{"id":195587,"date":"2026-03-02T06:01:44","date_gmt":"2026-03-02T11:01:44","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=195587"},"modified":"2026-03-02T06:01:44","modified_gmt":"2026-03-02T11:01:44","slug":"query-fan-out","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/query-fan-out\/","title":{"rendered":"What is Query Fan-Out? Understanding the Hidden Queries Driving AI Search"},"content":{"rendered":"<div class=\"intro-txt\">If you wanted to buy a red phone case online, how many searches would you make to find the right one? AI Mode typically makes 5 to 11. ChatGPT Deep Research made&nbsp;420.&nbsp;<\/div>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1002\" height=\"328\" class=\"wp-image-195588\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/chatgpt-deep-research-for-buy-red-phone-case-whi.png\" alt=\"ChatGPT Deep Research for &quot;buy red phone case&quot; which ran 420 searches and cited 30 sources.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/chatgpt-deep-research-for-buy-red-phone-case-whi.png 1002w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/chatgpt-deep-research-for-buy-red-phone-case-whi-680x223.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/chatgpt-deep-research-for-buy-red-phone-case-whi-768x251.png 768w\" sizes=\"auto, (max-width: 1002px) 100vw, 1002px\"><\/p>\n<p>Search engines used to work one-to-one: one search query returned a unique set of results featuring pages that best matched the exact query searched.<\/p>\n<p>Then they evolved to many-to-one, recognizing that queries like \u201cSydney plumber\u201d and \u201cplumbing service in Sydney\u201d could be satisfied by the same results.<\/p>\n<p>But AI search has now flipped the model to one-to-many. One search is expanded into many to help the AI model gain relevant context. This technique is called query fan-out.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1337\" height=\"2048\" class=\"wp-image-195589\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-query-fan-out-one-search-ex.jpg\" alt=\"Ahrefs illustration of query fan-out one search expanding into many\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-query-fan-out-one-search-ex.jpg 1337w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-query-fan-out-one-search-ex-277x425.jpg 277w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-query-fan-out-one-search-ex-768x1176.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-query-fan-out-one-search-ex-1003x1536.jpg 1003w\" sizes=\"auto, (max-width: 1337px) 100vw, 1337px\"><\/p>\n<p>This guide explains how query fan-out works, why AI platforms use it, and how to optimize for&nbsp;it.<\/p>\n<h2><a id=\"post-195587-_msfenweu1k4a\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"What is query fan-out?\">What is query fan-out?<\/div><\/div><\/h2>\n<p>Query fan-out is a technique used by AI search platforms that takes a single user query or prompt and automatically expands it into multiple related sub-queries to generate more comprehensive answers.<\/p>\n<p>AI search platforms use the query fan-out technique to:<\/p>\n<ul>\n<li><strong>Handle ambiguous queries<\/strong> by exploring multiple interpretations instead of incorrectly guessing user intent (e.g., \u201cred phone case\u201d triggers searches for iPhone, Samsung, and Pixel phone models simultaneously)<\/li>\n<li><strong>Pull information from diverse sources<\/strong> to create richer answers than any single page could provide<\/li>\n<li><strong>Anticipate follow-up questions<\/strong> and proactively gather information users will likely need&nbsp;next<\/li>\n<li><strong>Answer complex, multi-faceted questions<\/strong> that require synthesis across different topics and perspectives (e.g., \u201cis remote work good for productivity?\u201d)<\/li>\n<li><strong>Personalize results<\/strong> based on user context, location, search history, and behavior patterns<\/li>\n<\/ul>\n<p>For instance, when you search \u201chow to start a podcast\u201d in Google AI Mode or ChatGPT, you might assume the AI searches for that exact phrase. It doesn\u2019t.<\/p>\n<p>This applies whether you type a short query or paste a 1,000-word prompt.<\/p>\n<p>Either way, it breaks your query into sub-queries behind the scenes. In this example, the sub-queries relate to podcast structure, branding, technical setup, hosting, sourcing guests, content planning, promotion strategies, and audience engagement.<\/p>\n<p>For example, here are the angles ChatGPT searched for when asked how to start an SEO podcast.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1345\" height=\"258\" class=\"wp-image-195590\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-from-chatgpt-that-mentions.png\" alt=\"Snapshot of a response from ChatGPT that mentions &quot;I'll include recommendations on podcast structure, branding, equipment, distribution, guest strategy, and community building tailored to your theme.&quot;\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-from-chatgpt-that-mentions.png 1345w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-from-chatgpt-that-mentions-680x130.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-from-chatgpt-that-mentions-768x147.png 768w\" sizes=\"auto, (max-width: 1345px) 100vw, 1345px\"><\/p>\n<p>In the background, it ran searches for these exact queries (and&nbsp;more):<\/p>\n<ul>\n<li>\u201csolo interview podcast ideas\u201d<\/li>\n<li>\u201cmarketing podcast guide\u201d<\/li>\n<li>\u201cpodcast naming and branding ideas\u201d<\/li>\n<li>\u201c2025 podcast technical setup\u201d<\/li>\n<li>\u201cbest podcast hosting and distribution 2025\u201d<\/li>\n<li>\u201cpodcast guests in marketing tech design\u201d<\/li>\n<li>\u201cpodcast content planning in marketing tech\u201d<\/li>\n<li>\u201cpromoting podcast using SEO and social media\u201d<\/li>\n<li>\u201cbest SEO and marketing podcasts 2025\u201d<\/li>\n<li>\u201cpodcast segments diagram\u201d<\/li>\n<li>\u201cpodcast recording equipment\u201d<\/li>\n<\/ul>\n<p>These sub-queries run in parallel across multiple data sources, including web indexes, podcast platforms, knowledge graphs, product databases, and social media.