You know AI search matters. You have probably typed your own category into ChatGPT, watched a competitor get named instead of you, and felt that quiet drop in your stomach. So you want to start tracking. Good instinct. But then you hit the first real question: track what, exactly?
That is the part nobody hands you. Before you can measure a single mention or citation, you need a list of the actual questions buyers ask AI about your space. This playbook shows you how to find AI search prompts your buyers really type, from the highest-signal sources, so you can build an AI prompt tracking list you trust. We will keep it to the discovery work: how to gather, sort, and trim. Scoring, journey analytics, and deep internal-data mining each get their own guide.
Take a breath. You are closer to a real list than you think. By the end, you will know how to discover AI prompts to track from sources you already have access to, and how to trim them into something you can manage. Let's build it one step at a time.
Understand why prompts, not keywords, are the unit now
In old-school SEO, you picked keywords and chased a ranking position. A keyword came with a search volume and a leaderboard. That world was tidy.
AI prompt discovery for brands works differently. The thing you track is not a keyword; it is a full question a person types into ChatGPT or Perplexity. There is no volume number attached. There is no ranking. There is a prompt, and either the engine names you in the answer or it does not.
That shift is why you cannot just export a keyword report and call it done. Keywords are a starting seed, not the finish line. The language buyers use with AI is longer, more conversational, and often invisible to your usual tools.
It helps to remember the scale you are stepping into. Roughly 700 million AI conversations happen every week across the major consumer tools, and the visitors those answers send tend to convert at higher rates than traditional organic search traffic. That is a lot of buying intent flowing through questions you currently cannot see. AI prompt discovery for brands is simply the work of making those questions visible so you can decide which ones to defend.
There is also good news buried in the difficulty. Because so few teams have done this well, a focused effort to find AI search prompts your buyers actually use puts you ahead of most of your category fast. You do not need a perfect list. You need a real one.
How to tell you have this right: you stop thinking in two-word terms ("project management software") and start thinking in whole questions ("what is the best project management tool for a small remote team?").
Where people go wrong: they treat the two as the same and paste a keyword list straight into a tracker. The prompts that result sound like a search engine, not a human. Real buyers do not talk that way to AI.
Map the five prompt types before you collect anything
Here is the mistake almost everyone makes. They track "best X" prompts and stop. That covers one narrow slice of how buyers actually ask, and it leaves you blind everywhere else.
A balanced list covers five prompt types. Sketch these five buckets on paper first, because you will sort every prompt you gather into one of them.
- Informational. The buyer is learning about a problem, not shopping yet. "What causes high churn in SaaS?" Being cited here shapes how people define the problem before they ever compare vendors.
- Comparative. The buyer is weighing options. "Best accounting software for freelancers," "Shopify vs WooCommerce." This is the crowded category where most tracking piles up.
- Instructional. The buyer wants a process. "How to set up email automation for an ecommerce store." Often overlooked, high intent, and rich with citations.
- Brand-specific. The buyer names you or a rival directly. "Is [Brand] worth it?" Track these on their own, because when your name is in the prompt, a mention is nearly guaranteed and it will distort your category numbers if you blend them in.
- Transactional. The buyer knows what they want and is deciding where to act. "Best running shoes under $100 near me." Great for ecommerce and local, easy to deprioritize for most B2B SaaS.
There is a sixth pattern worth watching: generative prompts, where someone asks the AI to make something ("write me a content brief for X"). Visibility there is a different game, so give it its own line if it fits your product.
Now add one more label to each prompt: the buyer-journey stage. Awareness prompts use symptom language ("why are my conversions dropping?"). Consideration prompts use option language ("best X for Y"). Decision prompts use choice language ("which X is right for my situation," "is X worth it?"). Tracking across all three tells you whether you show up when buyers frame the problem, build a shortlist, and make the final call.
How to tell you have this right: your empty template has five type buckets and three stage labels, ready to receive prompts.
