Query Fan-Outs: The Blind Spot of 99% of Marketing Teams
Before answering, ChatGPT breaks your question into sub-queries. These query fan-outs decide your AI visibility — and almost nobody is watching them.
Here's what happens when you ask ChatGPT "what's the best GEO tool in 2026."
The model doesn't answer directly. It first breaks your question down into a series of sub-queries it sends to its search tools — Google, Bing, or its own indexes. These sub-queries are what we call query fan-outs.
The result: it's not your well-ranked pages on "best GEO tool" that determine your presence in the response. It's the pages and sources the model finds across each of its decomposed sub-queries.
And nobody is watching that.
Why Fan-Outs Change Everything
Traditional SEO trained you to optimize for one query, one intent, one piece of content. The mechanics of LLMs are different.
When a user asks a generative engine a complex question, the model breaks it down before answering. A question like "what type of company is Vurto right for" might generate fan-outs like:
- "Vurto GEO tool features"
- "GEO for e-commerce 2026"
- "LLM monitoring for SMBs France"
- "best tool to track ChatGPT citations"
- "AI readability e-commerce site"
The AI's final response is built from sources found across each of these sub-angles. If you're only present on the primary query but missing from the derivatives, you fall out of the final answer — partially or entirely.
What SEO Teams Do Instead
They optimize for keywords. Keywords that humans typed into Google. Those keywords don't map to LLM fan-out queries.
That doesn't mean SEO work is useless — it builds a base of content that feeds LLMs. But stopping there means missing the specific logic of generative engines.
LLM fan-out queries have different characteristics. They tend to be longer. They focus on specific attributes, comparisons, usage conditions. They vary by LLM: ChatGPT and Perplexity don't decompose the same queries the same way.
The result: a content strategy calibrated purely on classical search intent is partly disconnected from what actually drives visibility in AI responses.
How Fan-Outs Work
LLMs now have web search tools they activate based on the nature of the query. The exact architecture varies by model and evolves fast. But the decomposition mechanism is structural.
The model identifies information gaps in its knowledge base (what we call grounding queries — more on that in another article). It then builds a series of sub-queries to fill those gaps. Then it synthesizes the results into a coherent response.
What matters for your GEO visibility is being present on the right derivatives — not just on the initial question.
The Content Strategy That Addresses Fan-Outs
Understanding the fan-outs in your category means identifying the sub-angles to build or strengthen content around.
Map the fan-outs for your strategic queries. Ask LLMs questions about the key topics in your category and observe how they break down their searches. Some tools let you visualize these decompositions directly — Vurto does this, identifying the fan-out queries associated with your brand and sector to calibrate content recommendations.
Produce content dedicated to specific attributes. Fan-outs often target precise dimensions: use cases, comparisons, access conditions, measurable results. A piece of content that directly answers "what's the ROI of a GEO strategy for an e-tailer" is more likely to be pulled than a generic GEO article.
Cover the multi-LLM spectrum. ChatGPT and Perplexity don't generate the same fan-outs. Perplexity leans toward journalistic sources and recent comparisons. ChatGPT favors well-structured content with strong expertise signals. The optimal content strategy covers both logics.
Be present where LLMs look. Publishers, LinkedIn, Reddit, YouTube — these platforms are increasingly mobilized as sources in fan-outs. Being present only on your own domain means playing on a single field.
What This Changes for Content Teams
The brief changes. Instead of "an article on GEO for SMBs," the right brief becomes "a piece of content answering the question 'is GEO relevant for a company with under 50 employees and no dedicated SEO team' with concrete data and examples."
The difference is subtle. The impact on AI visibility is not.
LLMs pull content that answers a sub-question directly and precisely. Informational density counts. Clarity of the answer counts. Format counts.
The Signal Most Teams Don't Have
Knowing which fan-outs touch your category is a tangible competitive advantage. Not because it's an esoteric technique, but because it's a signal the vast majority of competitors don't have.
In 2026, most marketing teams are still optimizing for Google keywords. That's useful. It's not enough. Brands that add fan-out logic to their content strategy are the ones starting to appear in AI answers where their competitors are absent.
Today's blind spot is tomorrow's lever.
Vurto identifies the fan-out queries associated with your brand and sector to calibrate GEO content recommendations.