<\/p>\n<p>The AI then synthesizes all the results into a single comprehensive answer, citing the most relevant and prominent sources it identified.<\/p>\n<h2><a id=\"post-195587-_u1vvtlkov23a\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"The different types of fan-out queries\">The different types of fan-out queries<\/div><\/div><\/h2>\n<p>Fan-out queries can be understood in two ways: by their form (how they\u2019re constructed from the original query) and by their function (what information gap they\u2019re trying to&nbsp;close).<\/p>\n<h3><a id=\"post-195587-_eunvbjb9ztts\"><\/a>Fan-out query formats<\/h3>\n<p>Through analysis of Google\u2019s patent applications, researchers like <a href=\"https:\/\/ipullrank.com\/how-ai-mode-works#:~:text=Synthetic%20Query%20Type,Patent%20Source(s)\">Mike King<\/a> have identified the main forms that synthetic queries take.<\/p>\n<p>These patterns show up consistently across AI Mode, ChatGPT, and other AI search systems:<\/p>\n\n<table id=\"tablepress-523\" class=\"tablepress tablepress-id-523 tablepress-responsive tablepress-ahrefs-width-720px\">\n<thead>\n<tr class=\"row-1\">\n\t<th class=\"column-1\">Fan-Out Type<\/th><th class=\"column-2\">Description<\/th><th class=\"column-3\">Original Query<\/th><th class=\"column-4\">Example Sub-Queries<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"row-2\">\n\t<td class=\"column-1\">Related topics<\/td><td class=\"column-2\">Closely connected subjects that provide context<\/td><td class=\"column-3\">meal prep for beginners<\/td><td class=\"column-4\">meal prep containers,\u201d \u201ceasy meal prep recipes,\u201d \u201cmeal prep storage tips\u201d<\/td>\n<\/tr>\n<tr class=\"row-3\">\n\t<td class=\"column-1\">Implicit questions<\/td><td class=\"column-2\">Unstated concerns the AI predicts you&nbsp;have<\/td><td class=\"column-3\">switching to solar panels<\/td><td class=\"column-4\">how much do solar panels cost,\u201d \u201csolar panel installation time,\u201d \u201csolar panel ROI calculator\u201d<\/td>\n<\/tr>\n<tr class=\"row-4\">\n\t<td class=\"column-1\">Comparative queries<\/td><td class=\"column-2\">Side-by-side evaluations<\/td><td class=\"column-3\">project management software<\/td><td class=\"column-4\">Asana vs Monday,\u201d \u201cproject management tools for small teams,\u201d \u201cproject management software pricing comparison\u201d<\/td>\n<\/tr>\n<tr class=\"row-5\">\n\t<td class=\"column-1\">Recency<\/td><td class=\"column-2\">Time-sensitive searches that prioritize current or updated information<\/td><td class=\"column-3\">best smartphones<\/td><td class=\"column-4\">best smartphones 2026,\u201d \u201clatest smartphone releases,\u201d \u201ctop rated phones February 2026\u201d<\/td>\n<\/tr>\n<tr class=\"row-6\">\n\t<td class=\"column-1\">Reformulations<\/td><td class=\"column-2\">Different phrasings of the same intent<\/td><td class=\"column-3\">how to reduce bounce rate<\/td><td class=\"column-4\">improve website engagement,\u201d \u201ckeep visitors on site longer,\u201d \u201cdecrease website exit&nbsp;rate\u201d<\/td>\n<\/tr>\n<tr class=\"row-7\">\n\t<td class=\"column-1\">Contextual variations<\/td><td class=\"column-2\">Personalized angles based on user history, location, or behavior<\/td><td class=\"column-3\">best restaurants<\/td><td class=\"column-4\">best restaurants in [user\u2019s city],\u201d \u201cbest [cuisine type] restaurants,\u201d \u201cbest restaurants open&nbsp;now\u201d<\/td>\n<\/tr>\n<tr class=\"row-8\">\n\t<td class=\"column-1\">Next-step queries<\/td><td class=\"column-2\">Actions users typically take after the initial search<\/td><td class=\"column-3\">symptoms of diabetes<\/td><td class=\"column-4\">how is diabetes diagnosed,\u201d \u201cdiabetes treatment options,\u201d \u201cdiabetes diet&nbsp;plan\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<h3><a id=\"post-195587-_mqfxaqaq8d3y\"><\/a>Fan-out query functions<\/h3>\n<p>Query complexity and the information gap that an AI system is trying to close determine whether it uses fan-out, which queries it generates, and how many queries it generates.<\/p>\n<p>Research from <a href=\"https:\/\/www.seerinteractive.com\/insights\/gemini-3-query-fan-outs-research\">Seer Interactive<\/a> and <a href=\"https:\/\/nectivdigital.com\/blog\/new-research-we-analyzed-60k-google-fan-out-queries\/\">Nectiv<\/a> found an average of 9-11 fan-out queries per prompt, with 59% triggering 5-11 searches. But 24% trigger 12-19 fan-outs, reaching as high as&nbsp;28.<\/p>\n<p>Ambiguity and missing context in a user\u2019s prompt determine the fan-out depth.<\/p>\n<p>Underspecified queries force AI to either ask for clarification or gather context autonomously. For example, when asked to help a user buy a red phone case, Claude asked clarifying questions upfront and required fewer fan-out queries during research.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1213\" height=\"1099\" class=\"wp-image-195591\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-in-claude-that-asked-clarif.png\" alt=\"Snapshot of a response in Claude that asked clarifying questions in multiple choice format when the user was looking to buy a red phone case\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-in-claude-that-asked-clarif.png 1213w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-in-claude-that-asked-clarif-469x425.png 469w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-a-response-in-claude-that-asked-clarif-768x696.png 768w\" sizes=\"auto, (max-width: 1213px) 100vw, 1213px\"><\/p>\n<p>ChatGPT Deep Research did not request additional context; instead, it ran hundreds of searches to explore all possibilities. For example, it ran 200 searches just to hedge for the user\u2019s potential phone model and preferred case&nbsp;types:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1039\" height=\"691\" class=\"wp-image-195592\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-indicating-the-i.png\" alt=\"Snapshot of ChatGPT Deep Research indicating the independent steps the model would take to answer the query &quot;buy red phone case&quot; with over 200 searches performed for the first step of &quot;Identify the user's phone model and preferred case type assumptions.