Where people go wrong: they overweight comparative prompts and never notice the awareness-stage gap where shortlists are quietly formed.
Mine your first-party sources first
Start where the signal is strongest: your own buyers, in their own words. This is the highest-signal source there is, because the language is your market's, not a panel's or a tool's guess.
Pull from these, verbatim:
- Sales-call and discovery-call notes. The exact questions prospects ask in demos. Whatever records your calls is a goldmine of unedited buyer language.
- Support tickets and chat logs. The "I can't figure out how to..." questions that map straight to instructional prompts.
- Sales enablement docs. Battlecards and objection-handling sheets are prompts your team already answers out loud.
- Onboarding and product Q&A. First-week questions reveal the "how do I..." prompts.
- Win/loss interviews. Direct quotes about what buyers were evaluating are literal prompt material.
You do not need to process all of this now. A focused extraction across sales calls and support is its own deeper project, and it deserves its own dedicated pass. For today, skim for the recurring questions and copy them down in the buyer's phrasing.
How to tell you have this right: you have 15 to 30 questions written in real human language, each tagged with a type and stage.
Where people go wrong: they polish the wording. Do not. "How do I stop my emails going to spam" is the prompt, not "email deliverability optimization."
Listen where your buyers talk to each other
Next, go to the places buyers talk without you in the room. This is second-party signal: community and reviews, in the buyer's own language.
- Reddit. Subreddits in your category are a live feed of buyer questions. Read thread titles and top comments for the exact phrasing.
- Review sites. G2, Capterra, and TrustRadius reviews are full of decision language. The "why did you choose this over alternatives?" and "what problems does it solve?" sections are gold.
- Niche forums and communities. Industry Slack and Discord groups surface prompts nobody posts on Reddit.
- Quora and Q&A sites. Lower volume, but useful for long-tail questions.
Here is a pro tip worth its own callout. If your buyers use Perplexity, Reddit is not optional. Perplexity leans heavily on community content when it builds answers, so mining Reddit is the single highest-signal way to predict what that engine will reward. Skipping it is one of the most common discovery mistakes.
How to tell you have this right: you can point to specific threads and review quotes behind at least a third of your prompts.
Where people go wrong: they lift generic forum chatter that has nothing to do with a buying decision. Stay close to questions a real prospect would ask.
Pull public search signals to fill the gaps
Your first-party and community mining will be strong but uneven. Public search data fills the stages your calls do not cover.
- People Also Ask. Mine the PAA boxes under your core terms. PAA is the closest public mirror of real question language.
- Keyword tools with a question filter. Run your terms through your SEO tool and filter for question modifiers: how, what, why, which, vs, alternatives. Those map cleanly to prompts.
- Google AI Overviews and AI Mode. Search your core terms and note which ones trigger an AI answer. Those queries are prompts, and their cited sources show you the language patterns engines favor.
- Question aggregators. Tools like AnswerThePublic group question-format queries by intent for quick breadth.
Keep this in proportion. Public search data is noisier than your first-party sources, and expanding seeds into full prompt trees is a technique that deserves its own walkthrough. For now, use it to patch obvious holes, not to define the whole list.
How to tell you have this right: every journey stage now has prompts, including the awareness questions your sales calls rarely surface.
Where people go wrong: they let the keyword tool run the show and end up with a list that reads like search strings, not spoken questions.
Ask the AI engines directly, then check the competition
Now use the engines themselves as a discovery surface. This is fast, and it catches prompts your manual mining missed.
Open ChatGPT and Perplexity and ask a simple question: "What are the top 20 questions a [your ideal customer] would ask before buying [your product type]?" Treat the output as candidates to validate, not gospel. The models are guessing at buyer language, so weigh their suggestions against the real quotes you already gathered.
Then flip it around and look at your rivals. Find which competitor pages get cited most for prompts in your space. The questions that trigger those citations are, in effect, a ready-made starter list. Reverse-engineering competitor citations is one of the highest-leverage moves in discovery, because it tells you exactly which prompts are already sending attention to someone else.
