Score prompt design, structure, grounding, specificity, and reliability in one smart dashboard. Visualize strengths clearly. Build stronger instructions for measurable, consistent model performance gains.
| Version | Use Case | Complexity | Design Score | Observed Score | PES | Grade | Recommendation |
|---|---|---|---|---|---|---|---|
| V1.0 | Code Generation | 4 | 82.4 | 78.6 | 81.3 | A | Strong baseline. Improve grounding and compliance wording. |
| V2.1 | Summarization | 3 | 74.8 | 69.5 | 73.3 | B | Add evaluation checks and clearer output formatting. |
| V3.4 | Information Extraction | 5 | 89.6 | 86.9 | 88.8 | A | Near production-ready. Add one compliance reminder. |
Prompt Effectiveness Score (PES) is a weighted score from 0 to 100.
Main Formula:
PES = Σ[(Metric Score ÷ 10) × Metric Weight]
Subscores:
Design Score = (Weighted design contribution ÷ 72) × 100
Observed Score = (Weighted observed contribution ÷ 28) × 100
Complexity Benchmark = 66 + (Task Complexity × 4)
| Metric | Weight | Purpose |
|---|---|---|
| Task Clarity | 10 | Measures how clearly the prompt defines the task. |
| Context Depth | 8 | Rewards background details, audience, and domain framing. |
| Specificity | 8 | Rewards precise wording and success criteria. |
| Constraints Quality | 7 | Captures boundaries, rules, and exclusions. |
| Examples Quality | 5 | Credits helpful examples and counterexamples. |
| Grounding Strength | 9 | Measures support from data, facts, or source material. |
| Format Control | 7 | Rewards clear structure and response formatting. |
| Evaluation Readiness | 6 | Measures how easy the prompt is to test. |
| Safety Guardrails | 7 | Rewards safer and more controlled instructions. |
| Token Efficiency | 5 | Rewards concise prompts that still preserve intent. |
| Observed Accuracy | 8 | Captures correctness in real outputs. |
| Observed Consistency | 7 | Measures stability across repeated generations. |
| Observed Relevance | 8 | Measures alignment with task needs. |
| Observed Compliance | 5 | Measures adherence to required format and rules. |
It measures how well a prompt is designed and how well the resulting outputs perform. The score blends structure, grounding, formatting, safety, accuracy, relevance, and compliance into one number.
Design metrics evaluate the prompt itself. Observed metrics evaluate real model behavior. Using both prevents a polished-looking prompt from scoring too high when actual outputs remain weak.
A score above 80 is generally strong. A score above 85 with solid observed results and acceptable safety is usually good enough for more serious production testing.
Use 0 for missing or very poor performance, 5 for average quality, and 10 for exceptional quality. Try to score consistently across different prompt versions for fair comparisons.
No. A longer prompt can add clarity, but it can also introduce repetition and noise. The token efficiency metric helps identify prompts that are unnecessarily long for the value they provide.
Yes. Score each version using the same evaluation method, then compare the final score, benchmark gap, subscores, and top weaknesses to decide which prompt is stronger.
Re-evaluate whenever you change instructions, examples, constraints, target models, or use cases. You should also rescore prompts after discovering repeated failure patterns in live usage.
No. It is a structured decision aid, not a complete replacement for expert review. Human judgment is still important for nuance, safety, domain fit, and business requirements.
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.