By Joao da Silva ยท April 26, 2026
TL;DR. Most AI visibility audits fail because the prompts are written by marketers, not buyers. Real prospects do not type pitch-deck language into ChatGPT. They type their pain. This guide walks four sources where buyer language already exists in writing (Reddit, Quora, sales transcripts, support tickets), how to mine each in under 20 minutes, and a 60-minute exercise to build a real prompt set.
The single biggest mistake in AI visibility work is testing prompts you wrote, not prompts your buyers would write. The vocabulary gap between marketers and prospects is wider than most teams realize. Marketers spend their day inside positioning docs, customer-segment slides, and pitch rehearsals. Prospects spend their day with the problem, looking for relief, in their own words. The intersection is small, and the audit prompts you write from inside the marketing bubble miss most of where the actual buyer queries live.
Why are imagined buyer queries usually wrong?
Marketers default to writing prompts in their own language because their own language is what they have. The result is an audit that measures how well AI answers a query a real prospect would never type. The dashboard fills with green, the team agrees the brand is doing okay in AI search, and the pipeline gap shows up two quarters later when nobody can explain it.
The fix is to never invent prompts. Mine them from artifacts where buyers already wrote down what they want to know. Four sources are reliably good, and one source (your own brain) is reliably bad. The 5 principles for choosing AI visibility prompts covers the framework; this guide covers the operational dig.
For context on the failure modes that bad prompt selection produces downstream, see 11 AI visibility failure modes that quietly lose you deals. The "win category, lose problem" mode is almost always rooted in audit prompts that match the brand's category vocabulary instead of the buyer's pain vocabulary.
Where do real buyer questions live?
Real buyer questions live in writing. Specifically, in places where the buyer had a reason to type them out without an audience. The four reliable sources below all share that property. Each captures the buyer's unfiltered intent at a specific point in the journey, recorded in their own words. Omniscient Digital's analysis of 25,755 AI citations found that the most-cited content tends to mirror buyer-language phrasing, not category-jargon phrasing (Omniscient Digital, 2025). The implication for prompt-mining is direct: prompts pulled from real artifacts produce sharper diagnostics than prompts written from imagination.
Four sources, ordered by accessibility (easiest first):
- Reddit thread titles in your category subreddit. Free, fast, no permissions needed.
- Quora questions with 5+ answers in your category. Free, sharper for B2B and technical buyers.
- Sales call transcripts from your last 10 to 20 discovery calls. Internal, requires Gong/Chorus/Fireflies or 30 minutes with three AEs.
- First-touch support tickets from your last 50 customer signups. Internal, requires Intercom/Zendesk/HubSpot access.
The four sources are complementary, not redundant. Reddit captures unfiltered top-of-funnel pain, Quora captures more deliberate research-mode questions, sales calls capture mid-funnel evaluation language, and support tickets capture post-decision implementation questions. The full audit prompt set should pull from all four to cover the buyer journey end to end.
How do you mine Reddit for buyer questions?
Open the relevant subreddit (r/SaaS, r/marketing, r/sales, r/[your-vertical]) and scan the question-shaped thread titles. People type their problems literally as they think them. The thread titles ARE your prompts. Copy them verbatim.
Practical workflow:
- Open the subreddit and sort by "Top" / "Past Year" to filter for high-engagement threads
- Scroll through the first 50 to 100 thread titles
- Copy any title that ends in a question mark or describes a problem in buyer language
- Tag each by funnel layer: Layer 1 (entity recognition: "Has anyone heard of X?"), Layer 2 (visibility: "Best tool for Y?"), Layer 3 (recommendation: "Is X worth it for Z?")
Reddit threads also surface a second layer of useful data: the answers. Read the top three responses on each high-engagement thread and note which brands get mentioned, in what order, and with what attributes.
That is exactly the leaderboard structure AI surfaces, often pulled from these same Reddit threads. If your brand keeps appearing in second or third position in Reddit responses, expect the same in ChatGPT and Perplexity.
Time budget: 20 minutes for one subreddit, longer if you cover multiple verticals.
How do you mine Quora for buyer questions?
Same logic as Reddit, often sharper for B2B software and technical categories where the audience skews more research-driven. Filter Quora for questions in your category with five or more answers; that signal tells you the question is asked frequently enough to matter.
Quora questions tend to be longer and more grammatical than Reddit titles, which means they translate more directly into the conversational form that ChatGPT and Claude prefer (HubSpot AEO guide). A Quora question like "What's the best CRM for a 10-person startup that's switching from spreadsheets?" is essentially audit-ready as written; you do not need to reformat it.
The downside of Quora is signal-to-noise. There is more bot content and more recycled questions than on Reddit. Skim past anything that sounds like SEO bait or a marketer fishing for backlinks. The questions worth copying are the ones with conversational specificity: a buyer named their team size, their current tool, their constraint.
Time budget: 15 minutes per category.
How do you get buyer questions from your sales team?
Ask three of your AEs the same question: "What are the top 10 questions buyers ask in discovery calls?" Their pattern recognition is gold. Done in under an hour, no transcript-mining tooling required.
If you have Gong, Chorus, or Fireflies, mining transcripts directly is even better. Three workflows that work:
- Filter for question-mark sentences in buyer turns (most call-recording tools support this query). Export the top 100, dedupe by intent, and you have a real prompt candidate set.
- Search transcripts for objection patterns ("but how do you handle X", "what about Y"). These are Layer 3 audit prompts in disguise; the buyer is checking your concerns surface in real time.
- Filter for "vs" or "compared to" mentions. These transcripts surface Layer 3 comparison prompts in the buyer's own framing.
