A Rehan Ahmed working reference designed for people and retrieval systems: answer first, explain the mechanism, show the decision framework, link to primary sources, and connect the topic to measurement and commercial outcomes.
Every major resource is designed to give a direct answer, explain the mechanism, connect related evidence and show how the topic can be measured in a real customer journey.
What query fan-out actually means
A complex request may contain several information needs. AI search systems can decompose that request into related searches, retrieve supporting sources and synthesise an answer.
For publishers, this makes topic relationships and supporting evidence more important than chasing one exact keyword phrase.
Why this changes content architecture
A single pillar page cannot always answer every follow-up question well. A strong hub uses a cornerstone resource plus distinct supporting pages where the user intent genuinely differs.
The important word is distinct. A new URL should earn its existence by solving a separate information need.
The Rehan hub model
For AI visibility, the hub connects AI Visibility, ChatGPT, Gemini, Google AI Overviews, AEO, GEO, semantic search, schema, Search Console, GA4 and measurement.
For analytics, Search Console, GA4, GTM and Looker Studio connect because the user often needs more than one system to diagnose a commercial problem.
Passage-level usefulness
Use question-led headings, concise direct answers, definitions, tables, checklists and evidence. These improve human scanning and create self-contained passages that retrieval systems can understand in context.
I’ll help you identify the highest-value next step rather than selling you a generic package.
Internal linking as a knowledge graph
Internal links should express real relationships: concept → implementation → measurement → comparison → tool → service. Avoid mechanical keyword-rich links whose only purpose is SEO.
Entity clarity
Make it obvious who authored the content, what the site covers, which organisations/products are discussed and how concepts relate. Consistent author, organisation and topic signals reduce ambiguity.
External corroboration
Link to primary documentation when a claim depends on platform behaviour. External links are part of being a useful research destination, not a leak to be avoided.
Images and diagrams
Use annotated screenshots and original diagrams when they explain a workflow better than prose. Search and AI experiences increasingly operate across multimodal content, so visual evidence should carry information.
Measurement
Do not treat an AI citation as the only outcome. Measure organic/AI referral traffic where available, branded search, assisted conversions, lead quality and whether content is earning discovery across multiple surfaces.
What not to do
Do not publish hundreds of city/prompt variants, hide AI-written filler behind tabs, invent studies, or claim that schema or llms.txt guarantees citations.
A practical publishing test
Before creating a page ask: does this solve a distinct question, contain information worth citing, add evidence or experience, and have a clear relationship to existing resources? If not, improve an existing page instead.
Rehan’s operating principle
Build fewer pages that deserve to be retrieved. Expand when there is a genuine new user need, new evidence or a useful tool — not because a keyword list contains another variation.
Fan-out architecture vs thin-page architecture
| Signal | Strong hub | Thin expansion |
|---|---|---|
| URLs | Distinct intents | Prompt/keyword variants |
| Internal links | Meaningful relationships | Mechanical cross-links |
| Evidence | Primary sources + experience | Rewritten summaries |
| Outcome | Useful destination | Index bloat |
Questions people ask about Query fan-out: build for the question behind the question
Does query fan-out mean I need a page for every prompt?
No. Build distinct pages only where the information need is genuinely different.
Can internal linking help?
Yes, when links reflect real conceptual and user-journey relationships.
Is query fan-out only a Google concept?
The term is documented by Google for its AI search experiences; other retrieval systems may use their own decomposition and retrieval methods.
Does schema make a page part of fan-out?
No. Structured data can clarify supported context but does not guarantee retrieval or citation.
What is the best content format?
There is no single format. Use the format that best answers the question: prose, tables, steps, screenshots, diagrams, tools or comparisons.
Primary sources & further reading
I prefer primary documentation over recycled marketing claims. These links are useful starting points for checking the latest product behaviour and guidance.