Can you optimise for fan-out queries?

The prompt is only the beginning.

A traditional search returns ranked results. An AI-search workflow can interpret the prompt, split it into subtopics, retrieve across multiple searches and synthesise a cited answer.

1. 01

   User prompt

   The visible question and its constraints.

3. 02

   Hidden search universe

   Subtopics, comparisons, constraints and evidence needs.

5. 03

   Cited answer

   A synthesis assembled from the sources it selected.

Google calls this query fan-out. AI Mode can break a question into subtopics and issue multiple related searches simultaneously, and Google says Deep Search takes the technique further — issuing hundreds of searches, reasoning across separate information and producing a cited report.[1]

What query fan-out actually does.

Complex prompts carry entities, requirements, constraints and a decision. Consider: which CRM suits a 200-person manufacturing sales team using SAP, managing distributors, and migrating within six months?

Google describes query fan-out as concurrent related searches generated to retrieve more information across relevant subtopics and sources.[2]

Longer questions widen the need.

Pew Research Center analysed 68,879 Google searches conducted in March 2025. Longer and question-shaped searches were far more likely to produce an AI summary.[3]

The study does not reveal Google’s internal fan-out queries. It shows that longer, question-oriented needs are closely associated with generative experiences. In B2B journeys those needs usually combine organisational context, existing technology, capability requirements, security concerns, implementation risk and competing alternatives.

You cannot see the exact queries.

Platforms confirm fan-out happens. None of them publishes a complete log of every internal query generated for every prompt.

1. 01

   Prompt wording, conversation history and user context

   Variable

2. 02

   Location, freshness, available sources and gaps

   Variable

3. 03

   Model versions and search-system updates

   Variable

4. 04

   One exact, stable hidden-query list

   Unobservable

Third-party tools can model likely expansions. Treat those outputs as research hypotheses, not platform logs. Ahrefs notes that generated fan-out queries vary between runs, so recurring topics across many expansions are more useful than trying to reproduce one hidden search path.[4]

Bing provides grounding queries associated with cited pages, but describes them as grouped, generalised phrases rather than exact prompts or complete retrieval logs.[5]

The seed query is not the source set.

Ahrefs analysed 863,000 Google result pages and four million AI Overview cited URLs. Only 37.9% of cited URLs appeared in the first ten result blocks for the same query.[4]

Map the decision, not the phrases.

Start with the buyer’s decision, then identify the information required to make it. For a European financial-services company choosing a customer-data platform, the research map looks like this.

This is worth more than publishing a separate page for every syntactic variation of "best CDP for European banks".

Deepen, connect or create.

Every fan-out cluster raises the same question: does this need a new page, or a better one?

A comparison page may need deeper pricing and integration coverage. A product page can link to dedicated security documentation. A full data-residency policy deserves its own source. A minor rephrasing does not.

Build a connected evidence system. Do not maximise the number of URLs.

Google warns against page factories.

Google warns against creating separate content for every imagined query variation when the purpose is to manipulate rankings or generative responses, and says this can violate the scaled content abuse policy.[2] The spam policy defines scaled content abuse as producing many pages primarily to manipulate search rankings rather than to help users.[6]

• ×Hundreds of near-duplicate pages
• ×Synthetic questions with no customer evidence
• ×The same answer under different titles
• ×Thin location, industry or persona variations
• ×Pages created only because a tool suggested a query
• ×Unoriginal summaries assembled from other sources

Turn the search universe into a plan.

Five moves take a buyer prompt to an evidence plan.

1. 01

   Seed prompt

   What is the buyer deciding?

2. 02

   Variables

   What changes the answer?

3. 03

   Clusters

   What must be investigated?

4. 04

   Evidence gaps

   Which claims lack support?

5. 05

   Source plan

   Which asset should answer?

1. 01

   Start with commercially meaningful prompts

   Use sales calls, interviews, support tickets, site search, Search Console and competitor conversations.

2. 02

   Extract the decision variables

   Buyer type, company size, industry, systems, constraints, alternatives, risks and the proof each one needs.

3. 03

   Generate and cluster likely subquestions

   Combine customer evidence, subject experts, search data and AI tools — treating generated expansions as hypotheses.

4. 04

   Map the evidence

   For each claim: the best source, its reliability, its date, and the asset responsible for carrying it.

5. 05

   Inspect repeated AI answers

   Record the brands selected, the pages cited, the formats that recur, the dominant subtopics and what is missing.

Measure coverage and selection apart.

Topic coverage and source selection are different outcomes. A brand can cover every cluster and still not be the source an answer picks.

Grounding-query reports and third-party fan-out tools are useful directional inputs. Neither is a complete log of hidden searches, and neither should be presented as one.

Optimise the need, not a query list.

Query fan-out is real. Google confirms that AI Mode decomposes questions into subtopics and issues multiple searches, and that complex research can involve hundreds.

The full retrieval path is neither visible nor predictable. Brands can still prepare, by understanding buyer context, mapping the questions needed to reach a decision, and supplying credible evidence across a connected source system.