Test Result
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Aggregate analytics from multiple test runs that reveal trends and patterns in AI system quality over time.
Also known as: results
Overview
Test results provide analytics and insights by aggregating data across multiple test runs. They help you understand trends, identify regressions, and track quality improvements over time.
Analytics Provided
Trend Analysis:
- Pass rate over time
- Metric performance trends
- Regression detection
- Improvement validation
Comparisons:
- Baseline vs. current performance
- A/B testing between versions
- Environment comparisons
- Model performance differences
Deep Insights:
- Overall system health
- Behavior-specific performance
- Category and topic breakdowns
- Individual test stability
Visualizations
- Time series charts: Track metrics over time
- Heat maps: Identify problematic areas
- Comparison tables: Side-by-side analysis
- Distribution plots: Score distributions
Common Insights
Regressions:
- "Pass rate dropped 15% after deployment"
- "Safety metrics degraded in production"
- "New version performs worse on edge cases"
Improvements:
- "Accuracy improved 20% after fine-tuning"
- "Response time reduced by 30%"
- "Refusal rate appropriate for harmful content"
Best Practices
- Establish baselines: Know your starting point
- Regular monitoring: Check results after each deployment
- Set thresholds: Define acceptable performance levels
- Investigate changes: Understand why metrics change