This is also where an AI visibility platform earns its place. Tools built for this surface prompt universes and trending queries at a scale no human can mine by hand. DeepSmith fits here in two specific ways. Its Discover Prompts feature generates a starter prompt set from your product, persona, and buyer-stage context, which is the fastest path from a blank page to a working list. It pairs with your first-party mining rather than replacing it. Separately, its competitor citations view shows which pages AI actually cites for your prompts, whose pages are winning, and how each competitor performs by platform, so you can confirm your list maps to answers you can realistically influence.
How to tell you have this right: you have a raw, deduplicated pool of roughly 80 to 150 candidate prompts spanning all five types and three stages.
Where people go wrong: they take the AI's generated list at face value and never cross-check it against how real buyers phrase things.
Tag every prompt by engine, because ChatGPT and Perplexity are not one channel
Before you trim, add one more tag: which engine each prompt is really for. This step saves you from a costly assumption.
ChatGPT and Perplexity do not answer the same way. Published audits of common queries found ChatGPT leaning hard on Wikipedia and authoritative editorial sources, with Wikipedia making up close to half of its top citations. Perplexity leaned just as hard on Reddit, which supplied a similar share of its top citations. And the overlap between the two is thin: only around one in ten domains cited by one engine showed up in the other for the same queries.
That difference shapes what prompts to monitor ChatGPT Perplexity and Gemini each surface for the same buyer. A prompt that wins in one can lose in the other. So your list is not one list; it is a prompt universe you evaluate per engine. Knowing how to discover AI prompts to track means accepting that a single blended view hides more than it shows.
How to tell you have this right: each prompt carries an engine note, and you are prepared to see different winners on each.
Where people go wrong: they track one list tuned for Google SEO and wonder why it underperforms in both AI engines at once.
Filter and trim to a starter list you can actually manage
You now have a big, messy pool. That is exactly what you wanted. The last step is to cut it down, because a smaller, sharper list beats a bloated one every time.
Run each candidate through four quick filters:
- Business relevance. Does this prompt touch a topic, feature, or use case your product genuinely addresses? If not, drop it.
- Buyer intent. Which stage does it map to? Keep the stage label so your metrics stay clean.
- Influenceability. Can content and citations actually move this answer? Skip hard-coded facts, math, and pure definitions where nothing you publish will change the response.
- Scope fit. Is it tightly scoped to your category, or too broad to ever win? Tight prompts win faster.
Aim for a starting tracking list of 20 to 40 prompts. That is enough to cover your five types and three stages without drowning your budget or your attention. When you build an AI prompt tracking list this size, you keep it reviewable, and a reviewable list is one you will actually maintain. Run it across two or three engines for at least 30 days before you draw any conclusions, since answers drift from session to session and a single snapshot lies.
One more habit: put a refresh on the calendar. Revisit the list every quarter, and sooner whenever you ship a new product line or enter a new segment. Discovery is not a one-time project; it is a loop.
How to tell you have this right: you have 20 to 40 labeled prompts, each tied to a real buyer question and an engine, with a refresh date set.
Where people go wrong: they keep a 200-prompt list "to be safe," dilute the signal, and never look at it again.
What to do next
You have a real list now. That is the hard part, and you just did it. From here, the work gets more focused: score and prioritize the prompts so you know which to defend first, map them tightly to your buyer journey, and expand the strongest seeds into deeper prompt trees. Each of those is a short next step, not a mountain.
If you would rather not start from a blank page, DeepSmith can generate a starter prompt set from your brand context and track mention and citation across ChatGPT, Perplexity, and more, so you can see where you stand while you refine the list by hand. You can start a free trial and have real data in front of you within the week.
Whatever you choose, remember the point. You only needed a starting list, and now you have one. Momentum matters more than perfection here. Track a focused twenty, learn from a month of real answers, and grow the list from there.
