A rule-based review separates instruction components and calls no remote model.
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Prompt tools
Prompt Quality Checker
This tool evaluates a prompt for goal clarity, context, audience, constraints, examples, and output format. It returns both a score and a practical improvement checklist.
✓ Free✓ No account✓ In-browser
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What does this tool do?
Score goals, context, constraints, and output format with transparent rules.
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APPLICATION AND DECISION GUIDE
Prompt Quality Checker: method and usage
This tool evaluates a prompt for goal clarity, context, audience, constraints, examples, and output format. It returns both a score and a practical improvement checklist.
an instruction with an explicit goal, audience, context, constraints, and expected output format Confirm the shape first with a small example containing no personal data.
an editable prompt draft, coverage metrics, and explicit improvement actions
Practical steps
- Paste your prompt into the input area.
- Run the analysis and review component scores.
- Add missing elements and measure again.
model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases
Rule-based review does not prove real model behavior; retest with representative cases.
Suitable use cases
- Pre-flight checks for production prompts
- Shared team prompt standards
- Improving ambiguous instructions
Worked decision record
Check whether a team summarisation prompt states its goal, context, output format, boundaries, audience, and verification criteria before sending it to a model.
Start with only `Summarise this report`; then add synthetic report context, a five-bullet format, executive audience, a no-invention boundary, and a date check.
The second version passes more visible checks, each missing component receives a separate reason, and rerunning the same text returns the same deterministic result.
Do not assume a high score proves model accuracy; test the prompt on the target model with representative success, boundary, and adversarial examples.
LEARN THE WORKFLOW
Guides for this tool
Advanced Prompt Quality-Control Techniques
Evaluate prompts with tests, rubrics, counterexamples, and version discipline—not surface fluency.
Read guide →How to Protect Privacy in Prompt Engineering
Apply data classification, masking, and minimization before a prompt reaches any model.
Read guide →ABOUT THIS TOOL
Frequently asked questions
What input does Prompt Quality Checker accept?+
an instruction with an explicit goal, audience, context, constraints, and expected output format Start with only `Summarise this report`; then add synthetic report context, a five-bullet format, executive audience, a no-invention boundary, and a date check.
What does Prompt Quality Checker return?+
an editable prompt draft, coverage metrics, and explicit improvement actions The second version passes more visible checks, each missing component receives a separate reason, and rerunning the same text returns the same deterministic result.
How should I validate Prompt Quality Checker output?+
model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases Do not assume a high score proves model accuracy; test the prompt on the target model with representative success, boundary, and adversarial examples.
Does Prompt Quality Checker send or store input on a server?+
Tool input is processed in the active tab. Saving to the Workstation or transferring to another tool is a separate choice. Consider browser extensions and shared devices separately; the privacy page explains advertising and network connections.