Compare answer quality after every model update.
Include ordinary, harmful, and refusal cases to detect regressions.
Choose two appropriate contents.
Keep representative questions and expected evidence fixed, include safety and boundary cases, and inspect severe individual failures in addition to averages.
Detailed explanation
Quality can be compared across versions.
Quality can be compared across versions.
Safety and failure regressions become visible.
Safety and failure regressions become visible.
There is no stable comparison baseline.
There is no stable comparison baseline.
Small but high-impact failures can be missed.
Small but high-impact failures can be missed.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 3.3の評価データ、品質指標、責任あるAIを確認する。Expected result
モデル更新を同じ基準で比較し、安全性の回帰も評価できる。Key points
- Fixed set
- Regression
- Boundary case
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.