&quot;\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-indicating-the-i.png 1039w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-indicating-the-i-639x425.png 639w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-indicating-the-i-768x511.png 768w\" sizes=\"auto, (max-width: 1039px) 100vw, 1039px\"><\/p>\n<p>From what we\u2019ve observed, AI platforms tend to expand user prompts in a few recurring patterns, like:<\/p>\n<ul>\n<li><strong>Disambiguation<\/strong>: When a query is underspecified, AI first searches to narrow down possibilities. \u201cRed phone case\u201d becomes a search for iPhone, Samsung, and Pixel models to determine which device best fits the searcher\u2019s needs.<\/li>\n<li><strong>Entity attributes<\/strong>: AI resolves what the thing is across all dimensions: color, material, features, compatibility, etc. AI expands the user\u2019s query to cover the full space and stack the features the user is most likely to care&nbsp;about.<\/li>\n<li><strong>Journey stages<\/strong>: When a query spans multiple decision stages, AI searches across all of them. <a href=\"https:\/\/ahrefs.com\/blog\/search-experience-optimization\/\">\u201cBuy laser cutter\u201d<\/a> triggers simultaneous early research, education, material sourcing, community validation, and purchase queries.<\/li>\n<li><strong>Trust signals<\/strong>: High-stakes queries trigger searches for credibility markers like reviews, credentials, validation, policies, endorsements. A $15 purchase needs minimal verification. <a href=\"https:\/\/ahrefs.com\/seo\/glossary\/ymyl-pages\">YMYL topics<\/a> or expensive purchases require extensive validation.<\/li>\n<li><strong>Comparison criteria<\/strong>: AI identifies which attributes matter for decisions, not just what exists. Searches for \u201cprice comparison,\u201d \u201cmaterials comparison,\u201d and \u201crating comparison\u201d reveal evaluation dimensions rather than cataloging features.<\/li>\n<li><strong>Action and risk<\/strong>: When queries imply actions, AI verifies feasibility, consequences, and transaction infrastructure. Which sources best allow you to complete this action? What if it fails? Such searches cover product availability, shipping, returns, warranties, and refunds.<\/li>\n<\/ul>\n<p>The more dimensions that require resolution, the deeper the fan-out goes.<\/p>\n<h2><a id=\"post-195587-_hr3rj6wgurad\"><\/a>Why does query fan-out matter for SEO and AI search?<\/h2>\n<p>Query fan-out is used by all major AI-powered search platforms (Google AI Mode, ChatGPT, Claude, and Perplexity), making it central to how millions of people discover content.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"519\" class=\"wp-image-195593\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-of-fan-out-queries-in-chatgpt-.png\" alt=\"Example of fan-out queries in ChatGPT.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-of-fan-out-queries-in-chatgpt-.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-of-fan-out-queries-in-chatgpt--629x425.png 629w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\"><\/p>\n<p>It challenges the keyword mindset SEOs have optimised around for decades. <strong>Ranking #1 for a single query isn\u2019t enough anymore.&nbsp;<\/strong><\/p>\n<p>AI simultaneously searches dozens of related queries, scoring and comparing results across all of them. Your content now directly competes for relevance across an entire topic landscape, not just one search term.<\/p>\n<p>This raises the bar for what content actually gets&nbsp;cited.<\/p>\n<p>Perhaps most significantly, query fan-out expands on implicit context. It anticipates the different ways searchers explore topics and takes them a step closer to getting the answers they\u2019re looking for.<\/p>\n<p>Traditional search relied on explicit context in search queries. For instance, unless you mentioned you wanted headphones \u201cfor running\u201d, Google would not display pages or products that are specifically for runners.<\/p>\n<p>AI platforms don\u2019t necessarily need users to include all of the relevant context in their searches. They can infer a lot of it from search history and user behavior (among other data points).<\/p>\n<p>Here\u2019s an example of how ChatGPT gained context from past conversations with a user, implicitly adapting its response format according to what it thought the user would prefer:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"823\" height=\"322\" class=\"wp-image-195594\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpts-thinking-in-the-sidebar-of.png\" alt=\"Snapshot of ChatGPT's &quot;thinking&quot; in the sidebar of a chat with a user that indicates &quot;The user likes templates, so I'll provide a simple table schema...&quot; and personalizing its response.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpts-thinking-in-the-sidebar-of.png 823w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpts-thinking-in-the-sidebar-of-680x266.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpts-thinking-in-the-sidebar-of-768x300.png 768w\" sizes=\"auto, (max-width: 823px) 100vw, 823px\"><\/p>\n<p>AI accounts for the contexts that matter most to the searcher in the fan-out process.<\/p>\n<p>It fundamentally shifts SEO away from optimizing for individual keywords and toward understanding your audience and comprehensively covering topics they\u2019re interested in.<\/p>\n<h2><a id=\"post-195587-_jj86hqa2dhyc\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"How query fan-out works (the technical side made simple)\">How query fan-out works (the technical side made simple)<\/div><\/div><\/h2>\n<p>The basic query fan-out process follows these&nbsp;steps:<\/p>\n<ol>\n<li><strong>Query analysis<\/strong>: The AI analyzes your prompt or question to understand intent, complexity, and response type needed (happens in milliseconds).