Query Fan-Out

Query fan-out is the technique AI search systems use to answer a question by running many searches instead of one. The system breaks the original query into related sub-queries, retrieves information for each of them, and synthesises the results into a single response. Google described it when it launched AI Mode in March 2025 and its documentation says both AI Overviews and AI Mode may use it.

How it works

The system first classifies the query and identifies the entities in it, often using the Knowledge Graph to find related entities and attributes. It then generates synthetic queries, sometimes dozens, that cover different angles of the need, retrieves documents for each, and passes the best passages to a language model to compose the answer. MLforSEO’s lesson on fan-out, based on Google patents such as “Search with stateful chat”, groups the generated queries into types including:

• related queries (same intent, different words)
• implicit queries (needs the user did not state)
• comparative queries (alternatives and contrasts)
• entity-focused queries (specific brands, people or products)
• exploratory or adjacent queries (neighbouring topics)

Fan-out is also personalised: query context and session context shape which sub-queries are generated, so two people typing the same words can get different answers.

How it’s used in SEO and marketing

• Coverage planning: map the likely sub-queries around a target topic and check which ones your content answers.
• Site architecture: hubs with clearly separated, focused sub-topic pages are easier to retrieve for individual fan-out queries than one vague page.
• Entity clarity: because fan-out navigates entities and their attributes, clearly defined entities and EAV coverage improve retrieval chances.
• Measurement: fan-out explains why you can be cited for queries you never targeted, and missing for the one you did.

Example

A user asks “best laptop for students”. The system may also search “top student laptops”, “laptop battery life for lectures”, “MacBook Air vs Chromebook for university” and “student laptop discounts”, then combine what it finds. A retailer whose guide only targets the head term may be skipped, while a site with clear sections on battery life, comparisons and discounts can be cited several times.

Limitations and things to watch

• The exact sub-queries are not shown to users or site owners. Simulations and tools approximate them, so treat outputs as hypotheses.
• Fan-out behaviour changes between systems (Google, ChatGPT, Perplexity) and over time.
• Covering every sub-query is no excuse for thin pages; it rewards depth and distinct information gain.

Learn it on MLforSEO

• How Entities Power Query Fan-Out in AI Search
• What Are Synthetic Queries? Why They Matter for Semantic SEO and AI Search
• What is Query Context and Session Context in SEO?
• Blog and Site Architecture for SEO and Conversions in the AI Search Era
• Semantic ML-enabled Keyword Research (course)