Query Fan-Out

Query fan-out is the technique AI search systems use to split one user question into several related sub-queries, run them at the same time, and combine the results into a single answer. Google introduced the term publicly at I/O on May 20, 2025, describing AI Mode as 'breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf' (Google). The same pattern runs in ChatGPT search, Perplexity, Microsoft Copilot and other answer engines, whatever each vendor calls it.

A prompt such as 'best CRM for a 50-person consulting firm' does not get searched as written. The system might search for CRM features for consulting firms, pricing for mid-size teams, reviews from professional services users and integrations with common tools, read the results for each, and write one answer citing the strongest source for each part. The individual searches are grounding queries; fan-out is the act of generating several of them in parallel from one prompt.

The depth varies by product and mode. Google says its Deep Search mode in AI Mode takes fan-out 'to the next level' and 'can issue hundreds of searches'. ChatGPT works at a much smaller scale: OpenAI's help center says it rewrites a prompt into 'one or more targeted queries' and may follow up with more specific ones, and an October 2025 study by Nectiv measured an average of 2.17 searches per ChatGPT prompt that searched. Promptwatch's own dataset of 412,093 ChatGPT responses from December 2025 to April 2026 recorded 800,352 extracted fan-out queries, roughly two per response (ChatGPT query fan-out data).

The reach is large. Google said in May 2026 that AI Mode had passed 1 billion monthly users and AI Overviews 2.5 billion, and the two now run as one connected experience in which follow-up questions flow from an AI Overview into AI Mode.

Fan-out changes which pages get cited. Ahrefs analyzed 863,000 keyword results pages and 4 million AI Overview citations in March 2026 and found that only 37.9% of cited URLs also ranked in the top 10 for the original query, against about 76% in its July 2025 study. Ahrefs' explanation is that AI Overviews increasingly cite pages that rank for the fan-out sub-queries rather than for the query the user typed. A page can therefore win a citation without ranking for the head term, and lose one despite ranking first.

For content strategy, the unit of competition becomes the sub-query:

• Collect real fan-out queries for your important prompts instead of guessing; they are visible in developer APIs and in tracking tools for engines that expose them.
• Group them into recurring themes, such as pricing, integrations, alternatives, reviews and the current year, and check which themes have no matching page on your site.
• Write sections that answer one sub-query each, with the specific facts a model can quote: prices, limits, dates, names and comparisons.
• Rank for the sub-queries in the search engine each AI system uses, because the model reads what ranks there.

Fan-out queries are not stable. The wording changes between runs, and in August 2026 ChatGPT began issuing site: queries aimed at individual domains at scale. Track themes across many responses rather than reacting to single queries, and connect the gaps to content gap analysis and retrieval coverage.