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output-quality-rubrics
Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
skills/evaluation/output-quality-rubrics/SKILL.md
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# Output Quality Rubrics
Without a rubric, quality evaluation is subjective and inconsistent. A rubric defines what "good" means in concrete, measurable terms — so different evaluators reach the same conclusions.
## Core Quality Dimensions
- **Accuracy**: Is the information correct? Are claims verifiable? Are there hallucinations?
- **Relevance**: Does the output address what the user actually asked? Is everything included necessary?
- **Completeness**: Does the output cover everything needed? Are there gaps?
- **Helpfulness**: Can the user actually use this output to accomplish their goal?
- **Clarity**: Is the output easy to understand? Is it well-structured?
- **Tone appropriateness**: Does the output match the expected tone for the context?
- **Safety**: Is the output free from harmful, biased, or inappropriate content?
## Building a Rubric
For each dimension, define a scale:
**Example — Accuracy (1-5):**
- 5: All claims are verifiable and correct. No hallucinations.
- 4: Minor inaccuracies that don't affect usefulness. No hallucinations.