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comparative-evaluation

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A/B testing, side-by-side comparison, and preference ranking for AI outputs.

gemini-extension/evaluation/skills/comparative-evaluation/SKILL.md

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# Comparative Evaluation
Absolute quality scores are useful but limited. Comparative evaluation — putting outputs side by side and asking which is better — often reveals quality differences that rubrics miss.
## Comparison Methods
- **A/B testing**: Show different users different versions and compare outcomes
- **Side-by-side evaluation**: Show evaluators two outputs for the same input and ask which is better
- **Preference ranking**: Show evaluators multiple outputs and rank them from best to worst
- **Paired comparison**: Compare every pair of options to build a complete ranking
- **Elo rating**: Use tournament-style comparisons to develop continuous quality scores
## Designing A/B Tests for AI
A/B testing AI is different from A/B testing UI:
- **Variance is high**: The same prompt can produce different outputs, so you need more samples
- **Context matters**: The same change might help for one task and hurt for another
- **Metrics lag**: AI quality changes may take time to show up in user behavior
- **Interaction effects**: A change to one part of the conversation affects all subsequent parts
Design A/B tests with:
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