- \n
- AI systems break one prompt into multiple backend queries<\/strong> across criteria like pricing, integrations, comparisons, reviews, and use-case fit before generating a response.<\/li>\n\n\n\n
- Most fan-out queries <\/strong>have zero measurable search volume, so traditional keyword data alone won\u2019t reflect real AI retrieval behavior.<\/li>\n\n\n\n
- To optimize for query fan-out, start with priority prompts, not head terms.<\/strong> Define 20\u201350 realistic, multi-factor prompts using search demand + ICP framing + real buyer conversations.<\/li>\n\n\n\n
- Extract real fan-outs using tools <\/strong>to see actual retrieval branches.<\/li>\n\n\n\n
- Map fan-outs against your site.<\/strong> Check whether each underlying query is clearly and explicitly addressed.<\/li>\n\n\n\n
- Prioritize commercially relevant gaps.<\/strong> Focus first on evaluation, pricing, integrations, comparisons, and use-case fit.<\/li>\n\n\n\n
- Search volume still matters, but it\u2019s no longer enough.<\/strong> A topic can look complete in SEO tools while still missing key retrieval layers in AI-driven environments<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
What Is Query Fan-Out?<\/h2>\n\n\n\n
You open your favorite LLM and type:<\/p>\n\n\n\n
\u201cBest CRM for early-stage SaaS with automation and Slack integration.\u201d<\/p>\n\n\n\n
Within a few moments, you get a structured and comprehensive answer. It references specific tools, mentions pricing considerations, compares features, and even highlights integrations. This is not the result of a single web search. <\/p>\n\n\n\n
Instead, the AI performs a query fan-out: it breaks your prompt into multiple underlying search queries and runs each separately.<\/p>\n\n\n\n
Your single prompt may be decomposed into queries like:<\/p>\n\n\n\n
- \n
- CRM for early-stage SaaS<\/li>\n\n\n\n
- CRM automation capabilities<\/li>\n\n\n\n
- CRM Slack integration<\/li>\n\n\n\n
- CRM pricing tiers<\/li>\n\n\n\n
- CRM comparisons or alternatives<\/li>\n<\/ul>\n\n\n\n
Each of those represents a distinct angle or constraint embedded in your original question. The system retrieves sources that best satisfy each of those angles and then synthesizes a response across them.<\/p>\n\n\n\n
This process is not visible to you as the user, and you see only one answer, but behind that answer, the LLM is segmenting the intent of your prompt into separate decision dimensions.<\/p>\n\n\n\n
What Does This Mean for SaaS Marketing?<\/h3>\n\n\n\n
Query fan-out research tells marketers that prompts are getting longer and more complex<\/a>, and that according to Seer Interactive<\/a>, 95% of fan-outs generated by Gemini have no search volume.<\/p>\n\n\n\n
That means if you have been optimizing the old-SEO way, you will be missing content topics and structure that LLMs want. For your product to be mentioned and cited as a solution in relevant prompts, your content needs to clearly and explicitly address multiple criteria at once.<\/p>\n\n\n\n
That is, you need to write specific, contextual content to appear in LLMs for a multi-layered prompt like \u201cBest CRM for early-stage SaaS with automation and Slack integration.\u201d<\/p>\n\n\n\n
Does that mean producing ten versions of the same article? Not really, but it does mean your coverage has to cover topics you may not have targeted before, and your process for identifying new topics (or refreshing old ones) needs to include fan-out research.<\/p>\n\n\n\n
![\"query](\"https:\/\/singularity.digital\/wp-content\/uploads\/2026\/03\/Img-01-1024x614.png\")
<\/figure>\n\n\n\n<\/p>\n\n\n\n
How to Identify and Extract Fan-Outs Around Priority Prompts<\/h2>\n\n\n\n
1. Generate Relevant Prompts <\/h3>\n\n\n\n
Start by brainstorming prompts a real buyer might type onto ChatGPT, Google AI, or another LLM to research tools in your category. <\/p>\n\n\n\n
To come up with multi-factor-layered prompts like these for your own product: <\/p>\n\n\n\n
- \n
- Look at search data to understand which broad topics have traction in your space. <\/li>\n\n\n\n
- Narrow that topic through the lens of your ICP. Ask yourself: who is this for? An early-stage founder? A RevOps lead? A 50-person SaaS team? The same topic will turn into very different prompts depending on who is searching.<\/li>\n\n\n\n
- Use insight from your marketing persona work, sales team and support tickets to understand what factors influence decisions and what questions surface most often. <\/li>\n\n\n\n
- Layer in those decision factors that influence whether someone chooses your product \u2013 pricing thresholds, required integrations, feature depth, use-case fit, competitors, etc.<\/li>\n<\/ol>\n\n\n\n
Once you\u2019ve gathered all this data, stitch this information into prompts. aim at ~15 words long and don\u2019t be afraid to go longer \u2013 unlike short Google strings, AI searchers are increasingly prompting with more than 25 words<\/a>. For example, \u201cBest CRM for early-stage SaaS with automation and Slack integration\u201d or \u201cCRM under $50 per month for small SaaS teams with reporting features\u201d.
