TL;DR: Query fan-out is the process by which an AI search system breaks a user's complex query into multiple related sub-queries, retrieves information for those sub-queries, and combines the results to generate a single answer. For example, a question such as "What is the best AI SEO consultant for SaaS?" may lead an AI search system to investigate related questions about expertise, SaaS experience, services, reviews, pricing, alternatives, and authority before generating its response. This changes how businesses need to think about AI Search Visibility: being visible for the original question is not necessarily enough. Your content and other brand sources may need to be discoverable across the underlying questions that contribute to the final answer. This guide explains how query fan-out works, how the process differs across AI search experiences, how to identify relevant sub-queries, and how to build content that can be retrieved and cited across the broader query landscape.
What Is Query Fan-Out?
Query fan-out is the process by which AI search systems decompose a single user query into multiple parallel sub-queries before retrieving any content or writing any answer, rather than searching your exact words.
Take the query "Tesla Model Y." In traditional one-to-one search, that exact phrase returns one SERP, and a different phrase like "Tesla Model 3" returns a different SERP entirely. Many-to-one search collapses near-duplicate phrasings — "Tesla Model Y" and "Model Y Tesla" — into the same result set. Query fan-out is different in kind, not just degree: one query — "Tesla Model Y" — expands into many synthetic searches the AI runs on your behalf, such as "Tesla Model Y range and battery life," "Tesla Model Y price and variants," "Tesla Model Y vs Model 3 comparison," "Tesla Model Y review and ratings," "Tesla Model Y charging time at home," and "best electric SUVs like Tesla Model Y." A brand only optimised for the single phrase "Tesla Model Y" is invisible to five of those six underlying searches.
The same pattern holds in B2B contexts. When a buyer types "best project management software for remote engineering teams" into ChatGPT Search, the model does not search for that exact phrase. It generates a set of related sub-queries — something like "top project management tools for engineering teams 2026," "project management software comparison remote work," "Jira vs Linear vs Asana engineering teams," and "project management pricing for distributed teams" — and runs all of them simultaneously.
Each sub-query retrieves sources independently. The AI then applies Reciprocal Rank Fusion (RRF) — an algorithm that combines scores across multiple sub-queries — to weight the retrieved content. Content that appears across multiple sub-queries receives higher combined scores than content that appears for only one. That higher combined score directly increases the probability of being cited in the final answer.
The practical consequence is significant. Your content can rank on page one of Google for the original query and still never appear in the AI-generated answer — because the AI never searched your exact query. It searched the sub-queries, and your content covered none of them.
How Fan-Out Differs Across AI Platforms
Each major AI platform implements query fan-out differently. The sub-queries generated, the number of parallel searches run, and the sources each platform prefers within those searches vary substantially — which is why platform-specific fan-out optimisation is a different exercise for each surface.
ChatGPT Search
ChatGPT's fan-out behaviour is the most studied of any AI platform, largely because its network requests can be observed through browser developer tools. A Peec AI study of 5 million query fan-outs collected between April 1–21, 2026 found consistent patterns in how ChatGPT modifies and expands queries before searching. ChatGPT systematically injects words like "best," "reviews," and the current year into sub-queries even when those terms were not in the original prompt. A buyer asking "AI visibility tools" will trigger ChatGPT sub-queries that include "best AI visibility tools 2026" and "AI visibility tool reviews" regardless of the original phrasing.
ChatGPT search activates on 34.5% of queries as of February 2026 — down from 46% in late 2024 — because the model's training data covers many questions without needing live retrieval. When it does search, the sub-queries are precise and mid-funnel. ChatGPT skips definitional searches ("what is a CRM") because it already knows the answer. It searches for implementation specifics, comparisons, and current-year rankings. This means your mid-funnel and bottom-funnel content — comparison pages, implementation guides, pricing pages — is more likely to be retrieved by ChatGPT fan-out than your top-of-funnel definitions.
