LLM-driven search engines like Perplexity or Gemini don’t just echo your input. When you ask, for example, “CRM for SaaS companies 50-150 employees”, the LLM rewrites or expands the query behind the curtain. Instead of competing head-to-head with your exact keyword match, you’re now in a race across a landscape of semantically related (but often different) queries.
That’s why what you see in Google search results rarely matches what LLMs surface. Your high Google ranking for a specific keyword won’t guarantee you LLM visibility if your content isn’t also ranking for the fan out terms the LLMs are really using.
Query fan-out is the process AI search systems use to turn one question into many smaller, related questions so they can build a richer answer. Instead of treating your query as a single string to match against pages, the system decomposes it into subtopics and angles, runs separate lookups for each, and then stitches together the final response. When you type something like “best GEO strategy for SaaS,” the system doesn’t just look for that exact phrase. It also spins off internal questions like “what is GEO,” “GEO for SaaS examples,” “pricing and implementation considerations,” and “risks or limitations,” then pulls evidence for each of those behind the scenes.
This matters because your visibility in AI answers is no longer tied only to the exact query someone types. What really counts is how often your content can serve as a good answer to those hidden sub-queries that fan out from the original prompt. If your page only addresses a very narrow version of the question, it might match the main query but miss most of the internal branches the system actually uses to assemble its answer. On the other hand, content that covers the broader intent space—definitions, comparisons, edge cases, objections, next steps, and adjacent entities—has more chances to be selected as supporting evidence when the model is combining sources.
From a GEO or AI SEO standpoint, “optimizing for query fan-out” basically means designing content around the whole conversation, not just the starting query. A strong page anticipates the obvious follow-up questions, related terms, and alternative framings and either answers them directly on the page or links cleanly to supporting content that does. When AI systems perform their fan-out, those pages are eligible for multiple sub-queries, so they show up more often as citations or as the underlying context for generated answers. In practice, that pushes you to think less in terms of one keyword per URL and more in terms of covering a complete intent cluster that maps to how an AI system will explode and explore the user’s question.
What does QFO Stand for in SEO?
QFO in SEO stands for Query Fan-Out. It describes how AI search systems take one user query and explode it into many related sub‑queries, then use those to find and stitch together an answer.
