Rate observable qualities
Score correctness, completeness, clarity, evidence and constraint adherence against the same defined scale.
Free AI review scorecard
AI Output Quality Scorecard
Score AI-generated work for correctness, relevance, completeness, maintainability and evidence, with a transparent verification-weighted result.
- Calculated instantly from your inputs
- No signup or data submission
- A focused next step for MVP planning
Your entries remain in this browser session and are not sent to MVPHub.
Your inputs
Separate plausible output from verified output
Correctness and verification carry more weight than style, while missing evidence caps the recommendation.
Your calculated result
Your result
0/100
Planning score
Recommended next actions
How it works
Apply quality weights
Correctness and constraint adherence carry more weight because fluent output is not useful when it is wrong or out of bounds.
Act on the weakest dimension
The result highlights the lowest rating and turns it into a concrete review or prompting action.
Frequently asked questions
What should be scored as correctness?
Rate whether verifiable claims, calculations and code behavior match the stated task—not whether the answer merely sounds plausible.
Can scorecards from different tasks be compared?
Only when the rubric and difficulty are comparable. Use benchmark cases for model comparisons across repeated runs.
Does a strong score remove the need for human review?
No. The score records a review; it cannot prove security, factual accuracy or production readiness by itself.
How we compare
| Capability | MVPHub | LangSmith | Braintrust |
|---|---|---|---|
| Weighted quality criteria | ✓ | ✓ | ✓ |
| Weakest-dimension summary | ✓ | ✓ | ✓ |
| Single-output manual review | ✓ | — | — |
MVPHub supports a quick manual review of one output. LangSmith and Braintrust provide broader evaluation and observability workflows for repeated or programmatic assessment.