What is Model Evaluation?
Model evaluation measures output quality, reliability, and business fitness before and after launch.
Technical detail
Evaluation combines technical checks and workflow outcomes, such as accuracy, consistency, latency, and downstream impact. Teams should test against real scenarios, edge cases, and policy requirements. Evaluation is ongoing, not one-time. Model or prompt changes should be measured against baseline.
Why it matters
- Prevents quality drift in production workflows.
- Improves trust in AI-assisted decisions.
- Helps teams choose models and prompts objectively.
- Connects technical quality to business outcomes.
Example
Before rollout, a team scores responses across a fixed test set and compares human review outcomes. After launch, they track exception rates and correction effort weekly.
How Retailbridge relates
Retailbridge supports evaluation through workflow metrics, event traces, and weekly snapshots. Teams can detect degradation early and make controlled improvements.