The most common mistake new Promptive customers make is starting with too many prompts. They import every keyword variant they've ever tracked, build a list of forty, and end up with a dashboard that's noisy, expensive to run at high frequency, and impossible to act on. The fix is not more prompts. It's five better ones, chosen from a taxonomy that covers the full competitive surface area of almost any B2B category.
We call it the five-prompt taxonomy: category, comparison, objection, use-case, and alternative. These five types each represent a distinct phase of the buyer's evaluation journey. Together they surface 90% of the competitive intelligence that matters for most brands. Build your initial tracked set from one well-chosen example of each type, measure for four weeks, and you'll know exactly where your AI visibility gaps are before you invest in any intervention.
Prompt type 1: Category
A category prompt is framed around a space, not a product. "Best tools for AI visibility tracking." "Top CRM platforms for mid-market SaaS." "Leading generative engine optimization platforms in 2026." The defining feature is that no specific brand is named, the prompt asks the engine to define the category and name its leading representatives.
Category prompts reveal the engine's mental model. When an AI answers this type of prompt, it's telling you which brands it believes own the space, not which brands rank for a keyword. A brand can rank on page one for every relevant keyword while barely appearing in category-level AI answers because the model's training data associated category leadership with a different set of players. Category prompts expose that gap.
Choose the highest-intent version of your category description for your category prompt. Avoid both too-broad (just names incumbents you can't displace) and too-narrow (low volume, low signal). The right prompt is one that a serious buyer in active evaluation mode would actually ask.
Prompt type 2: Comparison
A comparison prompt names two or more specific products: "Promptive vs Profound," "HubSpot vs Salesforce for revenue operations," "Figma vs Sketch for product teams in 2026." These are among the highest-intent queries in any category, someone asking this has already narrowed their shortlist and is looking for a reason to choose.
Comparison prompts are also the most likely to surface inaccurate competitive framing. Engines regularly get the specifics wrong, outdated pricing, discontinued features, misattributed capabilities. The content that corrects this is the content that gets cited. A well-written comparison page that honestly names both products, acknowledges the competitor's genuine strengths, and makes a specific case for when each wins is more likely to be cited than a page that simply claims superiority across every dimension.
Track at least one comparison prompt for each major competitor in your set. If you don't know which competitors the engines are placing alongside you, run your category prompt first, they'll appear in the answer.
Prompt type 3: Objection
Objection prompts are framed around concerns or drawbacks: "Is Promptive worth it for small teams?" "Downsides of HubSpot." "Limitations of X compared to alternatives." The user asking this has evaluated your product, heard something that gave them pause, and is running a pre-purchase sanity check.
Objection prompts reveal the negative narrative the engines have absorbed about your brand. If you're scoring poorly here, either because the engine is surfacing legitimate concerns or because a competitor's negative framing has been absorbed as fact, it shows up first in objection prompt results. Objection sentiment is one of the strongest predictors of deal-stage churn: buyers who encounter negative AI sentiment during evaluation are significantly more likely to eliminate a vendor without telling their sales team why.
The content intervention for objection prompts is almost always the same: a page that directly addresses the concern, names it explicitly in the headline or opening, and answers it with specific evidence. "Is Promptive worth it for small teams? Here's our starter pricing, a two-minute setup path, and two customers tracking under twenty prompts who reached ROI in the first week." That level of specificity is quotable. Vague reassurances are not.
Objection prompts tell you what the engines say when your buyer is having second thoughts. That's the conversation that kills deals you never knew you were in.
Prompt type 4: Use-case
Use-case prompts name a specific problem or workflow: "Tool for tracking brand mentions in ChatGPT." "Platform for monitoring AI search visibility across a client portfolio." "Software to detect when AI engines recommend your competitors instead of you." These are the prompts closest to purchase intent, the user already knows what problem they're solving and is looking for the specific solution.
Use-case prompts are often where brands punch above their weight. A brand outcompeted in category-level answers can dominate use-case answers for specific workflows where they have genuine depth. This is the prompt type that surfaces your real competitive moat, the specific problems you solve better than anyone else, in the language buyers use when describing those problems.
For use-case prompt selection: pull your top three highest-value deals from the last quarter and look at the problem language in the initial outreach or early discovery calls. Those are your use-case prompts. If the engine isn't surfacing you for the exact language your best buyers use when they find you, you have a use-case visibility gap.
Prompt type 5: Alternative
Alternative prompts mirror comparison prompts but from the other direction: "Alternatives to Profound." "Competitors of HubSpot that are cheaper." "What are the options besides X?" The user asking this prompt has already evaluated, or been rejected by, your competitor and is actively looking for something different.
Alternative prompts represent a specific and often underserved opportunity: the disqualified prospect. Every established competitor in your category generates a stream of users who evaluated and decided they weren't the right fit. Those users are asking alternative prompts right now. If you're not in the engine's answer, you're invisible to them at the exact moment they're most receptive to switching.
The brands that track alternative prompts systematically almost always find at least one major competitor whose alternative prompt they're not appearing in, despite being a natural fit for the disqualified segment. That's typically a citation gap, fixable with one well-optimized alternatives page and Reddit presence in the relevant communities.
Building your first prompt set in a week
Week one: write five candidate prompts, one of each type. Run them through Promptive to get position, sentiment, and citation source data across all four engines. Record where you are across each prompt type. That's your baseline.
Week two: identify which of the five prompt types represents the highest-priority gap, either the largest current position deficit, or the prompt type where improving would have the most direct business impact. Narrow your first intervention to that single prompt. Continue tracking all five weekly so you're building trend data across the full taxonomy.
By the end of week four: you have position baselines across all five types, a citation source map for each, and one intervention in flight. That's a functioning GEO monitoring program. From there, add one or two prompts per week until you're tracking twenty to thirty and have a comprehensive view of your competitive surface area.
The teams that skip the taxonomy step, bulk-importing fifty prompts without strategic selection, end up with a lot of data and no leverage. Five well-chosen prompts with clear business stakes generate more actionable insight in month one than fifty prompts chosen by keyword volume alone. Start narrow. Get specific. Compound from there.
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