Score prompt breadth, depth, and risk coverage precisely. Track intents, variants, constraints, and failure checks. Use results to prioritize testing and close gaps systematically.
Submit values to calculate weighted prompt coverage and penalties.
| Project | Total Scenarios | Covered Scenarios | Total Edge Cases | Covered Edge Cases | Total Constraints | Covered Constraints | Total Variants | Covered Variants | Critical Failures | Ambiguity Cases |
|---|---|---|---|---|---|---|---|---|---|---|
| Prompt Test Set A | 120 | 96 | 20 | 14 | 15 | 12 | 30 | 22 | 2 | 4 |
| Prompt Test Set B | 80 | 70 | 16 | 15 | 10 | 8 | 22 | 21 | 0 | 1 |
| Safety Eval Batch | 150 | 102 | 40 | 23 | 25 | 16 | 45 | 27 | 5 | 8 |
1) Dimension Coverage (%)
Coverage = (Covered Items / Total Items) × 100
2) Weighted Coverage (%)
Weighted Coverage = Σ(Dimension Coverage × Dimension Weight) ÷ Σ(Weights)
3) Penalties
Critical Penalty = min(30, Critical Failures × 5) and Ambiguity Penalty = min(15, Ambiguity Cases × 1.5)
4) Final Prompt Coverage Score
Final Score = clamp(Weighted Coverage − Critical Penalty − Ambiguity Penalty, 0, 100)
Prompt teams often track test cases without a clear readiness summary. A Prompt Coverage Score turns raw counts into one decision metric for release reviews. This calculator combines four dimensions of evaluation quality and subtracts risk penalties. It helps QA, product, and safety teams compare prompt versions consistently. The score also reduces subjective debate because every result is tied to explicit inputs, documented weights, and repeatable calculations. It also improves handoffs between evaluators by standardizing evidence and interpretation across teams consistently.
Scenario coverage measures how many common user intents are represented in testing. Edge-case coverage measures rare, adversarial, or noisy situations that usually trigger failures. Constraint coverage verifies format, policy, safety, and style requirements. Variant coverage measures robustness across paraphrases, tone changes, and context wording shifts. Reviewing all four dimensions together prevents false confidence. Strong scenario coverage alone is not enough if edge behavior and constraints are still weak.
Weights allow the scoring model to reflect operational priorities. A regulated process may emphasize constraints, while a support workflow may favor scenarios and variants. The calculator normalizes any positive weight values automatically, so teams can enter practical numbers quickly. Weighted coverage is then reduced by critical failure and ambiguity penalties. This keeps the final score realistic and highlights reliability issues that raw coverage percentages can hide during reporting.
In practice, teams should report the final score with dimension percentages, penalties, and sample counts. A high score indicates broad and disciplined testing, but only when labels are accurate. Mid-range scores often show uneven planning, such as good baseline scenarios with weak edge coverage. Lower scores usually signal release risk and incomplete prompt governance. Use the result summary above the form to support release gates, retrospective reviews, and roadmap prioritization.
Use this calculator after every prompt revision, model upgrade, or policy change. Export CSV results and compare score trends by release date. If scores improve but incidents increase, expand edge cases and revise ambiguity definitions. Pair coverage scoring with latency, cost, and human review metrics for stronger decision making. Over time, teams can set threshold bands for different workflows and create predictable quality controls across prompt development lifecycles and audits for audit readiness.
It measures how well your prompt tests cover scenarios, edge cases, constraints, and variants, then adjusts the score using failure and ambiguity penalties.
Weights let teams reflect business risk. You can emphasize constraints for compliance workflows or scenarios and variants for customer-facing assistants.
A critical failure is a serious prompt breakdown, such as unsafe output, policy violation, invalid format, or a response that blocks task completion.
Yes. Use the same counting rules and weights for each version, then compare scores and penalties to evaluate testing progress consistently.
No. A high score is helpful, but launch decisions should also consider production monitoring, human review, latency, cost, and incident history.
Recalculate after prompt edits, model upgrades, policy updates, or major dataset changes so your score always reflects current evaluation coverage.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.