Which buyer decisions should prompts represent?
The best AI visibility prompts represent decisions buyers make before, during and after they compare solutions. Start by writing the decisions your company wants to influence, such as whether to shortlist a category, compare providers, validate a capability or choose between two named options.
Use sales calls, support conversations, paid search terms, site search and lost-deal notes to collect the language behind those decisions. Do not copy every phrase into a tracker. Combine similar questions when they would produce the same answer and keep separate prompts when the requested evidence or recommended provider could change.
A useful prompt set should answer three questions. Are relevant buyers asking about the problem? Does your brand appear when buyers compare possible answers? Does the answer cite a page that can support the recommendation? The third question prevents visibility from becoming a vanity measure. A brand mention without a useful cited page may still leave the buyer unable to verify the claim.
Cituna asks ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode buyer questions every day and records which brands they mention and cite, so each prompt should be clear enough to compare answers across all seven. It also generates the fix for each gap and its AutoSEO writes and publishes articles, while Claude or any other AI agent can work with the data over MCP.
Separate branded demand from category discovery
Separate branded prompts from nonbranded prompts before measuring performance, because each group answers a different marketing question. A branded prompt includes your company or product name and tests whether existing demand receives an accurate answer. A nonbranded prompt describes the problem, category or desired outcome without naming your company and tests whether buyers can discover you.
Keep both groups, but do not combine their scores into one headline number. Branded visibility can remain strong while category discovery is weak, especially when a company is well known to its existing audience but absent from comparison answers. The reverse can also happen when useful content earns category mentions before the brand has direct demand.
Add a small set of partially branded prompts when buyers use a product name, feature name or category term that could refer to several providers. These prompts reveal whether the engine connects the term to the right entity. Record the exact wording, intent and expected answer type so changes are not mistaken for improvement when the prompt itself changes.
The existing guide on branded vs nonbranded AI visibility explains what to measure for each group. Use that distinction here before deciding which prompts deserve more tracking capacity.
Where to find the prompts your buyers ask
Prompt research means collecting the questions real buyers put to AI assistants before you decide which ones to track. No engine shows you those questions directly, so the list has to be assembled from places where buyers already leave their wording behind. Use several sources, because each one sees a different stage of the decision.
| Source | What it gives you | Its limit |
|---|---|---|
| Google Search Console queries | The exact words people typed before they reached your site, including long, conversational queries | Google withholds rare queries, and only searches that showed your site appear |
| Sales call notes and recordings | The comparison and objection questions asked right before a decision | Skews towards buyers who already found you |
| Support tickets and onboarding questions | Capability, integration and “can it do X” questions | Mostly existing customers, so check which ones a prospect would also ask |
| People Also Ask and related searches | The follow-up questions Google groups around a topic | Search phrasing, which is usually shorter than a typed prompt |
| Community threads (Reddit, forums, review sites) | How buyers describe the problem in their own words, and which providers they name | Loud opinions are overrepresented |
| Competitor pages | The questions rivals answer on comparison pages, FAQs and pricing pages | Shows what rivals want to be asked, not what buyers ask |
| The engines’ own answers | The follow-up questions an engine suggests, and the competitors it names unprompted | Changes from run to run, so read several answers |
Search Console deserves a closer look than it usually gets. Google’s help documentation for the Performance report says a follow-up question in Google AI Mode counts as a new query and that AI Mode data sits inside the ordinary Web search type, so some of your query rows were typed into an AI surface rather than a search box. Search Console now also has a separate Generative AI performance report for Google’s AI features, which Google introduced in June 2026. They tend to read differently: long questions written as full sentences, instructions, and short chat replies or follow-ups such as “yes go on” that make no sense as a search. Read your longest queries and your oddest short ones, and keep the ones that describe a buying decision. The guide to using Google Search Console for AI search explains what that data can and cannot separate.
Rewrite each collected question the way a buyer would type it into ChatGPT, keep the buyer’s own constraint words, and label its source. A prompt that came from three sources is stronger evidence of real demand than one that came from a brainstorm.
Cituna reads the named queries in a connected Search Console property and flags the ones worded like prompts rather than searches, such as follow-ups, instructions and long questions. Treat that count as a floor on AI use, not as Google’s AI figures. It also proposes a starting prompt list from your site, which you then edit in the Prompts tab.
Cover the prompt types that shape a shortlist
A practical prompt portfolio covers discovery, comparison, qualification, proof and selection, because buyers do not ask one question only. Discovery prompts describe a problem or category. Comparison prompts ask which providers, products or approaches differ. Qualification prompts test fit, such as company size, location, integration or use case. Proof prompts ask for evidence, reviews, examples or documentation. Selection prompts ask which option suits a stated situation.
Give each prompt one primary type, even when it could fit two. A clear label makes gaps visible. For example, a portfolio may contain many discovery questions but no prompts that test whether a cited page supports a final recommendation. That imbalance can explain why a brand receives mentions yet rarely appears in shortlists.