<\/li>\n<li><strong>Decomposition<\/strong>: Your single prompt breaks into multiple sub-queries covering all relevant angles (e.g., \u201chow to start a business\u201d becomes queries about business plans, legal requirements, funding, marketing, and accounting).<\/li>\n<li><strong>Parallel retrieval<\/strong>: All fan-out queries are simultaneously searched across web indexes (such as Google, Bing, and Brave), knowledge graphs, databases, and specialized repositories.<\/li>\n<li><strong>Synthesis<\/strong>: The AI combines multiple search result lists into one unified set using <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/search\/hybrid-search-ranking\">reciprocal rank fusion (RRF)<\/a> \u2014 a method that scores and merges multiple lists of results by rewarding those that appear consistently across them.<\/li>\n<li><strong>Scoring<\/strong>: Each document gets scored based on its relevance to the original query and position across lists (e.g., ranking #2 in one list and #5 in another could score 1\/2 + 1\/5). Documents appearing in multiple lists accumulate higher scores.<\/li>\n<li><strong>Final ranking<\/strong>: Documents are re-ranked by their total score, producing the unified result set that the AI uses to generate its answer.<\/li>\n<\/ol>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1704\" height=\"2048\" class=\"wp-image-195595\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-how-query-fan-out-works-on.jpg\" alt=\"Ahrefs' illustration of how query fan-out works on the technical side \" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-how-query-fan-out-works-on.jpg 1704w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-how-query-fan-out-works-on-354x425.jpg 354w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-how-query-fan-out-works-on-768x923.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-illustration-of-how-query-fan-out-works-on-1278x1536.jpg 1278w\" sizes=\"auto, (max-width: 1704px) 100vw, 1704px\"><\/p>\n<p>This process explains why comprehensive articles appearing in multiple fan-out query results get cited more prominently. It\u2019s also validated in <a href=\"https:\/\/surferseo.com\/blog\/query-fan-out-impact\/\">Surfer SEO\u2019s study<\/a>, which suggests that ranking for multiple fan-out queries increases your chances of being cited by&nbsp;AI.<\/p>\n<p>Being relevant to one narrow search isn\u2019t enough anymore. You need relevance and visibility across entire topics.<\/p>\n<div class=\"sidenote\"><div class=\"sidenote-title\">Sidenote.<\/div> <em>This section describes the general fan-out process used by most AI platforms, though specific implementation details vary by provider. For instance, you can check out <\/em><a href=\"https:\/\/developers.google.com\/search\/docs\/appearance\/ai-features\"><em>Google\u2019s technical documentation<\/em><\/a><em> for query fan-out in AI Mode and AI Overviews.<\/em><\/div>\n<h2><a id=\"post-195587-_39opcf7jwvfs\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"How to optimize for query fan-out and improve AI visibility\">How to optimize for query fan-out and improve AI visibility<\/div><\/div><\/h2>\n<p>Understanding query fan-out is one thing. Adapting your SEO strategy for it is another. Here\u2019s a practical process for getting started.<\/p>\n<h3><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"1. Map your fan-out themes and patterns\" data-section=\"one\"><a id=\"post-195587-_qjve5bi0ixrh\"><\/a>1. Map your topic\u2019s fan-out themes and patterns<\/div><\/div><\/h3>\n<p>You can use many tools to find fan-out queries for your target keywords and topics.<\/p>\n<p>For example, in Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/brand-radar\">Brand Radar<\/a>, enter your brand or topic and navigate to the <strong>AI responses<\/strong> report. You\u2019ll see the fan-out queries for ChatGPT and Perplexity prompts.&nbsp;<img loading=\"lazy\" decoding=\"async\" width=\"1327\" height=\"389\" class=\"wp-image-195596\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/fan-out-queries-in-ahrefs-brand-radar-.png\" alt=\"Fan-out queries in Ahrefs' Brand Radar.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/fan-out-queries-in-ahrefs-brand-radar-.png 1327w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/fan-out-queries-in-ahrefs-brand-radar--680x199.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/fan-out-queries-in-ahrefs-brand-radar--768x225.png 768w\" sizes=\"auto, (max-width: 1327px) 100vw, 1327px\"><\/p>\n<p>Where many people go wrong is thinking that these queries are like topic clusters 2.0, and they need to optimize for these exact terms in their content.<\/p>\n<p>Functionally, they appear similar to long-tail queries, but under the hood, they\u2019re quite different. For instance, they\u2019re:<\/p>\n<ul>\n<li><strong>Synthetic<\/strong> since they\u2019re generated by AI to help it create a comprehensive response for a searcher<\/li>\n<li><strong>Inconsistent<\/strong> since even the same prompt triggers different fan-outs between AI models and searchers<\/li>\n<li><strong>Probabilistic<\/strong>, which means that even with the same prompt, model, and user, unique fan-out queries are very common<\/li>\n<li><strong>Context-rich<\/strong>, which means that AI adds contextual modifiers that humans may never actually search for<\/li>\n<li><strong>Zero-search volume<\/strong> queries since <a href=\"https:\/\/www.seerinteractive.com\/insights\/identifying-signal-from-noise-6-ways-to-leverage-query-fan-outs-for-ai-search-strategy\">over 95%<\/a> receive no recurring searches<\/li>\n<\/ul>\n<p>Instead, look for the patterns that emerge and adapt your search optimization strategy accordingly.