Calculator
Rate your prompt design and test outcomes. Scores range from 0 to 100.
Example Data Table
Sample inputs and typical outputs for quick reference.
| Scenario | Clarity | Schema | Ambiguity | Paraphrase Tests | Similarity | Failure | Final Score | Band |
|---|---|---|---|---|---|---|---|---|
| Baseline | 7.0 | 7.0 | 3.0 | 6 | 78% | 8% | ~70 | Robust |
| Tight Schema + Checks | 8.5 | 9.0 | 2.0 | 10 | 88% | 3% | ~86 | Highly Robust |
| Vague Prompt | 4.5 | 3.0 | 7.0 | 2 | 52% | 22% | ~38 | Fragile |
Formula Used
This calculator estimates robustness by combining prompt design quality with empirical test outcomes.
BaseQuality = Σ(wᵢ · normalizedFactorᵢ) − PenaltiesEmpirical = (0.55·Similarity + 0.35·(100−Failure) + 0.10·(100−SafetyIssues)) · ParaphraseFactorFinalScore = 0.65·BaseQuality + 0.35·(Empirical · ComplexityMultiplier)All scores are clamped to the 0–100 range.
How to Use This Calculator
- Pick a scenario name for the prompt version you are evaluating.
- Estimate structural quality: clarity, specificity, schema strictness, and how well constraints are written.
- Enter test outcomes from paraphrase runs: similarity, failure rate, and safety issue rate.
- Click Calculate Score to view results above the form.
- Download CSV or PDF to record changes and share with your team.
- Iterate: apply the tips, retest, and compare scenario history.
FAQs
1) What does “prompt robustness” mean?
It’s how consistently a prompt produces compliant, similar outputs across paraphrases, different inputs, and sampling randomness. Higher robustness means less drift, fewer failures, and clearer formatting.
2) How should I estimate similarity?
Compare key facts, structure, and required fields across runs. You can use manual review, rubric scoring, or an embedding-based similarity metric. Enter the average percentage across your paraphrase tests.
3) Why does ambiguity reduce the score?
Ambiguous language leaves more room for interpretation, so outputs vary. Replacing vague verbs with explicit constraints and decision rules usually increases both similarity and compliance.
4) What counts as a failure?
A failure is any run that breaks must/must-not requirements, misses required fields, exceeds length limits, or ignores the requested format. If you track multiple failure types, use the combined failure percentage.
5) How many paraphrase tests are enough?
Three tests give a basic signal, but five to ten is better for confidence. For critical workflows, test across different phrasings, edge cases, and typical user inputs.
6) How can I improve robustness quickly?
Add a strict output schema, numbered requirements, and a short example. Then include a final self-check step that verifies constraints before the response is produced.
7) Does a higher score guarantee perfect outputs?
No. It indicates stronger prompt design and better observed stability, but model behavior can still vary by content, context length, tools, and runtime settings. Use scores to guide iteration and testing.
8) Why include a safety issue rate?
Robust prompts should also be reliably safe. If some paraphrases trigger unsafe responses, you need clearer boundaries and refusal behavior, especially for user-generated or adversarial inputs.