Track quality over time
Quality does not stay fixed. A release that passes today can fail tomorrow. New prompts, changing data, and model updates all shift how the system behaves. If you do not track behavior over time, you will not know when it breaks.
Track behavior, not just scores
A score tells you that something changed. It does not tell you what changed.
Track whether output still:
- avoids unsupported details
- handles missing information correctly
- stays consistent across similar prompts
The key signal is behavior change, not score movement.
Watch for drift signals
| Signal | What it looks like | Why it matters |
|---|---|---|
| New failure patterns | A failure type appears that you have not seen before. | Indicates a behavior shift, not random noise. |
| Increasing frequency | The same failure appears more often across runs. | Reliability is weakening. |
| Scenario-specific drops | Failures cluster in one prompt type or context. | Signals a coverage or requirement gap. |
| Unexpected score jumps | Scores improve without a known product change. | The eval may be measuring the wrong thing. |
When behavior shifts, assume something changed and investigate.
Re-run the same prompts
Use a stable prompt set across runs so you can compare results directly.
You need to see:
- whether identical input now produces different output
- whether known failures are actually fixed
- whether new failures emerge
If prompts change every run, trend tracking breaks.
Separate signal from noise
Treat results as meaningful when:
- the same failure repeats across prompts
- failures appear in high-impact scenarios
- behavior changes in a consistent direction
Treat results as noise when:
- a failure appears once and never repeats
- prompts are unclear or low quality
- reviewers cannot apply checks consistently
Prioritize patterns you can act on.
Refresh the eval when it goes stale
An eval that always passes is no longer testing meaningful risk.
Update the eval when:
- the same prompts pass repeatedly with no stress
- old failure patterns disappear
- new real-world scenarios appear
- production issues are not represented in the prompt set
Add harder, realistic cases to keep pressure on the system.
Turn changes into actions
| What changed | What to do next |
|---|---|
| Reliability failures increase | Tighten requirements or constraints. |
| Failures move to new scenarios | Expand prompt coverage in that scenario. |
| Reviewer disagreement increases | Rewrite checks to be more observable. |
| No failures over time | Add more difficult and edge-case prompts. |
Tracking only pays off when a change you see turns into a change you make.
Key takeaway
Evals tell you where the system stands. Tracking tells you how that has changed.