AI Prompt Discovery

By RadarKit, the AI search visibility tracker. Built from what we see tracking 50,000+ prompts across six AI engines.

Most companies track the wrong prompts. They pick their own brand name, generic questions that rarely get a brand recommendation, or long made-up prompts few buyers would type. That gives them a visibility number that looks good and tells them nothing.

This skill finds the prompts where a real buyer asks an AI assistant for help, the assistant answers with named brands, and the company has a fair chance of being one of them.

Principles

Apply these throughout. Each rule in the workflow comes from one of them.

1. Track what people are likely to type. Many people ask AI the same short way they search: "best CRM software", "best CRM for real estate". Some add detail like team size or budget, but there's no reliable public data on how many. The engine also fills in much of that context itself by fanning the prompt out into its own sub-queries. Build the core set from short, natural prompts (usually 3–10 words) rather than long prompts packed with invented detail. Add longer variants where real customer language shows people ask that way.
2. Answer shape decides value. Many prompts get answers that name no companies: definitions, how-tos, general advice. Tracking those for visibility measures nothing. Prompts that ask for recommendations, comparisons, alternatives or "which one should I pick" get a list of brands, and that's where visibility is won or lost.
3. Unbranded first. A prompt with the company's own name almost always mentions it, which inflates the score. Branded prompts are for checking accuracy (is the AI describing pricing, features and positioning correctly?), not discovery.
4. Specific beats broad. Broad category prompts ("best project management software") are dominated by the biggest, most-cited brands. A focused company has a better chance on prompts with one qualifier that matches what it does best: a persona, use case, industry or location ("best CRM for real estate", "CRM with WhatsApp integration"). Keep a few broad prompts as a benchmark and build the rest from these one-qualifier variants.
5. Competitors define the field. "Alternatives to X" and "X vs Y" prompts come from buyers who already know the category and are deciding. The useful number isn't "we were mentioned 40% of the time". It's "we were mentioned 40% of the time, Competitor A 75%, and here are the prompts where they win."
6. Fan-out explains absence. AI search engines run several web searches behind the scenes, read the top results and summarize them. If a brand is missing from the pages those searches return, it's usually missing from the answer.
7. One check is a sample, not a ranking. AI answers vary between runs, between engines and from week to week. In RadarKit's data, two engines answering the same prompt on the same day overlap on only about 16% of the brands they name. Visibility only means something when it's measured repeatedly and per engine.