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Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
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| name | output-quality-rubrics |
| description | Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness. |
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.
For each dimension, define a scale: Example — Accuracy (1-5):
Not all dimensions matter equally for every use case:
A rubric is only useful if evaluators use it consistently:
Proactively identifying failure modes, misuse, and unintended consequences.
Managing shared context, memory, and state across multiple agents.
Coordinating text, image, voice, and tool-use modalities in a single interaction.
Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
Designing review workflows to surface and mitigate bias in AI outputs.