Do not turn every possible wording into a separate prompt. Keep variants only when changing the wording changes the audience, buying constraint, evidence requested or likely competitors. “Best payroll software for a small business” and “payroll software with accountant access for a small business” may deserve separate treatment because the constraint changes the answer. Generic rewrites usually add noise rather than insight.
Add constraints that expose where recommendations change
Add buyer constraints to prompts when a constraint could change the recommended provider or cited source. Useful constraints include company size, industry, geography, budget sensitivity, implementation capacity, existing tools, compliance needs and the buyer’s level of expertise. A broad category prompt often hides the conditions under which a competitor becomes the better fit.
Write constraints as part of the natural question rather than appending a long list of qualifiers. “Which customer support platform suits a small team already using a shared inbox?” is more diagnostic than a generic platform request followed by unrelated attributes. Keep the prompt realistic enough that a buyer might actually ask it.
Test one meaningful constraint at a time during the first pass. If a single prompt changes several variables, you cannot tell why the answer changed. After the baseline is stable, combine constraints to model high-value segments. Mark those combined prompts as scenario prompts so they are not confused with your core category measure.
The common failure is choosing prompts that flatter the brand’s broadest positioning. Constraint prompts are less comfortable, but they show where the company is genuinely suitable, where competitors fit better and which pages need evidence for a narrower claim.
Choose prompts that can be supported by a specific page
A prompt is worth tracking when a good answer could point to a specific page that supports the recommendation. Before adding a question, name the page type that should answer it, such as a comparison page, feature page, pricing page, implementation guide, policy page or customer evidence page. If no page could credibly support the answer, the prompt may reveal a content gap rather than a visibility opportunity.
Separate the two outcomes. A citation gap means an engine mentions the brand but cites another source or no source. A content gap means the company has no page that should be cited for the question. Both matter, but they require different fixes. Rewriting a page cannot solve a missing page, and publishing another page cannot solve a weak or inaccessible source.
Use the expected page as a review field, not as a forced answer. Engines may cite a different page when it contains stronger evidence. Compare the cited page with the intended page for freshness, specificity, first-hand detail and alignment with the question. Joining AI answers to Google Search Console data helps connect prompt findings to pages that already receive search visibility.
Linking each prompt to a page also makes prioritisation practical. The team can act on a cluster of prompts with one page issue instead of treating every answer as a separate task.
Prioritise prompts by business value and changeability
Prioritise prompts using business value, visibility weakness and fixability, rather than tracking every question with equal weight. Business value asks whether the prompt represents an important buyer or revenue path. Visibility weakness asks whether the brand is absent, inaccurately described, weakly supported or displaced by a competitor. Fixability asks whether a credible page, clearer evidence or better entity context could improve the result.
Use a simple decision rule: track a prompt first when it combines a high-value decision with a clear action the team can take. Defer prompts that are interesting but disconnected from a buyer, impossible to support with owned evidence or so broad that many unrelated answers would be reasonable. Keep a small watchlist for emerging language and competitor changes rather than allowing it to crowd out core prompts.
Record the reason each prompt matters. “Important category question” is too vague to guide a decision. “Tests whether operations leaders can verify implementation effort before requesting a demo” explains both value and the likely content response.
Cituna includes all seven engines on every plan without per-engine add-on fees, so a prompt should be prioritised for its buyer and action value, not because one engine is cheaper to monitor than another. Cituna’s entry plan covers 10 tracked prompts, making disciplined selection especially important for a small initial set.
What prompt volume can and cannot tell you
No AI engine publishes how often a prompt is asked. OpenAI, Anthropic, Google, Perplexity and xAI do not release query logs or per-question counts for their assistants, so there is no equivalent of keyword search volume for AI answers. Any “prompt volume” figure in a tool is an estimate: usually modelled from an opt-in panel of user conversations, from clickstream data, or from keyword volumes adjusted by a model. The method and the panel decide the number, which is why two tools can give the same question very different figures.
That does not make the estimates useless, but it limits what they can carry:
- Use them to compare topics, not to forecast traffic. An estimate can suggest that questions about pricing are asked more often than questions about integrations. It cannot tell you how many buyers will see your brand.
- Do not let a low estimate veto a decision prompt. A narrow comparison question asked by few people can sit right before a purchase. Panels see a small sample of conversations, and rare, specific questions are the ones they miss.
- Ask how the number is made. Before trusting a tool’s volume, ask what the panel covers (which engines, which countries, how many users), how often it refreshes and whether the figure is a count or a band.
- Keep demand and results separate. Volume says a question may matter. Only running the prompt says whether an engine names and cites you.
