Case study
Guardrail Studio
A small eval suite for tone, grounding, and unsafe replies.
Eval board
GroundingRefusalFormat
0.91
0.74
0.62
0.88
0.41
0.79
0.95
0.66
0.83
Grounding, refusal, and format scores
- 48
- Eval cases
- 3
- Score axes
- CI
- Release gate
One good example is not an eval
Guardrail Studio is a placeholder for an evaluation harness around an LLM feature. The point is a repeatable gate, not a larger prompt.
Engineering approach
Each case has an input, a tag, and a checker. Checkers are deterministic where possible. A model judge is used only for axes that need one, and its prompt is versioned too.
Results
Sample runs caught a format regression the author did not see in manual testing. Use your own case count and failure stories here.
Technical implementation
YAML cases, a runner that calls the same API as production, and a report that diffs the current prompt against the last green run.
- Python
- FastAPI
- Next.js
- LLM evals
Key features
- Cases for grounding, refusal, and format
- Diff against the last passing run
- A gate in the release checklist
- Failure examples stored with the score
Written by
Anuoluwa Olutayo
AI Engineer