Rubrics and scoring AI outputs

Judge outputs with a checklist and weights

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Learning goal

• Build a rubric with 4–5 criteria and weights.
• Score one AI output with it.

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Why it matters

• Rubrics make evaluation repeatable.
• Weights reflect what matters most.

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Explanation

• Criteria examples: correctness, completeness, clarity, tone, actionability.
• Weights add up to 100%.
• Ask model to self-score and justify; you confirm.

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Examples

• Prompt: “Use this rubric (criteria + weights) to score the output; give per-criterion notes.”
• Weak: “Is this good?”

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Guided exercise (10–15 min)

1. Create a rubric (5 items, weights total 100%).
2. Apply it to one AI output; record scores and notes.

Independent exercise (5–10 min)

Adjust weights to match your team’s priorities and rescore.

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Self-check

• Rubric created with weights.
• Output scored with notes.
• Weights reflect priorities.

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Optional deepening

• Evaluation templates: prompt guide