Output Quality Rubrics

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Owl-Listener
• Source: Owl-Listener/ai-design-skills
• License: MIT

Install and usage instructions live in the source repository linked above.