AI
Building Evaluation Loops for LLM Apps
LLM quality improves when every change is tested against representative prompts and expected outcomes.
Design representative eval sets
A small but well-curated eval suite can catch most regressions. Include common, edge, and adversarial prompts from real product usage.
Score quality with multiple metrics
- Task success and factuality checks.
- Format adherence and safety policy compliance.
- Latency and token cost constraints.
Gate releases on eval outcomes
- Block deployment on critical regression thresholds.
- Track prompt and model changes in version control.
- Re-run evals continuously with production feedback.