How to use ChatGPT itself to discover what your customers ask AIs — and turn it into a strategy
There's an underrated shortcut for finding out which queries you need to answer to show up in AIs: ask the AI itself. Here's the step-by-step method.
Most companies that decide to improve their AI visibility start in the wrong place: by producing content about what they think customers want to know. The result is a blog full of posts on topics the team considers important — but that real customers never ask AIs.
There's a smarter shortcut: ask the AI itself what your customers ask it. It's not a paradox — it's one of the most efficient tactics available to any company that wants to show up in the answers of generative AIs. And it's free.
The query-discovery loop: how it works in practice
The principle behind this tactic is simple: AIs were trained on massive volumes of text from the internet — including real questions people ask in forums, communities, Q&A platforms, and search engines. When you ask an AI to list the most frequent questions about a topic, it's reproducing, to a degree, patterns of what people actually ask — not inventing from nothing.
That isn't a perfect data source (it's not the same as real search analytics), but it's a surprisingly good source of initial hypotheses — especially when combined with later validation.
The loop works in four steps:
Step 1 — Map your business's core topics. Before opening ChatGPT, list the 5 to 10 topics your company has the most expertise in and that are relevant to your customers. For an insurance brokerage: life insurance, business insurance, claims coverage, plan comparison. For a software agency: MVP, mobile development, choosing a tech stack, IT project management. Those topics are the starting point.
Step 2 — Use structured prompts to extract questions. For each topic, use prompts like:
"What are the 15 most frequent questions someone researching [topic] asks a generative AI? Focus on questions from people making a purchase or hiring decision, not from people who are already experts on the subject."
"Give me the 10 questions a [ideal customer profile] asks ChatGPT when deciding on [decision relevant to your product]."
"Which questions about [topic] have a different answer depending on the customer's segment or situation? List the questions and the variables that change the answer."
Step 3 — Filter and prioritize the questions by conversion potential. Not every question that comes back has equal strategic value. Awareness-stage questions ("what is X") build an audience over the long run. Decision-stage questions ("how to choose between X and Y," "what are the criteria for hiring X," "when is X the best option") convert more directly. Prioritize a mix of both, weighted toward decision questions.
Step 4 — Validate with real data before producing. The questions the AI suggests are hypotheses. Before producing content, validate using Google Search Console (to see what already generates impressions on your site), Google Trends (for relative volume), and, if available, tools like Semrush or Ahrefs (for search volume). Questions the AI raises AND that have confirmed search volume are the ones that deserve priority content.
How to turn the questions into content AIs cite
Discovering the questions is half the work. The other half is structuring the content so AIs can extract the answer precisely.
There's a principle we call BLUF (Bottom Line Up Front): the direct answer to the question should appear in the first paragraph, not after three paragraphs of context. Generative AIs extract the beginning of pages more often than the middle or the end — a text that reaches the answer after a long intro loses most of its potential citations.
The ideal structure for a post built around a specific question:
- Title = the question (or a direct version of it)
- First paragraph = the direct answer, no throat-clearing
- Body = the explanation, with context, nuance, and examples
- A cases section with variations of the answer depending on the reader's profile
- A conclusion with the synthesis and next steps
This format works well for both traditional SEO (featured snippets) and AI citation — which makes it doubly valuable.
The question map as an editorial-planning tool
Once you've extracted 40 to 60 questions from the AIs and filtered them by potential, you have a 6-to-12-month editorial map with clear prioritization. That solves a recurring problem for content teams: knowing what to write next.
Organize the questions into thematic clusters. Each cluster can become a series of posts or a pillar page with subpages. Within each cluster, the publishing sequence goes from the most basic ("what is X") to the most specific ("how to do X in context Y"). That creates a content architecture AIs recognize as comprehensive and authoritative on the topic.
The competitive edge: your competitor isn't doing this
Most mid-market marketing teams still plan content by instinct, by an agency-suggested calendar, or by copying what a competitor publishes. Very few are using the AI itself as a demand-discovery tool — which means the questions your customers ask AIs are being answered, today, by the competitors who got there first.
The loop described in this post — discover the questions, produce content for each one, measure whether you're showing up in the AIs — is the continuous-improvement cycle of AI visibility. And the first company to run that cycle in each niche tends to dominate that space for a considerable time, because the accumulation of indexed content and citation history creates a compounding advantage.
How Crowly can help you close the loop
The discovery loop described in this post has a critical gap without a measurement tool: how do you know whether the content you produced is actually showing up in the AIs when those questions are asked?
That's exactly where Crowly comes in. The platform monitors how often your brand shows up in the answers of ChatGPT, Gemini, and Perplexity for your sector's queries — and lets you track the trend week over week. When you publish a post answering a specific question and two months later your Crowly score rises for that query, you have evidence the content worked.
Without that measurement, you're producing content in the dark. With it, you have a learning cycle that improves each round.
Measure whether your content is showing up in the AIs. Free diagnostic, results in 2 minutes. Analyze my brand →
Three prompts to start now
Prompt 1 — General discovery: "What are the 20 most frequent questions a [your ideal customer profile] asks ChatGPT when researching [problem you solve]? Organize them by journey stage: awareness, consideration, decision."
Prompt 2 — Comparison questions: "What comparison questions does someone ask ChatGPT when deciding between [your product/service category] and alternatives? Include questions like 'what's the difference between,' 'when is it more worth it,' 'what to consider when choosing.'"
Prompt 3 — Segment questions: "If I'm a [your ICP's role] at a [size/sector] company, what specific questions would I ask an AI about [relevant topic]? Be specific about the concerns this profile has that a generic profile wouldn't."
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