Prompt coverage is the proportion of a defined prompt set for which a brand, domain or source achieves a specified visibility outcome at least once. It is a metric defined by blimpp, published here with its full measurement method. Where probability metrics measure depth, how reliably an outcome occurs per prompt, coverage measures breadth: across how much of the category’s question space the brand appears at all.
In one sentence
Coverage tells you how much of the territory you show up in, which is a different question from how strongly you show up where you do.
How prompt coverage is calculated
Prompt coverage = (prompts with the outcome in at least one run ÷ prompts in the set) × 100
The outcome is specified with the figure: mention coverage, citation coverage and recommendation coverage are reported as separate numbers, using the definitions in the measurement methodology. Each prompt is run multiple times per the repeated-run protocol, and the run count per prompt is stated, since coverage rises mechanically with more runs.
Worked example
A 50-prompt category set is run five times per prompt on one engine. The brand is mentioned at least once for 19 of the 50 prompts: mention coverage 38%. Within those 19 prompts its average mention probability is 72%, so the brand is strong on narrow ground: reliably present where the engine knows it, absent from most of the category’s question space. Illustrative example: blimpp benchmark data is added to this page as studies publish.
Why prompt coverage matters
Coverage and probability fail in opposite ways and prescribe opposite work. Low coverage with high in-territory probability means the brand’s associations are deep but narrow, and the work is expanding into adjacent sub-questions through fan-out territory it currently misses. High coverage with low probability means shallow presence everywhere, and the work is strengthening evidence where it already appears. A single blended visibility number hides which situation a brand is in.
What affects prompt coverage
Evidenced factors: whether the brand’s pages and third-party footprint address the range of sub-questions engines generate for the category. Factors with practitioner evidence: breadth of use cases covered in independent sources, the span of comparison and listicle content naming the brand, and how many distinct buyer intents the brand’s evidence speaks to. These are treated as working hypotheses until tested.
Related concepts
References
- Google Search Central, Optimising your website for generative AI features on Google Search: developers.google.com
Author: Harpal Singh · Last reviewed: 7 August 2026