What Is a Fan-Out Query in AI Search

Full definition

A fan-out query (also written query fan-out) is the process by which an AI engine analyzes the intent behind a user's question, breaks it into multiple more specific sub-queries, and executes them simultaneously against web indexes, knowledge bases, entity graphs, and other sources. The results from all sub-queries are merged and synthesized into a single coherent response, with citations from the most relevant sources found in each sub-query.

The term became widely known when Elizabeth Reid, VP of Search at Google, described it at Google I/O 2025 as the fundamental technique that makes Google AI Mode possible. She explained that the engine decomposes questions into different subtopics and fires multiple queries simultaneously on behalf of the user.

All major generative engines use fan-out: Google AI Mode, ChatGPT Search, Claude, and Perplexity. Differences between them lie in the number of sub-queries generated, the depth of decomposition, and the fusion criteria applied to results.

Why it matters in 2026

Fan-out radically transforms the competitive model in search. In classic SEO, a company competes for a specific keyword. In an engine with fan-out, that same company competes simultaneously across the dozens of sub-queries the engine generates from a single user prompt.

An Ahrefs analysis (2026) measured between 9 and 11 sub-queries per prompt on average, with 24% of prompts generating between 12 and 19 sub-queries, and extreme cases reaching 28. Seer Interactive and Nectiv found that 59% of prompts generate between 5 and 11 additional searches.

For home services and construction companies, this means that a question such as "best bathroom renovation company in Chicago" can generate sub-queries about average prices, legal warranties, sector certifications, company comparisons, and customer reviews. A company with content only about pricing but not about warranties or certifications covers a fraction of the relevant sub-queries and has a lower probability of appearing in the final synthesized response.

How it works

The fan-out process follows four phases:

1. Query analysis. The engine classifies user intent (informational, comparative, transactional) and identifies implicit variables in the query that the user did not state explicitly.

2. Decomposition (fan-out). The LLM generates the sub-queries needed to answer the question completely, including implicit contexts and likely follow-up questions.

3. Parallel retrieval. Sub-queries are executed simultaneously. Each sub-query can target different source types: price tables, reviews, technical definitions, regulations, comparison pages.

4. Synthesis with Reciprocal Rank Fusion (RRF). Results from all sub-queries are merged and re-ranked. Fragments that appear relevant in multiple result lists accumulate higher scores and have a greater probability of being cited in the final response.

Difference from traditional search

Dimension	Traditional search	Fan-out (AI search)
Queries generated	1 per user search	9-11 on average per prompt
Competition	For individual keyword	Across full topic cluster
Visible result	Ranked list of links	Synthesized response with selected citations
Success criterion	Best position in the SERP	Broadest coverage of sub-intents with quality
Visibility without click	Not applicable	Frequent: brand cited without user clicking

The main strategic implication is that topic-cluster content architecture — one pillar page plus interconnected sub-topic pages — is the structure that best aligns with how fan-out works. Each sub-topic page can answer a different sub-query and accumulate score in the fusion process, increasing the probability that the brand is cited in the integrated response.

Related terms

RAG (Retrieval-Augmented Generation), Share of Voice AI, Citability.