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longitudinal-measurement

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Tracking AI product quality over time — drift, degradation, and improvement.

skills/evaluation/longitudinal-measurement/SKILL.md

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# Longitudinal Measurement
AI products change over time — models get updated, usage patterns shift, and quality can drift without anyone noticing. Longitudinal measurement is how you track quality across time and catch degradation before users do.
## What Changes Over Time
- **Model updates**: New model versions may improve some capabilities and regress others
- **Prompt drift**: System prompts accumulate edits that may interact in unexpected ways
- **Usage evolution**: Users discover new use cases that weren't tested for
- **Data drift**: The real-world inputs diverge from what was tested
- **Expectation drift**: Users' expectations change as they become more experienced
## What to Measure Longitudinally
- **Quality scores**: Track rubric scores on a consistent test set over time
- **Task success rates**: Monitor whether users are completing tasks at the same rate
- **Satisfaction signals**: Track trends in explicit and implicit satisfaction
- **Error rates**: Monitor failure frequency and type distribution
- **Latency**: Response time changes can indicate degradation
- **Engagement patterns**: Changes in usage frequency, depth, and breadth
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