Build better prompts with a structured scoring workflow. Set weights, score criteria, and capture notes. Download CSV or PDF, then iterate with confidence daily.
| Criterion | Weight (%) | Score (0–5) | Weighted Points | Example Note |
|---|---|---|---|---|
| Clarity | 15.0 | 4.0 | 60.0 | Goal and task are unambiguous. |
| Specificity | 12.0 | 4.0 | 48.0 | Precise details reduce guessing. |
| Context | 12.0 | 3.0 | 36.0 | Background and assumptions are provided. |
| Constraints | 14.0 | 3.0 | 42.0 | Limits, exclusions, and rules are explicit. |
| Output Format | 12.0 | 4.0 | 48.0 | Structure and fields are defined. |
| Examples/Evaluation | 10.0 | 3.0 | 30.0 | Includes examples or success criteria. |
| Safety/Compliance | 15.0 | 4.0 | 60.0 | Avoids unsafe or disallowed requests. |
| Tone/Style | 10.0 | 4.0 | 40.0 | Voice and audience are stated. |
Score₅ = ( Σ (wᵢ × sᵢ) ) / ( Σ wᵢ )
Score₁₀₀ = (Score₅ / 5) × 100
A prompt scorecard turns qualitative feedback into repeatable governance. By scoring clarity, constraints, and formatting, teams can compare drafts without relying on subjective impressions. Use the Notes field to capture observable behaviors, such as missing fields, hallucinated assumptions, or policy-sensitive phrasing. Over time, the scorecard becomes a lightweight audit trail that explains why a prompt changed and what risk it reduced. It also helps onboard new reviewers faster with shared language.
Weights express business priorities. For customer support, safety and tone often carry higher weight; for data extraction, output format and specificity matter more. Keep weights near 100% to improve interpretability, but any scale works because the calculator normalizes by total weight. Revisit weights quarterly: when product requirements change, the scorecard should shift with them. Avoid splitting importance evenly unless every criterion truly drives outcomes.
Consistency improves when you score against the same test set of inputs. Build a small suite that covers typical queries, edge cases, and adversarial attempts. Run the prompt, capture outputs, and score each criterion from 0–5 using evidence. If reviewers disagree, write short scoring rules, like “5 means all required fields appear” or “2 means constraints are frequently ignored.” Store examples of a 5, 3, and 1 to anchor judgement.
One run can be misleading. Repeat the test set across multiple model versions or temperature settings and export each run. Look for variance: a prompt with a high average but large spread may be brittle. Use the Top improvement targets list to fix criteria with the largest weighted impact, then re-run and confirm variance drops along with failure rates. Stable prompts reduce support tickets and downstream rework.
Exported CSV reports support dashboards, sprint reviews, and regression checks. Compare current scores with the last approved baseline to spot regressions early. When a score falls, use the Notes to propose a concrete edit, such as adding structured fields, stricter constraints, or examples. Repeat until scores stabilize and the prompt behaves predictably under load. Treat the scorecard as a living spec, updated whenever user traffic reveals new patterns.
Scores above 80/100 usually signal a reusable structure. Confirm by running a small test set and checking variance. If results stay stable across runs, promote the prompt to a shared template.
Start with clarity, context, constraints, and output format. Add domain criteria such as grounding, tool usage, privacy, or citation rules. Remove anything you cannot observe consistently in outputs.
No. The calculator normalizes by total weight, so any scale works. Keeping totals near 100% simply makes it easier for humans to interpret importance at a glance.
Targets are ranked by weighted impact: weight × (5 − score). This highlights where a small improvement can raise the overall score most, helping you prioritize edits efficiently.
Rescore after meaningful edits, model changes, or new user behaviors. Many teams score weekly during development, then monthly once stable, with quick spot checks after incidents or regressions.
Add explicit constraints, define the output schema, and include one good example. Then rerun the same test set at least twice. If failures persist, split the task into smaller steps or add validation rules.
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.