI tools are most likely to generate.
Surfer SEO’s advice on this is worth taking seriously: rather than obsessing over reverse-engineering specific fan-out queries, which shift constantly, build topical authority in your niche.
A coach who has covered a topic deeply from multiple angles will naturally rank across many of the sub-queries AI tools generate, without needing to predict any individual one.
The goal is to become the most comprehensively useful source on the topic so that whatever sub-queries get generated, some of your content is there.
If you want to understand how well your current content covers the fan-out landscape for your coaching niche, get in touch.
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