Calculator inputs
Rate each prompt dimension from 0 to 10. Use higher values when the role is explicit, consistent, constrained, and easy to verify.
Example data table
This sample represents a fairly strong role prompt for a medium-risk AI workflow. If entered into the calculator, it produces a final score near 82.2.
| Input | Example value | Interpretation |
|---|---|---|
| Role specificity | 9.0 | Role and seniority are explicit. |
| Domain expertise depth | 8.0 | Strong subject knowledge is requested. |
| Task ownership clarity | 9.0 | Responsibilities are clearly assigned. |
| Decision authority clarity | 8.0 | Recommendation limits are mostly defined. |
| Audience alignment | 8.0 | Outputs fit the intended reader well. |
| Output structure clarity | 9.0 | Format and sequence are explicit. |
| Constraints clarity | 8.0 | Important boundaries are listed. |
| Context completeness | 8.0 | Useful background context is included. |
| Tool and data access clarity | 7.0 | Available sources are partly specified. |
| Tone alignment | 8.0 | Voice expectations are consistent. |
| Success criteria clarity | 9.0 | Good output is measurable. |
| Ambiguity term count | 3.0 | Only a few vague terms remain. |
| Conflict level | 2.0 | Minor tension exists between instructions. |
| Task complexity | 8.0 | The task is demanding. |
| Examples included | Yes | One example anchors format and style. |
| Use-case risk level | Medium | Output quality matters, but it is manageable. |
Formula used
Core Score = Σ(metric score × weight × 10)
Weights sum to 1.00, so the core score stays on a 0–100 scale.
Example Bonus = 3 if an example is included, otherwise 0
Ambiguity Penalty = minimum of 12 and ambiguity terms × 0.6
Conflict Penalty = conflict level × 1.2
Complexity Gap = max(0, task complexity − support mean)
Support Mean = average of role specificity, context completeness, output structure, constraints clarity, and success criteria
Gap Penalty = complexity gap × 2.2
Final Score = clamp(core score + example bonus − ambiguity penalty − conflict penalty − gap penalty, 0, 100)
Target Score = risk baseline + max(0, task complexity − 5) × 2
How to use this calculator
- Read your prompt and rate each metric from 0 to 10.
- Estimate how many vague terms appear, such as “good,” “better,” or “appropriate.”
- Rate conflict level based on competing rules, mixed priorities, or contradictory style instructions.
- Set the task complexity and risk level to match the real workflow.
- Submit the form to view the final score, readiness status, and weighted breakdown.
- Use the improvement list to tighten role definition, context, and evaluation criteria.
- Download CSV or PDF after calculation if you want to share the review.
Frequently asked questions
1. What does this calculator measure?
It measures how clearly a prompt defines the model’s role, authority, context, constraints, and expected output. Strong role clarity usually reduces drift, ambiguity, and unstable responses.
2. Why is role clarity important in AI prompts?
A clear role narrows the model’s behavior space. It helps the model choose better assumptions, maintain the right tone, and prioritize the right knowledge and output structure.
3. What is a good final score?
For low-risk tasks, scores above the upper sixties can be workable. Medium-risk tasks often need upper seventies or better. High-risk uses should usually target the mid-eighties or higher.
4. Does a longer prompt always score better?
No. Long prompts can still be vague or conflicting. The calculator rewards useful specificity, not extra words. Clear structure and measurable expectations matter more than sheer length.
5. Why are ambiguity and conflict penalized separately?
Ambiguity weakens interpretation, while conflict forces the model to choose between competing rules. Both reduce reliability, but they damage prompts in different ways, so they are tracked independently.
6. What counts as an example?
A strong example shows the desired format, detail level, and voice. It can be a short sample answer, a template, or a role-specific response pattern.
7. Can I use this for team prompt reviews?
Yes. Teams can rate prompts together, compare scores, and standardize prompt quality before deployment. The CSV and PDF exports are useful for review logs and sign-off records.
8. Is this score a guarantee of model quality?
No. It is a decision-support score, not a guarantee. Model quality also depends on task difficulty, model capability, available tools, retrieval quality, and real-world validation.