<\/p>\n\n<table id=\"tablepress-524\" class=\"tablepress tablepress-id-524 tablepress-responsive tablepress-ahrefs-width-720px\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\">Fan-Out Pattern<\/th><th class=\"column-2\">What Triggers It<\/th><th class=\"column-3\">Optimization Priority<\/th><th class=\"column-4\">Example<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\">Entity-heavy<\/td><td class=\"column-2\">Products, tools, services with multiple attributes<\/td><td class=\"column-3\">Explicit attribute coverage + structured data<\/td><td class=\"column-4\">Wireless headphones\u201d \u2192 prioritize model comparisons, feature specs, compatibility charts<\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">Journey-heavy<\/td><td class=\"column-2\">Complex purchases, unfamiliar categories, multi-stage decisions<\/td><td class=\"column-3\">Content clusters spanning all stages<\/td><td class=\"column-4\">Home solar panels\u201d \u2192 awareness content, cost calculators, installation guides, ROI analysis<\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\">Trust-heavy<\/td><td class=\"column-2\">YMYL topics, high-cost items, irreversible decisions<\/td><td class=\"column-3\">EEAT signals + third-party validation<\/td><td class=\"column-4\">Financial advisor\u201d \u2192 credentials, certifications, client reviews, regulatory compliance<\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">Comparative<\/td><td class=\"column-2\">Queries implying a choice between options<\/td><td class=\"column-3\">Side-by-side evaluations + decision criteria<\/td><td class=\"column-4\">Best CRM software\u201d \u2192 feature comparison tables, use-case fit, pricing breakdowns<\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\">Personalized<\/td><td class=\"column-2\">Location-dependent or contextual queries<\/td><td class=\"column-3\">Local relevance + user-specific angles<\/td><td class=\"column-4\">Coffee shops\u201d \u2192 neighborhood guides, hours, amenities, user preferences<\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\">Recent<\/td><td class=\"column-2\">Time-sensitive or evolving topics<\/td><td class=\"column-3\">Content freshness + temporal qualifiers<\/td><td class=\"column-4\">SEO trends\u201d \u2192 2026-specific tactics, recent algorithm updates, current best practices<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<p>Once you identify the patterns emerging from fan-out queries about your brand or topic, prioritize them based on impact.<\/p>\n<p>Not every fan-out pattern matters equally. Focus on patterns that:<\/p>\n<ul>\n<li>Align with your business goals and target audience <em>(e.g., a project management tool targeting small businesses focuses on \u201cteam productivity\u201d clusters, not \u201centerprise workflows\u201d)<\/em><\/li>\n<li>Fill gaps in your existing content coverage <em>(e.g., you rank for \u201chow to start a podcast\u201d but have nothing on \u201cpodcast equipment for beginners\u201d)<\/em><\/li>\n<li>Offer competitive differentiation opportunities <em>(e.g., competitors own \u201cbest CRM software\u201d but no one has strong coverage on \u201cCRM for freelancers\u201d)<\/em><\/li>\n<\/ul>\n<p>As a final check, I like to enter the priority queries into Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/keywords-explorer\">Keywords Explorer<\/a> to analyze search metrics. This helps to quickly weed out queries with no search potential:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"846\" height=\"699\" class=\"wp-image-195597\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-list-of-keywords-that-chatgpt-searched-as.png\" alt=\"Example list of keywords that ChatGPT searched as fan-out queries entered into Ahrefs' Keywords Explorer, indicating only one out of eleven has search volume.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-list-of-keywords-that-chatgpt-searched-as.png 846w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-list-of-keywords-that-chatgpt-searched-as-514x425.png 514w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/example-list-of-keywords-that-chatgpt-searched-as-768x635.png 768w\" sizes=\"auto, (max-width: 846px) 100vw, 846px\"><\/p>\n<div class=\"sidenote\"><div class=\"sidenote-title\">Sidenote.<\/div> Keywords that aren\u2019t indexed in the Ahrefs database are usually excluded due to extremely low search interest. We have a database of over 110 billion discovered keywords and filter it to the 28.7 billion that are the most popular and worth optimizing for. Most fan-out queries don\u2019t make the&nbsp;cut.<\/div>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"2. Audit your fan-out query coverage for key topics\" data-section=\"two\">\n<h3><a id=\"post-195587-_g18qokotua2g\"><\/a>2. Audit your fan-out query coverage for key topics<\/h3>\n<\/div><\/div>\n<p>Next, audit your existing content against the priority query fan-out patterns you\u2019ve identified. Which angles do you already cover? Which are missing?<\/p>\n<p>Start by going broad. Look at your sitewide content and check out any obvious content gaps.<\/p>\n<p>A quick way to do this is in Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/site-explorer\">Site Explorer<\/a> &gt; <strong>Site Structure<\/strong> report to see all pages you have and how they perform in search:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1229\" height=\"474\" class=\"wp-image-195598\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-site-structure-report.png\" alt=\"Ahrefs Site Structure report\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-site-structure-report.png 1229w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-site-structure-report-680x262.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-site-structure-report-768x296.png 768w\" sizes=\"auto, (max-width: 1229px) 100vw, 1229px\"><\/p>\n<p>If you have a large site, try using the filters to look for specific themes and topics. Assess if you cover the top-level patterns that emerge from your query fan-out analysis. For instance, do you cover the topic from multiple intents? Do you have relevant content for different stages in a searcher\u2019s journey?<\/p>\n<p>Note any gaps at this level. These will become tasks to create new content.