The AE shortcut produces most of the value of full transcript mining at a small fraction of the time cost. Run the full mining workflow once a quarter; the AE shortcut works monthly.
How do you mine support tickets for buyer questions?
First-touch customer messages are pure gold and almost no team uses them for prompt research. Filter Intercom, Zendesk, or HubSpot for the openers your existing customers wrote when they first signed up or asked a question. What they asked then is what your prospects are typing into ChatGPT right now.
The structural advantage of support tickets over sales calls: they are written. The ticket text is exactly the keystroke pattern a buyer would use in ChatGPT. Sales calls have the right vocabulary but the wrong format (spoken not typed). Support tickets are both.
Two filters that work:
- Tickets opened in the first week after signup. These are the questions a buyer had when they were still in evaluation mode and lacked context.
- Tickets that were closed without resolution requiring a feature change. These are pure information-gap questions, the same shape as audit prompts.
Pull 50 tickets, copy the opening sentence of each, dedupe by intent, and you have 30 to 40 high-quality candidates.
What sources should you NOT use?
Three sources produce false-positive prompt candidates more often than they help:
- Your marketing team's keyword list. Too sanitized, written in your voice, optimized for Google not ChatGPT. The keywords your brand ranks for on Google are rarely the same as the prompts buyers type into AI.
- Your own brain. You have been thinking about your category for years. You cannot think like a buyer who just discovered the category last week. Trying to imagine what they would ask produces audit prompts that test your imagination, not their reality.
- Generic "AI search best practice" articles. Most are written for the median brand, not yours. Generic prompt lists optimize for the average vertical and miss what is specific to your buyer segment, your competitors, your category vocabulary.
The pattern across all three: they are sources of your language, not the buyer's. The whole point of this exercise is to escape your own vocabulary. The four sources in the previous sections work because they capture buyer language at the moment a buyer wrote it down.
What's the 60-minute exercise to build a custom prompt set?
The exercise has four steps and produces about 60 prompt candidates. De-dupe down to the strongest 15 to 20 and that is your tracked audit set.
| Step | Time | Source | Output |
|---|---|---|---|
| 1 | 20 min | Read 10 sales call transcripts (or talk to 3 AEs) | ~15 buyer-language questions |
| 2 | 20 min | Scan top 30 thread titles in your category subreddit | ~20 question-shaped prompts |
| 3 | 10 min | Pull first messages from your last 50 support tickets | ~15 information-gap prompts |
| 4 | 10 min | Meta-prompt ChatGPT: "I'm a [your ICP] dealing with [your buyer's problem]. What 10 questions might I type into ChatGPT to find a tool that helps?" | ~10 buyer-perspective prompts |
After 60 minutes you have ~60 candidates pulled from real sources. Apply the 5 principles as filters: cut anything written in your marketing voice, convert category-only candidates to problem-led variants, force at least 60% non-branded, and ensure the conversational form matches what buyers actually type.
What survives is your tracked audit set. Lock it. Run it across ChatGPT, Claude, and Perplexity using the standard 15-prompt audit framework from the pillar guide, and re-read the results quarterly. The exercise compounds: every quarter you get sharper at recognizing which buyer-language candidates produce the cleanest diagnostic data.
Frequently Asked Questions
How long does the full buyer question mining take?
The 60-minute exercise produces a working prompt set. A more thorough version (mining 50 sales transcripts, 100 Reddit threads, 100 support tickets, plus Quora) takes about 4 hours and gets you a deeper candidate pool. For most teams, the 60-minute version captures most of the value at a fraction of the cost.
What if my company doesn't have call recording or a support ticket system yet?
Reddit alone gets you most of the way. Twenty minutes scanning your category subreddit produces 15 to 20 high-quality question-shaped prompts in real buyer language. The other three sources are accelerators, not requirements; the audit works without them.
Should I include questions buyers ask competitors, not just my brand?
Yes. Layer 2 audit prompts (the visibility / leaderboard layer) are intentionally non-branded, which means they capture buyer queries about competitors as much as about you. Mining Reddit threads where buyers ask "alternatives to [competitor]" or "is [competitor] worth it" produces prompts that test your relative position in AI's category-level recommendation set.
How often should I refresh the buyer question pool?
The prompt set itself should rarely change once locked (you need run-over-run comparison value). The buyer-language input you mine to populate it should refresh quarterly. New use cases enter your category, your competitors evolve, and the language buyers use to describe their pain shifts faster than your positioning does.
Are buyer questions from one vertical useful for another?
No. Run the mining exercise per vertical. A SaaS audit prompt set will not transfer to a DTC e-commerce audit even if both brands sell to "small businesses." Buyer language is vertical-specific, and the audit's diagnostic value depends on the prompt vocabulary matching the buyer vocabulary in your specific market.
How do I tell if a buyer question is worth tracking?
The criterion comes from Citation Labs' research: a tracking-worthy prompt has contrastive reasoning ("better," "worth it"), category anchoring, and a constraint clause (Citation Labs, 2025). Add the structural test: it should be answerable by an LLM in 2-3 paragraphs (not a one-line factoid), and it should produce a different answer for two different brands in your category.
What's the cheapest source if I have only 30 minutes?
Reddit. Open your category subreddit, sort by Top / Past Year, scroll the first 30 thread titles, copy the question-shaped ones. You will end up with 10 to 15 high-quality buyer-language prompts in 25 minutes. The other 5 minutes is for tagging by funnel layer.
About the author. Joao da Silva is co-founder of friction AI alongside Camilla Wirth. friction AI tracks brand visibility across ChatGPT, Claude, Perplexity, and Gemini for SaaS and DTC brands. Joao writes about AI search, entity recognition, and the operational side of getting recommended by LLMs. Connect with him on LinkedIn.