How LLMs work
Fan-out queries: how one prompt becomes many searches
Written by Ghizlene Mejdi, Founder & GEO Project Manager · Last updated: June 2026 · 4 min read
How fan-out works
The model decomposes the user's prompt into variations, comparisons, criteria angles, price questions and edge cases - often including an English-language search even when the user wrote in another language. It runs those sub-queries against its retrieval layer, gathers the results, and fuses them into a single answer. The user sees one response; the model ran ten searches behind the scenes.
Why it changes content strategy
You can't win a fan-out with one page. You win it with a thematic cluster of interlinked pages, each answering one branch cleanly. If three of the model's five sub-queries land on your cluster, you become the most likely brand to cite. See retrievabilityfor why each branch page has to be extraction-ready, and how LLMs choose brands for what tips selection.
How to build for fan-out
Map the sub-queries
Prompt the major LLMs with your buyer's question and ask for the sub-questions they would expand it into. Cross-reference with People Also Ask and forum threads. The output is a tree, not a list.
Build a content silo with explicit internal links
One pillar page, one page per branch, and dense internal linking between them. Every branch page links up to the pillar and across to its siblings.
Structure each page as extractable units
40–60 word direct-answer blocks, FAQ sections, comparison tables. The model needs to lift one unit per branch - give it one.
This is exactly the architecture you're reading right now. Each page in this hub answers one branch of a fan-out around GEO. Book a GEO Audit call if you'd like the same mapped for your category.
Frequently asked questions
How do we discover the sub-queries our buyers' prompts fan out into?+
By prompting the LLMs directly and observing the sub-questions, mining People Also Ask, and analysing LLM referrer logs. RocketGEO's Prompt Intelligence Mapping does this systematically.
Does this mean we need far more pages?+
Not more - better connected. A focused silo of interlinked pages that each answer one branch outperforms a sprawl of disconnected posts.
How is fan-out different from normal keyword research?+
Keyword research targets what users type; fan-out targets what the model generates on their behalf, which is broader, more conversational and often multi-step.