<\/p>\n<p>Next, go deep by doing a page-by-page audit. The purpose is to assess the depth of each post on the target query or topic. These gaps will become tasks to update existing content.<\/p>\n<p>You can do this manually by reading each page and considering whether there are any gaps you can fill simply by adding new sections. Or you can try out Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/ai-content-helper\">AI Content Helper<\/a>.<\/p>\n<p>Enter your page and the main keyword you want to optimize for, and the report generates automatically.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1065\" height=\"644\" class=\"wp-image-195599\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/set-up-screen-for-ahrefs-ai-content-helper.png\" alt=\"Set-up screen for Ahrefs' AI Content Helper\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/set-up-screen-for-ahrefs-ai-content-helper.png 1065w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/set-up-screen-for-ahrefs-ai-content-helper-680x411.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/set-up-screen-for-ahrefs-ai-content-helper-768x464.png 768w\" sizes=\"auto, (max-width: 1065px) 100vw, 1065px\"><\/p>\n<p>If there are specific fan-out queries you want to optimize for, you can enter those instead of the article\u2019s main keyword to get deeper insights and optimization angles.<\/p>\n<p>The report will also run an intent analysis to ensure the page you\u2019re optimizing matches the intent of the fan-out query. You can use this to understand the dominant search intents your target topics and their fan-outs cover.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1402\" height=\"771\" class=\"wp-image-195600\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-intent-analysis.png\" alt=\"Ahrefs' AI Content Helper intent analysis\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-intent-analysis.png 1402w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-intent-analysis-680x374.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-intent-analysis-768x422.png 768w\" sizes=\"auto, (max-width: 1402px) 100vw, 1402px\"><\/p>\n<p>Then it will give you ideas for sections to add that cover the specific fan-out query you\u2019re interested in.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"849\" height=\"644\" class=\"wp-image-195601\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-topic-gap-analysis-and-r.png\" alt=\"Ahrefs' AI Content Helper topic gap analysis and recommendations\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-topic-gap-analysis-and-r.png 849w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-topic-gap-analysis-and-r-560x425.png 560w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-ai-content-helper-topic-gap-analysis-and-r-768x583.png 768w\" sizes=\"auto, (max-width: 849px) 100vw, 849px\"><\/p>\n<p>You can also use query fan-out patterns to inform your off-site strategy. Many fan-out queries trigger searches for external validation, such as review sites, <a href=\"https:\/\/ahrefs.com\/blog\/best-lists-research\/\">\u201cbest of\u201d listicles<\/a>, industry publications, comparison sites, and community discussions. You can\u2019t optimize for these on your own website.<\/p>\n<p>You can, however, use Brand Radar\u2019s <strong>Cited pages<\/strong> report to see which third-party sources AI platforms cite for your priority topics and fan-out queries.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1425\" height=\"1129\" class=\"wp-image-195602\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-brand-radar-cited-pages-report-example-for.png\" alt=\"Ahrefs' Brand Radar Cited Pages report example for the topic of gardening.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-brand-radar-cited-pages-report-example-for.png 1425w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-brand-radar-cited-pages-report-example-for-536x425.png 536w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-brand-radar-cited-pages-report-example-for-768x608.png 768w\" sizes=\"auto, (max-width: 1425px) 100vw, 1425px\"><\/p>\n<p>Look for patterns like:<\/p>\n<ul>\n<li><strong>Where you\u2019re already visible:<\/strong> Review sites, industry directories, affiliates already mentioning you<\/li>\n<li><strong>Where competitors appear, but you don\u2019t:<\/strong> Gaps in your third-party presence<\/li>\n<li><strong>What content types dominate:<\/strong> Listicles, comparisons, reviews, news coverage<\/li>\n<\/ul>\n<p>Add them to your <a href=\"https:\/\/ahrefs.com\/blog\/link-prospecting\/\">outreach prospect list<\/a> if you want to improve your brand\u2019s positioning within them.<\/p>\n<p>Whether auditing your own or third-party presence, prioritize the gaps that align with high-priority fan-outs identified in your analysis.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"3. Close gaps on and off your website\" data-section=\"three\">\n<h3><a id=\"post-195587-_insf8v5va0jf\"><\/a>3. Close gaps on and off your website<\/h3>\n<\/div><\/div>\n<p>Query fan-out is how AI search makes educated guesses about what you\u2019re really looking for. Optimising for it means thinking beyond topic clusters. The right approach depends on what kind of context the AI is trying to fill&nbsp;in.<\/p>\n<h4><a id=\"post-195587-_9s4br1qsdve3\"><\/a>For products, tools, and services, make sure your entity data is complete and consistent:<\/h4>\n<p>Make sure all your product or entity attributes are listed and accurate.<\/p>\n<p>For instance, if a searcher wants to buy a phone case, they don\u2019t really have a lot of questions about phone cases that need to be answered in a blog&nbsp;post.<\/p>\n<p>What they care about more are attributes and features of the product, like:<\/p>\n<ul>\n<li>Colour and design, e.g, \u201cred phone&nbsp;case\u201d<\/li>\n<li>Phone model it fits, e.g, \u201ciphone 15 phone&nbsp;case\u201d<\/li>\n<li>Material it\u2019s made of, e.g, \u201cleather phone&nbsp;case\u201d<\/li>\n<li>Style and features, e.g, \u201cphone case with card holder\u201d<\/li>\n<\/ul>\n<p>But they also care about implicit features that don\u2019t often appear in their search queries. They use these as a mental filter to choose which suppliers and products appeal to&nbsp;them.