Without trusted volumes, rank prompts with signals you can check: how many of your sources the question came from, whether it maps to a revenue path, whether a competitor is already named in the answer, and whether a page could fix it. That is the same business value, weakness and fixability test above, and it holds up better than a modelled count. The comparison of AI visibility tools with prompt search volume covers which kinds of tool report demand and how they define it.
Compare the same prompts across all seven engines
Run the same prompt wording across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode when the goal is to compare engine behaviour. Changing the wording between engines makes differences in retrieval, citations or recommendations impossible to interpret. Keep the prompt, location assumptions, language and date consistent wherever the measurement setup allows it.
Treat engine agreement and engine disagreement as separate signals. Agreement suggests a broader visibility or source issue, especially when several engines cite the same competitor page. Disagreement can reveal a platform-specific retrieval path, a missing source type or a prompt whose wording is ambiguous. Neither result is automatically good or bad.
Capture more than whether the brand appeared. Record the mention, citation, cited URL, competitors named, answer position or prominence where available, factual accuracy and whether the answer matches the buyer’s constraint. A mention that misstates the product may require a faster response than an omission in a low-value question.
Do not add Microsoft Copilot to a seven-engine comparison and call the set complete. Any separate Copilot research should be labelled as an additional, different measurement rather than folded into the same engine comparison.
Set a review cadence without rewriting the measurement
Review prompts on a fixed cadence and change the portfolio only when buyer language, products, competitors or answer behaviour changes. A stable core set makes movement interpretable. If prompts are rewritten whenever results disappoint, an apparent improvement may simply reflect an easier question.
Keep three prompt states: core prompts that remain unchanged for trend measurement, diagnostic prompts used to investigate a known issue and experimental prompts testing new buyer language. Promote an experimental prompt into the core set only after confirming that it represents a real decision and has a page or content action attached to it. Retire a core prompt when the underlying product, market or buyer decision no longer exists, and record the reason.
Review prompt quality alongside answer quality. A prompt may need revision when engines interpret it in inconsistent ways, when it combines several decisions or when the expected audience no longer matches the wording. Preserve the old version in the change log rather than silently replacing it.
A useful review asks what changed in the market, what changed in the company and what changed in the answer. Those three causes lead to different actions. New competitor language may call for a diagnostic prompt, while a changed product may justify retiring a core prompt.
Keep the tracked set stable enough to compare
A trend only means something if the question stayed the same. Freeze the parts of a core prompt that change the answer, give the set a version number, and log every change with its date so a movement in the results can be traced to the market, the page or the prompt.
| Freeze for every core prompt | Why |
|---|---|
| The exact wording, including punctuation | A one-word change can produce a different shortlist |
| Language and country | Engines answer the same question differently by market |
| The engines it runs on | Adding or dropping an engine changes any combined figure |
| The prompt’s type and source label | Keeps discovery, comparison and proof results apart |
| The intended page | Lets you see whether the cited page moved, not only the mention |
When a prompt must change, retire the old one and add the new one as a separate line instead of editing it in place, and keep both in the log for at least one review cycle. Compare results only within one version of the set. Judge a change over several runs rather than one answer, because assistants compose a new answer each time and the same prompt can name different brands on consecutive days. For questions that only matter to a narrow segment, the guide to measuring AI visibility for long-tail prompts shows how to group and score them by intent.
Turn prompt results into a ranked action queue
Turn prompt results into a ranked action queue by grouping repeated problems and assigning each group one owner, one target page and one next test. The queue should distinguish missing mentions, weak citations, inaccurate descriptions, unsuitable recommendations and competitor displacement. Each problem points to a different type of work.
Start with the group that affects a valuable buyer decision, appears across several engines or can be improved by a concrete page change. A cited competitor page may justify strengthening first-hand evidence on your relevant page. An inaccurate answer may require clearer product facts, consistent terminology or an entity correction. A missing page may require creating useful content before further prompt tuning.
Write the next test before making the change. For example, record that a comparison page will be revised to state the relevant constraint and provide evidence, then rerun the same core prompts and a small set of diagnostic prompts. Do not judge success from a single answer. Look for a more accurate mention, a better-matched citation and improved representation of the buyer’s constraint.
For readers comparing tools, Cituna’s AI visibility tracking pricing page lists its seven-engine plans and prompt capacity. The buying decision should still follow your measurement needs: confirm engine coverage, prompt limits, connected search data and the route from an answer to an actionable fix.
Related reading
- AI Content Visibility: An 8-Step Check
- How AI Engines Rank Brands in Answers: A Practical Guide
- How to Get AI Engines to Recrawl Updated Content
Sources consulted
- Google Search Central (developers.google.com)
- OpenAI platform documentation (platform.openai.com)
- Perplexity API documentation (docs.perplexity.ai)
- Google Search support (support.google.com)
Drafted with AI assistance from our own research and Search Console data, and reviewed by Rahul A before publishing. Rules and prices change; check the linked official source before you act.