Score Your Prompt Variants
Example Data Table
Sample scoring output to show the report format.
| Variant | Overall | Clarity | Specificity | Constraints | Structure | Safety |
|---|---|---|---|---|---|---|
| Variant A | 86.40 | 88 | 84 | 85 | 90 | 78 |
| Variant B | 81.10 | 80 | 86 | 79 | 82 | 74 |
| Variant C | 74.55 | 72 | 70 | 68 | 76 | 83 |
Formula Used
Each criterion is scored on a 0–100 scale. The overall score is a normalized weighted average:
Overall = Σ (wᵢ / Σw) × scoreᵢ
- Heuristic mode estimates scores from prompt signals (lists, constraints, numbers, format cues, guardrails).
- Manual mode converts your 1–10 ratings to 0–100.
- Hybrid mode blends 70% heuristic + 30% manual, when manual ratings exist.
How to Use This Calculator
- Paste your base prompt (optional) to measure Coverage.
- Add several rewritten variants in the text areas.
- Pick a mode: heuristic, manual, or hybrid scoring.
- Adjust weights to match your evaluation priorities.
- Click Score Variants to see ranked results.
- Download CSV or PDF to share and track iterations.
Why prompt variants benefit measurable evaluation
Iteration improves results only when changes are tracked. This scorer turns rewriting into a repeatable evaluation loop by converting qualitative prompt choices into criterion scores. Weighted scoring helps teams align on what “better” means, whether that is clarity, structured output, or safer requests. Using a consistent rubric also reduces reviewer bias across experiments. Run the same inputs weekly to monitor drift and keep baselines comparable across releases.
Criteria signals and what they represent
Clarity reflects readability proxies such as sentence length and vocabulary variety. Specificity rewards numbers, parameters, and explicit fields that narrow model ambiguity. Constraints detect rule language like “must” and “only,” which prevents drifting responses. Structure values lists, headings, and code blocks that guide formatting. Coverage compares overlap with a base prompt to preserve intent while improving execution. Actionability emphasizes clear verbs and deliverables, while conciseness favors targets that fit your channel.
Weighting strategy for practical use cases
Product support prompts often prioritize clarity and actionability, while data extraction prompts benefit from structure and specificity. Safety weights matter for public-facing assistants or regulated domains. A useful approach is to start with balanced weights, score several variants, then adjust weights after reviewing failures. Normalizing weights prevents accidental inflation when you add new criteria. Hybrid scoring is helpful when expert reviewers provide 1–10 ratings for nuance that heuristics may miss.
Interpreting scores and ranking decisions
Treat the overall score as a decision aid, not a guarantee. Two variants can tie overall yet differ in tradeoffs, like higher constraints but lower conciseness. Use the per-criterion breakdown to choose the best fit for the task. Notes highlight missing must-include terms or avoid-list hits, which are fast fixes before another run. Many teams set a minimum safety score and then optimize the remaining criteria for their workflow.
Exporting results to improve prompt governance
CSV exports support version history, review workflows, and A/B test logs. PDF exports work well for stakeholder sign-off or audit trails. Storing rankings alongside the final adopted prompt helps explain why a variant was chosen. Over time, trend analysis can reveal which improvements consistently raise quality across projects and teams at scale.
FAQs
1) What does the overall score represent?
It is a normalized weighted average of all criterion scores. Use it to compare variants consistently, then review the breakdown to understand tradeoffs that the single number can hide.
2) How should I choose weights?
Start with balanced weights, run several real prompts, and adjust based on failure modes. If formatting is wrong, increase Structure. If responses drift, increase Constraints and Coverage. Keep Safety higher for public or regulated use.
3) When is Manual or Hybrid mode useful?
Manual mode fits expert reviews when nuance matters. Hybrid mode blends your ratings with automatic signals, so you keep consistency while capturing human judgment on tone, helpfulness, or completeness.
4) Why is Coverage low even when the variant looks good?
Coverage estimates overlap with the base prompt to preserve intent. If you rephrased heavily, overlap drops. Add key domain terms, constraints, and the original objective to improve alignment without losing improvements.
5) What should go into Must-Include and Avoid words?
Must-Include should list essential intent anchors like role, audience, output format, and critical constraints. Avoid words should list unwanted behaviors or risky terms you never want to appear in a request or output.
6) What is included in CSV and PDF exports?
Exports include rank, overall score, every criterion score, and notes. Use CSV for tracking iterations and analysis, and PDF for sharing results with stakeholders or keeping lightweight review records.