<\/p>\n<p>For instance, ChatGPT Deep Research conducted 420 searches before recommending red phone cases to buy. It analyzed the explicit signals searchers often look for (listed above) and then added many implicit ones too, like specific shades of red, anti-yellowing, wireless charging alignment, popular retailers near the searcher, and&nbsp;more:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"986\" height=\"698\" class=\"wp-image-195603\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-for-the-query-b.png\" alt=\"Snapshot of ChatGPT Deep Research for the query &quot;buy red phone case&quot; with examples of implicit context highlighted like specifying exact shades of red, features like anti-yellowing and wireless charging alignment and more.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-for-the-query-b.png 986w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-for-the-query-b-600x425.png 600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/snapshot-of-chatgpt-deep-research-for-the-query-b-768x544.png 768w\" sizes=\"auto, (max-width: 986px) 100vw, 986px\"><\/p>\n<p>This is what I call feature stacking. It\u2019s the mental list of features and expectations a searcher forms when looking for the thing they want to buy. Query fan-out makes this visible and a layer we need to optimize for.<\/p>\n<ul>\n<li><strong>Optimize product pages<\/strong> with accurate descriptions, images, and details of relevant features. For example, add images with red cases and a color picker on the product page.<\/li>\n<li><strong>Optimize images<\/strong> with specific mentions of features and attributes they represent. For example, call the image \u201cred phone case for iPhone 15 by {Your brand}\u201d. Add similar descriptors in the alt&nbsp;text.<\/li>\n<li><strong>Optimize your tags and categories<\/strong> (and other taxonomies) to include high-priority properties of your core product line. For example, add a tag for \u201cred\u201d if you sell many types of red phone&nbsp;cases.<\/li>\n<li><strong>Create relevant collection pages<\/strong> to optimize directly for keywords like \u201cred phone cases\u201d, provided they have search volume or are priority segments in your product line.<\/li>\n<li><strong>Add relevant product schema<\/strong> and fill it out as accurately and completely as possible. Do not skimp on the technical specifications of your product or relevant features and attributes.<\/li>\n<li><strong>Check your merchant centre data<\/strong> and relevant product feeds to ensure product properties, features, and attributes are accurately included where appropriate.<\/li>\n<\/ul>\n<p>If you want to make sure you don\u2019t miss anything, try asking your preferred LLM to map out a decision flow chart or run a deep analysis to identify deeper patterns. If you\u2019re optimizing for other entities besides products, the same process applies to them,&nbsp;too.<\/p>\n<p>For instance, ChatGPT developed this decision flowchart and added fan-out queries at every&nbsp;level:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1181\" height=\"1885\" class=\"wp-image-195604\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/a-decision-tree-chatgpt-generated-when-answering-t.png\" alt=\"A decision tree ChatGPT generated when answering the query &quot;buy red phone case&quot;, indicating all the layers of context and complexity that it needed to research and answer before providing the user with product recommendations.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/a-decision-tree-chatgpt-generated-when-answering-t.png 1181w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/a-decision-tree-chatgpt-generated-when-answering-t-266x425.png 266w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/a-decision-tree-chatgpt-generated-when-answering-t-768x1226.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/a-decision-tree-chatgpt-generated-when-answering-t-962x1536.png 962w\" sizes=\"auto, (max-width: 1181px) 100vw, 1181px\"><\/p>\n<h4><a id=\"post-195587-_q3psyv8ttizj\"><\/a>For complex search journeys, cover every stage of the decision process:<\/h4>\n<p>Optimize through content clusters spanning all stages. Build pillar pages (broad topic overviews) supported by cluster pages (deep dives into specific subtopics) that cover each stage: awareness, education, comparison, decision, and implementation.<\/p>\n<p>You can also use the <strong>Questions<\/strong> report in Keyword Explorer (or visual tools like <a href=\"https:\/\/alsoasked.com\/\">AlsoAsked<\/a> and <a href=\"https:\/\/answerthepublic.com\/\">Answer the Public<\/a>) to map common questions at different parts of a searcher\u2019s journey.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1436\" height=\"652\" class=\"wp-image-195605\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-questions-report-in-keywords-explorer.png\" alt=\"Ahrefs' Questions report in Keywords Explorer\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-questions-report-in-keywords-explorer.png 1436w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-questions-report-in-keywords-explorer-680x309.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-questions-report-in-keywords-explorer-768x349.png 768w\" sizes=\"auto, (max-width: 1436px) 100vw, 1436px\"><\/p>\n<p>This works great for informational topics, where articles can provide the comprehensive answers people are seeking.<\/p>\n<p>Optimizing at this stage primarily includes creating new content to build out your <a href=\"https:\/\/ahrefs.com\/blog\/topical-authority\/\">topical authority<\/a> and <a href=\"https:\/\/ahrefs.com\/blog\/republishing-content\/\">updating existing content<\/a> for deeper coverage.