A query fan-out is the process where an AI system takes one user question and explodes it into many smaller, related sub questions in order to find consensus and build a better answer.
For example, a prompt like:
“Emergency electrician Parramatta”
Is almost never used by an LLM in that exact form.
Instead, the system internally expands the prompt into a range of related, location-specific questions such as:
- Is there a licensed electrician available in Parramatta right now?
- What qualifies as an electrical emergency?
- How quickly can an electrician attend in Parramatta?
- Do emergency electricians charge call-out or after-hours fees?
- What suburbs around Parramatta are serviced?
- Can an electrician handle switchboard faults or power outages?
- Is the electrician insured and compliant with NSW regulations?
Each of these is treated as a separate retrieval task. The final answer is stitched together from content that best addresses each sub-query.
Your content is only visible if it answers one or more of those internal questions well.
Why You Aren’t Visible in LLMs / AI Overviews
I want to reiterate some interesting research from David Quaid at Primary Position SEO about LLM search and the Query Fan Out.
If your brand is not showing up in LLM answers, it has nothing to do with missing schema, llms.txt files, or a lack of Reddit mentions. It also has very little to do with traditional brand signals.
The real reason is simpler: LLMs do not use your original query the way Google does.
Instead of running the exact prompt you type, LLMs rewrite it behind the scenes. They expand it, reword it, narrow it, and split it into multiple related searches. This process is what actually determines visibility.
That process is called Query Fan Out.
Query Fan Out in SEO
Query fan-out is the missing concept in most conversations about LLM visibility.
In traditional SEO, you optimise a page to rank for a specific keyword or a tight group of keywords.
In LLM-driven search, that original keyword is rarely used directly. The system first breaks it into multiple related questions, retrieves content for each of those questions, and then synthesises a final answer.
If your content only matches the original query, but not the fan-out queries, it is unlikely to be selected.
From an SEO perspective, this means visibility in LLMs is less about ranking for one phrase and more about ranking across an entire intent cluster.
What Does QFO Stand For in SEO?
QFO stands for Query Fan-Out.
In SEO terms, it describes how AI-driven search systems decompose a single user query into multiple related sub-queries, retrieve results for each, and then combine those results into a generated response.
If you want visibility in LLM answers, you need to rank for the fan-out queries, not just the original prompt.
Why Query Fan Out Matters for LLM Visibility
LLMs do not “scan” or “read” your page the way people imagine. They retrieve relevant passages based on semantic similarity and usefulness to the internal sub-queries they generated.
That means:
- Schema alone does not make you visible;
- Being a “brand” does not guarantee inclusion;
- Ranking #1 for the original query is not enough.
What matters is coverage.
Pages that define the topic, explain how it works, compare alternatives, answer objections, and address edge cases are more likely to be used during the fan-out process.
In practice, this pushes SEO toward topic completeness, not keyword density or formatting tricks.
Query Fan Out Tools?
You do not need a paid tool to find your query fan-out.
LLMs already show you the evidence if you know where to look.
Most AI search tools expose the intermediate steps or reasoning paths they used to generate the answer. Those steps are effectively your query fan-out.
This makes reverse-engineering LLM visibility far more practical than most people realise.
How to Quickly Find Your Query Fan Out
You can identify your query fan-out in a few minutes:
- Go to Perplexity or Gemini
- Enter a realistic prompt
- Example: “CRM for SaaS companies with 50–150 employees”
- Open the “Steps”, “Sources”, or similar reasoning view
- Review the individual questions the system explored
Those rewritten questions are the actual search queries driving the answer.
That list is your roadmap for content creation.
LLM Visibility = Ranking for Fan-Out Queries in Search
If you are not visible in AI answers, the solution is not more PR, more schema, or more speculation.
The solution is simple:
- Identify the fan-out queries;
- Create content that ranks for those queries;
- Cover the full intent space, not just the headline keyword.
When your content consistently answers the sub-questions LLMs rely on, it becomes eligible for inclusion during the answer-generation process.
That is how visibility is earned.
LLM Tips and Tricks
- Use Reddit to research how people phrase questions, not to spam links;
- Create comparative and alternative-based content;
- Answer obvious follow-ups and objections on the same page;
- Write clearly and naturally because formatting tricks are overrated;
- Focus on usefulness, not “optimising for AI” (it doesn’t need you to do this).
Don’t Take Anyone’s Word for It: Get Evidence
If someone claims they have a secret to LLM visibility, ask for proof.
Ask them to screen-share. Ask them to run a live prompt. Ask them to show where a brand appears and why.
If they cannot demonstrate it in real time, the advice is probably theoretical.
Reality beats speculation every time.
Google Query Fan Out
Query fan-out is not new.
Google has discussed query expansion and decomposition for years, especially in the context of complex or conversational searches.
LLMs simply make this process more explicit and more aggressive.
What’s Next – Try It Yourself
Forget one-off hacks, schema tweaks, or chasing every new AI acronym.
The path forward is:
- Discover your query fan-outs;
- Build content that answers those real questions;
- Treat SEO as intent coverage, not keyword matching.
You do not need permission, special tools, or insider access to do this.
You just need to chase the queries that actually matter.