Quick Answer
Interpreting evals means slicing results by risk, comparing to baselines, and choosing the right fix. The goal is not just score improvement but controlled, explainable changes.
TL;DR
- Look for regressions by slice, not just averages.
- Tie changes to business risk and user impact.
- Pick the smallest intervention that fixes the failure mode.
FAQ
What delta is significant?
Use thresholds by severity tier; a 2-point drop can be critical in high-risk slices even if the overall average is stable.
How do I avoid overreacting to noise?
Compare against variance and confidence intervals, and require consistent regression across multiple runs or time windows.
How do I decide the next action?
Trace failures to the layer that can change: prompt, retrieval, policy, or product constraints, then re-evaluate.
Build an Error Taxonomy First
Raw scores tell you what happened, not why it happened. A simple taxonomy lets teams diagnose failures and fix the right component.
Regression vs Improvement
Track trends, not just snapshots. A small drop in a high-risk category can outweigh a large gain in low-risk tasks.
Metrics That Lie
- High average accuracy: hides critical failures in rare intents.
- LLM-as-judge only: can drift with prompt changes or model updates.
- Static test set: misses real-world query shifts.
PM Playbook: What To Do Next
- Identify the failing bucket: map each failure to the taxonomy.
- Decide if it is a regression: compare against the previous release.
- Choose the action: prompt fix, retrieval fix, policy update, or product constraint.
- Communicate clearly: share the impact on user trust and business risk.
For an example of how drift surfaces in production, see Query Drift.
KPI Dashboards & Executive Readouts
Once the taxonomy is defined, consolidate the signals into an executive-ready dashboard.