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When your clients ask ChatGPT or Gemini one thing, the mannequin quietly fires a set of conventional internet searches within the background, retrieves the rating pages, and synthesizes the answer from those. The websites that rank for these hidden queries get cited. Those that don’t, don’t. QueryFan generates persona-specific prompts, runs them by means of each fashions, and captures the precise searches each triggered. That checklist is your actual AI visibility goal. It’s free.
Key phrases Lists Are Helpful, They Simply Miss Half The Image
Let me be exact about that earlier than anybody writes a livid reply.
I’m utilizing the time period “key phrases” to check with the “one-shot” queries that go into conventional search engines like google and yahoo. Sure, I do know we’ve been in a “semantic” world for over a decade, however let’s simply agree on terminology that everybody can comply with for now.
The first subject of “key phrase lists” in context to AI search is threefold:
- Sometimes, queries (prompts) that go into LLMs are typically longer, multifaceted, and conversational in nature. Conventional searches are typically extra slender in scope.
- Conventional search is “one-shot.” You do your search, get your info, then do one other impartial search. Queries/prompts on LLMs are typically conversational in nature and carry the context of earlier tokens.
- The mechanisms that LLMs use for internet search additionally carry personalization context. If the consumer has beforehand said they’re a vegan, they usually ask the LLM about [running shoes], it’s extremely possible the LLM will carry out a search to accommodate this.
In essence, AI search has change into a type of “common intent decoder” for customers. These massive, multifacted conversations with the AI get damaged down into subsets of solvable queries, that are run within the background as “conventional” searches on Google or Bing, with the ensuing websites used to generate a response. The method is called “Retrieval Augmented Technology” (RAG).
The optimization goal has moved. You’re now not optimizing purely for what the human sorts right into a chat field. You’re optimizing for what the AI agent quietly searches for on their behalf, within the background, with out the consumer figuring out it occurred.
These background queries are what QueryFan captures. They’re typically fairly totally different from what the consumer really requested. And they’re the precise checklist of issues you must rank for to look in AI-generated solutions.
Exhibit A: Reddit Fell Off A Cliff On A Tuesday
The scope and depth of this secret relationship grew to become clear when Reddit was enjoying meteoric visibility increases in Google, and tragedy struck on September tenth, 2026. In line with quotation monitoring information from PromptWatch, Reddit’s citation rate in ChatGPT responses collapsed virtually in a single day. It had been operating as excessive as 15% of all citations. Inside days, it was sitting beneath 2%.
The trigger was unglamorous: Google quietly removed the power to request 100 search outcomes concurrently (the num=100 parameter) from its search API on that date.
Take into consideration what this tells you. Reddit’s visibility in ChatGPT responses tracked Google’s bulk search capabilities, not something Reddit did, not a coaching information replace, not an alignment tweak. The implication is about as delicate as a dropped piano: ChatGPT was bulk-pulling Google search outcomes, Reddit dominated these outcomes on the time, and when the bulk-pull disappeared, so did Reddit’s citations.
AI search surfaces are, in large part, wrappers around traditional search. The “AI” bit is actual (the synthesis, the personalisation, the conversational coherence) however the info retrieval step is remarkably acquainted. Google indexes and ranks the online; the AI consults that index. Your content material nonetheless must rank.
How QueryFan Works
Step 1: Your ‘Conventional’ Key phrases
Your conventional key phrase checklist for the time period “trainers” could incorporate varied instructed variations of this time period, from a supply like Google Recommend.
For QueryFan, we are able to merely take the subject of “trainers” and use this as our first step, as we’re going to generate prompts round this.
Step 2: Outline Personas
Your personas are how we’re going to customise the prompts we generate. This can alter our traversal of the token area, aligning us with coaching information from the tens of millions of communities, discussion board posts, Reddit threads, and web discourse the place actual customers ask actual questions with these identities.
QueryFan sends your persona + matter mixture to the LLM to generate the sorts of questions that persona would really ask an AI software. Not key phrases. Questions. Actual, conversational, context-laden questions. For the [middle-aged vegan man who just started running] instance, it can produce issues like:
- “Which vegan trainers are good for middle-aged males simply beginning to run?”
- “The place can I purchase vegan trainers on-line within the UK?”
- “What ought to I search for when selecting my first pair of trainers as a newbie?”
Step 3: LLM Choice And AlsoAsked Enrichment
AI conversations department. Somebody who asks about vegan trainers will ask follow-up questions: about value, about manufacturers, about damage prevention. QueryFan passes the generated prompts by means of the AlsoAsked API to seize the nearest-intent follow-up questions round each. Individuals Additionally Ask information is the precise instrument right here as a result of it was constructed to mannequin query proximity, which is exactly what you want if you’re making an attempt to foretell the place a dialog goes subsequent.
