Story 01 · AI Engineer
Evaluate an AI application before release
Turn a proposed support-assistant change into evaluation evidence and a reviewable governance decision before a separate release system acts.
The situation
A support team has updated its retrieval and prompt configuration. Before shipping, the AI engineer needs clear evidence that the candidate meets the team’s quality criteria.
The question
Does this candidate have enough evidence to support a release review?
The governance workflow
From question to governed outcome
- 01
Select the governed inputs
Start with the dataset, prompt, model, and candidate configuration that will be evaluated.
- 02
Run the evaluation
Use the configured evaluation provider to run the candidate and persist item-level results.
- 03
Inspect the result
Review progress, metrics, failures, and the comparison with the relevant baseline.
- 04
Test the policy
Simulate the quality policy against the available evidence before relying on it.
- 05
Review the decision
Inspect the persisted governance outcome, its explanation, evidence graph, lineage, and audit context.
In Studio
The screens behind the story
Evaluation progress and results stay attached to the governed candidate.
A policy evaluates available evidence; it does not manufacture a passing result.
The final outcome is reviewable with the evidence that supported it.
The outcome
The team has a durable, explainable basis for its release review instead of a spreadsheet of scores or an informal approval.
OSS capabilities used