What is prompt coverage in AI visibility?
Prompt coverage is how much of your set of tracked buyer questions AI engines answer with your brand named, broken down by question, topic and engine. A single mention rate tells you how often you are named on average. Coverage tells you where: which questions, topics and engines name you, and which leave you out entirely. It is the map you plan from, because a question no engine names you on is a different problem from one where only one engine does.
How do tools measure AI answer coverage and citation visibility?
Coverage is measured as a grid: questions or topics down one side, engines across the top, and in each cell how many answers named your brand out of how many were read.
From the grid come the summary figures. Question coverage is the share of tracked questions where at least one engine named you. Engine coverage is the share of questions each engine named you on. Topic coverage rolls questions up into the topics they belong to, so you can see which part of your market the engines associate you with. The same grid can be built for citations instead of mentions, showing where your pages are used as sources.
What does prompt coverage look like in a real example?
In the public Tesla example (4 Oct 2026), the 25 buyer questions fell into six topics, and coverage varied far more by topic and engine than the overall 53% mention rate suggests.
On the four Supercharger network questions, ChatGPT and Google AI Overviews named Tesla every time. On the five solar panel installation questions, Google AI Overviews named Tesla in 4 and Claude in none. On the five Powerwall home battery questions, Perplexity and Google AI Overviews named Tesla once each. Those are three different problems with three different fixes, and an average hides all of them.
| Topic | ChatGPT | Perplexity | Claude | Google AI Overviews |
|---|---|---|---|---|
| Full Self-Driving (Supervised) | 4 of 5 | 2 of 5 | 3 of 5 | 3 of 5 |
| Megapack energy storage | 3 of 5 | 3 of 5 | 2 of 5 | 3 of 5 |
| Powerwall home battery | 3 of 5 | 1 of 5 | 3 of 5 | 1 of 5 |
| Semi truck charging | 1 of 1 | 0 of 1 | 1 of 1 | 1 of 1 |
| Solar panel installation | 1 of 5 | 1 of 5 | 0 of 5 | 4 of 5 |
| Supercharger network | 4 of 4 | 3 of 4 | 2 of 4 | 4 of 4 |
How should I build a representative set of buyer questions to test?
Start from the decisions buyers make before they know your name, write each as a full question, and group the questions into the handful of topics you want to be known for.
- Pick five or six topics, each a part of your market a buyer would search for on its own.
- Write up to three questions per topic in the forms buyers use: "what is the best X for Y", "X alternatives", "X vs Y", "which X has a free trial".
- Add the constraints buyers care about, such as price, team size or integrations, because engines answer constrained questions differently.
- Keep questions that name your brand in a separate set. They check accuracy, not visibility.
- Ask every question on every engine in the same words, so a gap between engines is a real difference and not a wording artefact.
Which tool better supports tracking different search intents?
Look for a tool that lets you tag each question with a topic and an intent, reports coverage per tag and per engine, and shows the searches each engine ran before answering. Intent shows up in the wording: a comparison question, a "best for" question and a pricing question pull different pages and often different brands.
Phrasing matters too. Two questions with the same intent can name different brands, so a coverage grid is only as good as the questions in it. Re-read the questions you lose, and if a rewording a buyer would plausibly use names you, track both. Query fan-out explains why: engines break a question into sub-searches, and the wording decides which ones they run.
Where do you see prompt coverage in Proofsource?
In Proofsource, Prompts lists every tracked buyer question by topic with its mention rate and rank, and each question opens to the answers from ChatGPT, Perplexity, Claude and Google AI Overviews, the brands named instead and the pages cited. Engine searches shows the follow-up searches the engines ran for each question. The Prompts feature page shows the screens.
Common questions about Prompt coverage
How many buyer questions should I track?
Start small: five or six topics with up to three questions each is enough to see the pattern. Add questions once you have acted on the first set, rather than tracking hundreds you will not read.
Which tool best supports testing hundreds of buyer prompt variations?
Volume only helps if the variations are ones buyers would type and someone reads the results. Prefer a tool that groups variations by topic and intent, reports coverage per group and per engine, and keeps every answer, over one that only reports a single average across a large list.
What is the difference between prompt coverage and mention rate?
Mention rate is one number: the share of answers that name you. Coverage is the breakdown of that number by question, topic and engine. Two brands with the same mention rate can have very different coverage, one strong everywhere and one dominant on a single topic and absent from the rest.
Should branded questions count toward coverage?
No. A question that names your brand will mention it in almost every answer. In the Tesla example, 19 of 20 answers to brand questions named Tesla. Track them separately to check the answers describe you correctly.
Related terms
- AI visibility
AI visibility is how often AI assistants such as ChatGPT, Perplexity, Claude and Google AI Overviews name your brand when people ask about your category.
- Query fan-out
Query fan-out is when an AI engine splits one question into several related searches and builds its answer from all the results.
- AI share of voice
AI share of voice is your brand's share of all the brand mentions in AI answers to a set of questions.
- Citation share
Citation share is the share of all the citations in AI answers that point at a given website.
Worked example: Tesla, public AI answers, 4 Oct 2026: Proofsource's first scan of Tesla, 25 buyer questions and 5 brand questions, one answer per question on each of four engines. A small sample from one brand, shown as an example, not a benchmark.
See where AI leaves you out, and what to fix.
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