You can do this manually, or with AI by using a template like this:<\/p>\n\n\n\n\n
You are an [ICP \u2014 describe role, company size, industry, stage, budget context].<\/p>\n\n\n\n
You are [core topic \u2014 describe what this person is trying to evaluate, replace, compare, or improve within your product category].<\/p>\n<\/blockquote>\n\n\n\n
Here are important decision factors for you:
\u2013 Pricing concerns: [budget limits, contract preferences, pricing structure sensitivities]
\u2013 Required integrations: [tools this persona already uses or insists on connecting]
\u2013 Key features: [specific capabilities they care about most]
\u2013 Common objections or friction points: [recurring concerns, risks, or deal blockers]
\u2013 Competitor comparisons (optional but recommended): [tools frequently compared against yours]<\/p>\n\n\n\nBased on this information, generate [X number] realistic, full-sentence prompts this buyer would type into ChatGPT or Google AI when actively evaluating or comparing {your product category}.<\/p>\n\n\n\n
Remember, you don\u2019t need to target every brainstormed or suggested prompt. Narrow down your initial list to the queries that most closely align with your target buyer and product positioning.<\/p>\n\n\n\n
2. Extract Real Fan-Out Queries With a Fan-Out Tool<\/h3>\n\n\n\n
You\u2019ve generated and identified the prompts that you want your brand to appear for in AI conversations.<\/p>\n\n\n\n
Now you need to see how those prompts are being broken down behind the scenes so you can optimize for the underlying fan-outs that LLMs like Gemini<\/a> and ChatGPT generate when grounding against the web.<\/p>\n\n\n\n
Note that these tools aren\u2019t interchangeable. As per recent research, Gemini (which powers Google\u2019s AI Overviews and AI Mode) generates way more fan-outs per prompt – up to 28<\/a> in some instances, while ChatGPT typically generates fewer, around 2 to 4.<\/a> So you will have to approach each LLM separately. <\/p>\n\n\n\n
Here\u2019s how to pull fan-outs from the back-ends of these LLMs:<\/p>\n\n\n\n
For Gemini\/Google\u2019s AI Mode:<\/h4>\n\n\n\n
Dan Petrovic built a Query Fan-Out<\/a> tool that uses Google\u2019s own grounding system via the Gemini API. Under the hood, it pulls the webSearchQueries field \u2013 which contains the exact backend queries Google executes when generating AI responses.<\/p>\n\n\n\n
Instead of writing your own API scripts, this tool surfaces that data directly from the \u201cAI Mode Prompt\u201d you put into the tool\u2019s interface. To use it, simply add your prompt and hit \u201cExtract Fan Out Queries\u201d.<\/p>\n\n\n\n
![\"Dejan](\"https:\/\/singularity.digital\/wp-content\/uploads\/2026\/03\/image-11-1024x461.png\")
<\/figure>\n\n\n\nYou can see that when the prompt \u201cWhat\u2019s the best CRM for early-stage SaaS with automation and Slack integration\u201d is entered, it returns multiple distinct retrieval branches \u2013 including comparison queries, Slack-focused variants, review-focused angles, and year-modified searches.<\/p>\n\n\n\n
These are the actual queries Gemini runs when grounding.<\/p>\n\n\n\n
For ChatGPT: <\/h4>\n\n\n\n
Fan-out data in ChatGPT is retrievable, but doing it manually requires developer tools, so we again suggest using a tool. Something like ChatGPT Search Capture<\/a>, which surfaces ChatGPT\u2019s retrieval-level data directly, is a good option. It shows the queries executed behind the scenes, the URLs cited, brand mentions, and other retrieval signals.<\/p>\n\n\n\n
![\"query](\"https:\/\/singularity.digital\/wp-content\/uploads\/2026\/03\/query-fan-out-tool-screenshot-1024x505.png\")
<\/figure>\n\n\n\nHere\u2019s how to do it:<\/p>\n\n\n\n
- \n
- Install the ChatGPT Search Capture extension.<\/li>\n\n\n\n
- Run one of your priority prompts in ChatGPT with browsing enabled.<\/li>\n\n\n\n
- Use the extension to:\n
- \n
- List the query fan-outs executed<\/li>\n\n\n\n
- Extract all cited URLs<\/li>\n\n\n\n
- Identify brand mentions
<\/li>\n<\/ul>\n<\/li>\n\n\n\n - Export the results into TSV or Excel format.<\/li>\n\n\n\n
- Review which retrieval branches appeared and which sources were cited.<\/li>\n<\/ul>\n\n\n\n
For other LLMs<\/h4>\n\n\n\n
As of early 2026, there aren\u2019t widely available tools that surface this data as easily for other LLMs like Claude as the Gemini or ChatGPT solutions discussed above. To expose and access similar retrieval or query fan-out data for them, you\u2019d have to inspect API responses or write custom scripts. So if you want to analyze fan-outs in those systems, expect to involve a developer.<\/p>\n\n\n\n
3. Map Extracted Fan-Out Queries Against Existing Site Content<\/h3>\n\n\n\n
Once you\u2019ve extracted the fan-out queries, the next step is to check whether your site actually answers them.<\/p>\n\n\n\n
Go through each fan-out and think: if this exact query were run in isolation, do you have a page that clearly addresses it? It\u2019s not enough that the topic is loosely mentioned somewhere. The coverage needs to be explicit and structured enough that an AI system can confidently retrieve it as a relevant source. <\/p>\n\n\n\n
You can do this manually, but tools like Domain Coverage Analyzer<\/a> make the process much faster. You upload your list of fan-out queries, and the tool scans your site to classify coverage into categories such as:<\/p>\n\n\n\n
- \n
- No Coverage<\/li>\n\n\n\n
- Clear Gap<\/li>\n\n\n\n
- Related Gap<\/li>\n\n\n\n
- Aligned<\/li>\n\n\n\n
- Perfect Coverage<\/li>\n<\/ul>\n\n\n\n
![\"\"](\"https:\/\/singularity.digital\/wp-content\/uploads\/2026\/03\/image-12.png\")
<\/figure>\n\n\n\n
- Most fan-out queries <\/strong>have zero measurable search volume, so traditional keyword data alone won\u2019t reflect real AI retrieval behavior.<\/li>\n\n\n\n