The August 8, 2026 fanout change documented in our research is a direct illustration of how rapidly ChatGPT's fan-out behaviour can shift. On that date, sub-queries using the site: operator — which scopes a search to a specific domain — jumped from 0.37% to 16.8% of all fan-out queries in a single day. That change restructured which content types got retrieved, redistributing citation share away from discussion-heavy platforms and toward single-page direct answers. Fan-out behaviour is not static. Monitoring it is not optional.
Google AI Mode and AI Overviews
Google AI Mode's fan-out implementation is architecturally the most sophisticated because Google controls both the query decomposition layer and the index being searched. When Google AI Mode processes a query, it decomposes it into multiple sub-queries and retrieves from its own web index simultaneously. The sub-queries generated by AI Mode tend to be more topic-based and less commercially modified than ChatGPT's — Google is less likely to inject "best" or "reviews" automatically because its own search quality systems already weight those signals.
Google AI Overviews, by contrast, draw heavily from the top organic results for each sub-query — creating a stronger link between traditional organic rankings and AI citation than exists on other platforms. FAQPage schema remains the highest-impact single technical signal for Google AI Overview citation because it provides pre-structured Q&A pairs that map directly onto the sub-query decomposition pattern.
Perplexity
Perplexity is the only major AI platform that makes its sub-queries publicly visible. When you search on Perplexity, you can see the exact queries it runs before generating the answer. This transparency makes Perplexity the single most useful platform for reverse-engineering the fan-out landscape for any topic. A search for "payment gateway for SaaS" on Perplexity will show you explicitly that it searched "best payment gateway SaaS 2026," "payment gateway integration complexity SaaS," and "payment gateway pricing recurring billing" — telling you exactly which content angles you need to cover.
Perplexity also shows its sources at the paragraph level, making it possible to see not just which URLs were cited but which specific paragraphs within those URLs contributed to which parts of the answer. This paragraph-level citation visibility is the most useful diagnostic tool currently available for understanding how fan-out retrieval actually works in practice.
How to Find the Fan-Out Queries Your Brand Needs to Cover
There are three practical methods for discovering the sub-queries AI platforms generate for your target prompts — ranging from free manual approaches to systematic research tools.
Method 1 — Use Perplexity's Visible Sub-Queries
Because Perplexity shows its sub-queries openly, it is the fastest free method for fan-out research. Run your 10–15 most important buyer queries in Perplexity and document every sub-query it shows. These are real sub-queries that an AI platform is actually running for those prompts — not keyword suggestions generated by a tool. The sub-queries Perplexity shows are your content gaps mapped directly to AI retrieval behaviour.
Method 2 — Inspect ChatGPT's Network Requests
ChatGPT's sub-queries are observable through browser developer tools. Open ChatGPT in Chrome, press F12 to open developer tools, navigate to the Network tab, then run a prompt with web search enabled. Filter network requests by your conversation ID (visible in the URL after /c/). The search requests that appear in the Network tab contain the actual sub-queries ChatGPT ran. This method requires technical comfort but provides the most accurate view of ChatGPT's specific fan-out behaviour for your prompts.
Method 3 — Use Google AI Mode's "Show More" and Related Searches
Google AI Mode sometimes shows the sub-queries it used in a "Show more" panel or in the sources it cites. Run your target prompts in AI Mode and document which source pages are cited — the titles and content of those pages reveal which sub-query angles the AI was searching for. Autosuggest data from Google Search is also a strong proxy: fan-out sub-query patterns closely follow autosuggest variations because both are driven by the same underlying query intent modelling.
How to Optimise Content for Fan-Out Coverage
Understanding fan-out is only valuable if it changes how you build and structure content. The optimisation principle is straightforward: stop creating variations of the same page and start creating pages that each target one specific sub-query layer within your topic's fan-out landscape.
Map Your Fan-Out Before Creating Content
For each primary topic your brand needs to own, run the prompt through Perplexity and ChatGPT and document all visible sub-queries. Group them into three categories: definitional sub-queries (what is X), comparison sub-queries (X vs Y, best X for use case), and implementation sub-queries (how to do X, X pricing, X integration). These three categories correspond to three different content types that each serve a different role in the fan-out retrieval process.