For example, a search within the UK for “trainers” would floor comply with up questions on particular manufacturers, asking tips on how to decide a shoe, and even widespread medical queries.
It’s also possible to choose in the event you want to use ChatGPT, Gemini, or each. Every LLM handles and fan out queries barely in another way, so in the event you’re optimising for a selected platform it’s best to get the info from there.
Step 4: Question Fan-Out
QueryFan sends the enriched immediate checklist to GPT-5 with internet search enabled (through the OpenAI Responses API) and to Gemini with Google Search grounding lively (through the Gemini Grounding API). Each fashions, once they resolve a immediate requires present info, carry out precise Google searches behind the scenes.
This course of captures the fan-out queries as each APIs are, fairly usefully, clear about what they searched. The Gemini API returns a webSearchQueries array within the groundingMetadata discipline of each grounded response. OpenAI’s Responses API logs the precise search queries within the web_search_call output. QueryFan harvests each.
The result’s a desk: persona-specific prompts in, the precise Google search queries the AI fired out. Not what your buyer typed. What the AI looked for on their behalf. These are your new website positioning targets, and till now there was no free software that surfaces them at scale.
The Grounding Query: Not Each Immediate Triggers A Search
A short however necessary caveat earlier than you dash off to categorise all the things as an website positioning alternative.
Not every prompt causes the AI to perform a web search. The fashions decide primarily based on the consensus of token prediction as to if stay info is required.
To offer an instance, the immediate “What do crimson blood cells do?” doesn’t set off a search. The reason being there’s a very steep bell-curve of which tokens are going to look subsequent. Within the billions of coaching paperwork, the reply has stayed very secure, so an “in-model” reply can confidently be generated.
On the reverse finish of the size, a immediate akin to “What occurred within the information in the present day?” would set off an online search. There could be a really flat curve of “wtf tokens are subsequent?,” as there isn’t any “secure” reply inside the coaching information; it at all times modifications, it requires stay information. It’s one other model of the Query Deserves Freshness (QDF) idea that SEOs have used for years.
In the event you’re involved in grounding, Dan Petrovic has finished some glorious work on this space, and even released trained models on Hugging Face to foretell whether or not queries shall be grounded once they hit a confidence threshold.
QueryFan surfaces which prompts triggered searches and which didn’t. Solely the grounded ones (those that really precipitated a Google search to occur) are actionable by means of website positioning. The in-model answers are, for now, largely outside your reach. You’d have to affect coaching information to maneuver the needle there, which is a unique undertaking completely, with a for much longer horizon.
What You Do With The Outcomes
You now have a listing of precise search queries that AI instruments fireplace when answering questions out of your particular personas. Run a typical hole evaluation:
- Which of those queries do you will have content material for?
- Which do you already rank for?
- Which have zero protection, both in your web site or wherever you’re more likely to be talked about?
The primary two classes are diagnostic. The third is your motion checklist.
One necessary distinction from conventional website positioning: Your own ranking isn’t the only path to AI visibility. LLMs scan the highest 10, 20, generally 50 outcomes for a grounded question and synthesize throughout them. A trusted evaluation web site rating at place 3 is a respectable path to showing in an AI-generated reply, even when your individual area by no means makes the primary web page. Getting a product reviewed on a high-authority specialist web site, incomes a point out in a roundup article, showing in related group content material, all of those depend.
LLM visibility is a multi-site focus. This implies the hole evaluation has two outputs: content material to create by yourself web site, and placements to earn on other people’s sites.
The Punchline
Forged your thoughts again to that Reddit quotation graph. The one which fell off a cliff when Google modified a single API parameter. A completely impartial firm’s AI visibility tracked the habits of a search API it didn’t management and possibly didn’t know existed.
That’s the form of the dependency. And the implication isn’t that website positioning is lifeless; it’s virtually the alternative. website positioning is now working at one further take away: as a substitute of optimizing for the human question, you must optimize for the AI-translated question that occurs between the human and Google.
QueryFan offers you a option to see what that translation really produces. Your key phrase checklist tells you what folks typed right into a search bar. QueryFan tells you what ChatGPT and Gemini looked for on their behalf, within the background, with out anybody asking them to announce it.
These are totally different lists. The hole between them is just not a minor refinement to your content material technique. It’s the a part of AI search that no person has been measuring as a result of no person has had a free software to measure it with.
Disclosure: The creator is the creator of Queryfan.
Extra Sources:
This put up was initially revealed on Mark Williams-Cook Substack.
Featured Picture: Roman Samborskyi/Shutterstock
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