Evaluation & Quality
Golden dataset
A carefully curated set of high-quality examples with trusted expected answers.
Example
A carefully reviewed set of questions and answers anchors repeated quality tests.
Why it matters
It gives teams a dependable anchor for repeated quality checks.
You'll hear it
“This reference answer needs review because the policy changed.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Can a golden dataset contain a mistake?
- Yes. Careful preparation reduces errors but does not make the collection infallible.
- Should it contain only straightforward cases?
- No. Important exceptions can be essential for judging useful performance.
- Does golden mean it must never change?
- No. Corrections and new requirements can justify updates, with a clear record of what changed.