Evaluation
Drift detection
Drift detection finds changes in model behavior over time.
Quick definition
Drift detection finds changes in model behavior over time.
- Category: Evaluation
- Focus: quality measurement
- Used in: Comparing models or prompt variants.
What it means
It helps maintain quality after updates. In evaluation workflows, drift detection often shapes quality measurement.
How it works
Evaluation uses tests and benchmarks to measure quality and catch regressions.
Why it matters
Evaluation ensures you can measure and improve quality over time.
Common use cases
- Comparing models or prompt variants.
- Tracking accuracy over time with regression tests.
- Validating that outputs meet acceptance criteria.
Example
Detect higher error rates post-deploy.
Pitfalls and tips
Overfitting to a single benchmark can mislead. Use varied tests and real-world examples.
In BoltAI
In BoltAI, this appears when measuring or comparing results.