Build One Page Per Sub-Query Layer, Not One Page Per Topic
Traditional SEO consolidates related content onto single comprehensive pages. Fan-out optimisation requires the opposite: one dedicated page per sub-query angle, each structured to answer that specific sub-query directly in the opening paragraph. A brand that wants to appear across the full fan-out for "AI SEO tools" needs separate pages for "AI SEO tool comparison," "AI SEO tool pricing," "AI SEO for SaaS specifically," and "how to choose an AI SEO tool" — not one page that tries to cover all of these simultaneously.
Content that answers only one sub-query receives a lower Reciprocal Rank Fusion score than content that appears across multiple sub-queries, according to Peec AI's analysis of 5 million ChatGPT fanouts. But that does not mean packing all angles onto one page — it means building enough dedicated pages that your brand appears across multiple sub-queries independently. The LLM Content Strategy service maps this fan-out architecture specifically for each client's category.
Include Fan-Out Modifier Terms Naturally
ChatGPT injects "best," "reviews," and the current year into sub-queries automatically. If your content does not include these modifiers naturally, it will not match the modified sub-queries ChatGPT is actually running. Comparison language, review-style summaries, current-year references, and use-case specificity ("for SaaS startups," "for Series A companies") all increase the probability that your content matches the modified sub-queries rather than just the original prompt.
Use FAQPage Schema to Pre-Structure Sub-Query Answers
FAQPage schema is particularly effective for fan-out optimisation because each question-answer pair functions as a pre-structured response to a potential sub-query. A page with FAQPage schema covering six variations of a topic question gives AI systems six extraction opportunities within a single page — increasing the probability that at least one matches a sub-query from the fan-out. Write each FAQ question in the exact phrasing a buyer would use in an AI search, not in formal keyword language.
The Fan-Out Coverage Audit — What to Check Right Now
Before building any new content, run this five-step audit to understand where your brand currently sits in the fan-out landscape for your most important buyer queries.
Step 1 — Select your 10 most important buyer queries and run each one in Perplexity with web search enabled. Document every sub-query Perplexity shows and every source it cites.
Step 2 — Run the same 10 queries in ChatGPT with Browse enabled. Document which sources appear. Compare with the Perplexity sources — your brand's name appearing on neither platform for any sub-query is your baseline gap.
Step 3 — For every sub-query identified, check whether your brand has a page that directly and specifically addresses that sub-query. Not a page that mentions it in passing — a page whose primary purpose is to answer that specific question.
Step 4 — Document which sub-queries your competitors are cited for that you are not. These are your highest-priority content gaps because they represent buying intent that AI is actively routing to someone else.
Step 5 — Prioritise the gaps by business impact: comparison sub-queries and pricing sub-queries first (bottom-funnel, highest conversion probability), then implementation sub-queries, then definitional sub-queries last.
The $247 AI Visibility Audit runs this fan-out analysis across 20+ buyer queries on ChatGPT, Perplexity, Google AI Mode, and Gemini simultaneously — identifying your sub-query coverage gaps and competitor citation positions in 3 business days.
Traditional SEO optimises for the query. AI search requires optimising for the sub-queries the AI generates from the query — and those are different documents.
Your ranking for the original query is not irrelevant — Google AI Overviews still draw heavily from top organic results. But your ranking for the original query is no longer sufficient to guarantee AI citation because the AI is searching sub-queries, not the original prompt. The brands that understand this early are building fan-out-aligned content architectures. The brands that do not are wondering why their traffic reports look fine and their pipeline does not.
Find out which sub-queries your brand is invisible on
The $247 AI Visibility Audit maps your fan-out coverage across 20+ buyer queries on ChatGPT, Perplexity, Google AI Mode, and Gemini — and identifies the exact sub-query gaps your competitors are currently filling instead of you.
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