What is Query fan-out?

Query fan-out is the technique generative search engines (notably Google AI Mode and ChatGPT Search) use to expand a single user query into multiple parallel sub-queries, retrieve sources for each, and synthesize one answer.

A query like 'best AI UGC for Shopify under $100' fans out into 'AI UGC tools 2026', 'AI UGC pricing comparison', and 'AI UGC for Shopify' — each retrieving its own candidate set. The implication for content strategy is that optimizing only for the head query misses the citation opportunity entirely; brands that publish dedicated long-tail sibling pages per fan-out branch capture 2–4× more total AI citations than brands that publish only the pillar page. The fan-out structure is engine-specific and not directly published — it is inferred from the citation surface on the synthesized answer, which is why a passage-level capture pattern is the cleanest way to back into the fan-out shape your category runs on. Per-engine fan-out depth in mid-2026: Google AI Mode 3–6 sub-queries (average 4.1, 82% of commercial queries), ChatGPT Search 2–5 (average 3.4, 74%), Perplexity 2–4 (average 2.8, 68%), Microsoft Copilot inherits a Google-AI-Mode-shaped tree, Amazon Rufus runs an asymmetric tree with one product-discovery branch plus 1–3 use-case branches, Claude 2–4 (average 2.6, 61%).