| name | output-quality-rubrics |
| description | Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness. |
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.
- 3: Some inaccuracies that could mislead if not caught. No dangerous hallucinations.
- 2: Significant inaccuracies. User would need to verify most claims.
- 1: Major hallucinations or factually wrong information presented confidently.
Weighting Dimensions
Not all dimensions matter equally for every use case:
- A medical AI weights accuracy and safety highest
- A creative writing AI weights helpfulness and tone highest
- A coding AI weights accuracy and completeness highest
- A customer service AI weights tone and helpfulness highest
Define weights when creating the rubric. Make the priorities explicit.
Rubric Calibration
A rubric is only useful if evaluators use it consistently:
- Anchor examples: Provide sample outputs at each score level
- Calibration sessions: Have multiple evaluators score the same outputs and discuss disagreements
- Inter-rater reliability: Measure agreement between evaluators and refine the rubric until agreement is high
- Edge case guidance: Document how to score ambiguous cases
Design Artefacts
- Scoring rubric with dimension definitions and scales
- Anchor examples at each score level
- Dimension weighting specifications per use case
- Calibration session protocols
- Scoring templates and checklists
