What is query fan-out?
Query fan-out is the technique Google has described for AI Mode: when a user asks a question, the system generates multiple related sub-queries covering different subtopics and interpretations, issues them across its search index and other data sources simultaneously, and synthesises the results into a single answer with links to sources.
The consequence for publishers is that the engine is not looking for a page that matches the user's phrasing. It is looking, in parallel, for passages that answer each of the sub-queries it generated, and it assembles the answer from whatever it finds.
What does a fan-out look like?
As a worked example, a user asking whether they should hire an AI employee for their finance team might fan out into sub-queries such as: what is an AI employee for finance, what tasks can it do, what does it cost, how is it approved and controlled, how does it compare with outsourced bookkeeping, what are the risks, and how long does it take to set up.
Each of those is searched separately. A page that answers the cost question well in a self-contained section can be cited for that sub-query even if the rest of the page is about something else, and a page that answers everything but in one long undifferentiated block may be cited for nothing.
- What: definition of the entity
- How: mechanism, tasks, and workflow
- Cost: pricing model and what drives it
- Risk and control: governance, approval, failure modes
- Versus: comparison with the obvious alternatives
- When and who: fit and timing
- Setup: what it takes to start
How should you plan content for fan-out?
Plan content by listing the likely sub-queries before writing, five to nine of them, using the pattern above: what, how, cost, risk, versus, when, who, setup. Then decide where each will be answered: a section of this piece, an FAQ entry, or a linked piece in the same cluster.
The SPEC-035 standard on ftes.ai requires this list for every piece. It is the difference between content that happens to answer some sub-queries and content designed to answer all of them.
How should you structure for fan-out?
Structure for fan-out by giving each sub-query its own self-contained section with a question heading, so that the passage retrieved for that sub-query is complete on its own. Restate the entity in each section, because the sub-query retrieval does not carry the page title with it.
Use FAQs for the sub-queries that need a short answer, and keep those answers spoken-ready. The content structure guide covers the section discipline in detail.
Why does the cluster matter?
The cluster matters because the engine assembles from many sources, and a site with a cluster of consistent pages can be cited for several sub-queries of the same question, each from the page that answers it best. A definition page, a cost page, a governance page, and a comparison page, all naming the same entities the same way and linking to each other, cover a fan-out between them.
Measure by sampling the primary question and, separately, its likely sub-queries, and recording which pages are cited for which. That tells you which sub-queries you own and which you need to write for.
How does a Search Visibility FTE plan a piece around fan-out?
A Search Visibility Digital FTE plans a piece around fan-out before drafting a word. From the primary question a buyer asks, it lists the likely sub-queries using the pattern what, how, cost, risk, versus, when, who, and setup, and checks each against the cluster: which sub-queries an existing page already answers well, which a page answers poorly, and which nothing answers. The brief for the new piece then assigns each uncovered sub-query to a section with a question heading or to an FAQ entry, and names the existing pages the piece should link to for the sub-queries they already own.
The draft follows the brief, with each section self-contained and the entity restated, and the fact gate checks every claim. After publication the measurement sweep samples the primary question and its sub-queries separately, recording which of the cluster's pages are cited for which. Sub-queries still uncited become the next brief. Planning this way turns fan-out from a search-engine internal into a content calendar, and it is why the pieces in the ftes.ai library each carry a fan-out list in their design.