<\/p>\n<h4><a id=\"post-195587-_fprn1oq4e4m0\"><\/a>For high-stakes or YMYL topics, make your expertise and credentials impossible to&nbsp;miss:<\/h4>\n<p>Help AI recognise your expertise on the topic by including social proof and trust signals it can surface (commonly referred to as <a href=\"https:\/\/ahrefs.com\/blog\/eeat-seo\/\">EEAT signals<\/a>), such&nbsp;as:<\/p>\n<ul>\n<li>Author credentials<\/li>\n<li>Third-party citations<\/li>\n<li>Reviews<\/li>\n<li>Awards<\/li>\n<li>Transparent methodologies<\/li>\n<li>Published policies<\/li>\n<li>Case studies<\/li>\n<li>Community presence<\/li>\n<\/ul>\n<p>Once you identify what trust signals show up in query fan outs, you can perform an <a href=\"https:\/\/ahrefs.com\/blog\/eeat-audit\/\">E-E-A-T audit<\/a> to find any gaps you can&nbsp;close.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1336\" height=\"724\" class=\"wp-image-195606\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-eeat-audit-template.png\" alt=\"Ahrefs' EEAT Audit template\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-eeat-audit-template.png 1336w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-eeat-audit-template-680x369.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/ahrefs-eeat-audit-template-768x416.png 768w\" sizes=\"auto, (max-width: 1336px) 100vw, 1336px\"><\/p>\n<p>Focus on the priority patterns you noticed in the fan-out queries you analyzed. Remember: AI pulls trust signals from across the web, not just your&nbsp;site.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"4. Measure your topic coverage and performance\" data-section=\"four\">\n<h3><a id=\"post-195587-_bsgyf25dfflv\"><\/a>4. Measure your topic coverage and performance<\/h3>\n<\/div><\/div>\n<p>Query fan-out may change what you measure, but it shouldn\u2019t replace traditional SEO metrics. Rather, it adds a new layer. You need visibility into both traditional search performance and AI citation patterns.<\/p>\n<p>Here\u2019s how you can do that in Ahrefs.<\/p>\n<ul>\n<li><strong>Track AI search visibility with <\/strong><a href=\"https:\/\/ahrefs.com\/brand-radar\"><strong>Brand Radar<\/strong><\/a><strong>:<\/strong> Monitor when and how your brand gets cited across ChatGPT, Perplexity, Google AI features, and more. Since fan-out means you could be cited for queries you never directly optimized for, track broadly across your topic space, not just target keywords.<\/li>\n<li><strong>Use <\/strong><a href=\"https:\/\/ahrefs.com\/rank-tracker\"><strong>Rank Tracker<\/strong><\/a><strong> for topic cluster monitoring:<\/strong> Add your priority fan-out queries with decent search potential alongside your main keywords. Use tags to group related queries by topic cluster, then track aggregate performance across each cluster rather than obsessing over individual positions.<\/li>\n<li><strong>Monitor topic-level performance with <\/strong><a href=\"https:\/\/ahrefs.com\/portfolios\"><strong>Portfolios<\/strong><\/a><strong>:<\/strong> Group pages covering the same topic into portfolios representing your topic clusters. Track aggregate metrics to see if your comprehensive coverage strategy is improving visibility across the entire topic landscape, not just specific pages.<\/li>\n<li><strong>Shift your success metrics for AI visibility:<\/strong> Focus on topic-level visibility trends and citation frequency rather than individual keyword rankings. Query fan-out means a single ranking (even on competitive keywords) is not enough. Patterns across AI platforms reveal whether your content is being recognized as authoritative for your topic&nbsp;space.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1424\" height=\"1082\" class=\"wp-image-195607\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/overview-of-ahrefs-brand-radar-dashboar-displayng.png\" alt=\"Overview of Ahrefs' Brand Radar dashboar displayng performance and visibility in various AI search surfaces including AI Overviews, AI Mode, ChatGPT, Gemini and more.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/overview-of-ahrefs-brand-radar-dashboar-displayng.png 1424w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/overview-of-ahrefs-brand-radar-dashboar-displayng-559x425.png 559w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/03\/overview-of-ahrefs-brand-radar-dashboar-displayng-768x584.png 768w\" sizes=\"auto, (max-width: 1424px) 100vw, 1424px\"><\/p>\n<p>Traditional SEO metrics (rankings, traffic, conversions) remain important for measuring search performance. AI visibility metrics (citations, topic coverage, cluster-level performance) add a new dimension that complements rather than replaces traditional measurement.<\/p>\n<h2><a id=\"post-195587-_dezmws92bio\"><\/a>Final thoughts<\/h2>\n<p>Query fan-out reveals something that\u2019s been true all along: searchers care about context they rarely put into words. They mentally stack requirements and filter by implicit criteria they often don\u2019t search for directly.<\/p>\n<p>AI search handles that cognitive load through query fan-out, transforming one underspecified query into comprehensive research. For visibility in AI search, the goal isn\u2019t to rank for individual keywords or prompts; rather, it\u2019s to comprehensively cover the implicit and explicit contexts behind each search.<\/p>\n<p>To get started, choose one high-priority topic. Map its fan-out patterns, audit what you have, and systematically fill the&nbsp;gaps.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Search engines used to work one-to-one: one search query returned a unique set of results featuring pages that best matched the exact query searched. Then they evolved to many-to-one, recognizing that queries like \u201cSydney plumber\u201d and \u201cplumbing service in Sydney\u201d<span class=\"ellipsis\">\u2026<\/span><\/p>\n<div class=\"read-more\">Read more \u203a<\/div>\n<p><!-- end of .read-more --><\/p>\n","protected":false},"author":195,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[469,335],"tags":[],"coauthors":[458],"class_list":["post-195587","post","type-post","status-publish","format-standard","hentry","category-ai-search","category-general-seo","odd"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is Query Fan-Out? 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