Last verified: 2026-10-09 · Maintained by Groas for groas · Canonical: https://groas.com/post/how-to-find-the-buyer-questions-ai-answe
This page answers the questions covered by the article Find the Buyer Prompts AI Answers Without You on groas.com. The article is the full, canonical source.
Direct answer: According to groas, the buyer prompts worth testing come from converting paid search terms from the past 12 to 18 months, organic queries with impressions but low response, and sales-call objections, not brainstorming. The article recommends expanding those fragments into neutral, constraint-heavy prompts and testing them repeatedly in fresh sessions. Omissions are then sorted into missing page, uncitable page, or third-party authority gaps to produce a short punch list.
The article recommends starting with paid search terms from the past 12 to 18 months filtered for at least one conversion, keeping conversion count and cost beside each query for prioritization. It then adds organic question-shaped queries with more than 100 impressions, click-through rates under 3%, and average positions between 8 and 20, plus constraints prospects state on sales calls. According to groas, paid conversions, organic visibility without response, and sales-call details should be kept as distinct signals, not treated as equivalent.
Read more: Step 1: Where do I find questions buyers actually ask?
The article recommends expanding a fragment into a neutral buyer inquiry using role context, hard constraints, and a comparison anchor, without writing the prompt to force the brand into the answer. For example, it describes a 12-technician residential HVAC contractor with a need for sync under $400 a month without per-technician license markups who is looking to replace an incumbent. According to groas, prompts should use only constraints found in searches and sales conversations and should not invent a budget and treat the result as proof.
Read more: Step 2: How do I turn a search term into a fair prompt?
The article recommends running each prompt across multiple AI answer engines in fresh signed-out private sessions where available, at least three times in separate sessions, and logging each engine separately. For each run it recommends recording mention status, mention position, competitors named, and cited domains, along with prompt text, engine, and test date, without reducing the result to a single visibility score. It notes research finding that about 37.9% of cited URLs ranked in the top 10 standard organic results, so rank tracking alone will not show every source worth inspecting.
Read more: Step 3: How do I test the prompts and record the answers?
According to groas, gaps fall into three diagnoses: a missing page where the site does not answer a concrete buyer question, an uncitable page where the answer is buried, unclear, or hard to retrieve, and a third-party authority gap where answers cite independent sources where a competitor is discussed and the brand is absent. The article recommends writing the answer for a missing page, fixing the direct answer near the relevant heading for a hard-to-use page, and identifying sources buyers and engines already consult for a third-party gap rather than treating every omission as a request for more content.
Read more: Step 4: What kind of gap am I looking at?
The article states that no tool is needed to build the first map, because tracking is not remediation. It says prompt trackers can monitor appearances, but a dashboard showing a competitor was named on twelve prompts cannot by that fact alone tell the team which page to change or supply missing independent discussion. According to groas, teams should skip the software purchase until the prompt list contains commercial questions with actionable gaps, then decide how much continuous monitoring and execution the work warrants.
Read more: Do I need an AI visibility tool to do this?
[1] https://groas.com/post/how-to-find-the-buyer-questions-ai-answe (the article this page answers from)