What is a golden dataset in the context of AI evals?

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A golden dataset is a small, human-checked set of test cases with known-good answers. It is the fixed reference you score every prompt, model, or retrieval change against.

Each row pairs an input with an expected output or grading criteria, plus any context the system needs.

{"id": "ret-014", "input": "Can I return shoes after 40 days?", "context": ["policy: 30-day returns"], "expected": "No, the window is 30 days.", "must_include": ["30"]}
{"id": "ret-015", "input": "my order never showed up", "expected_tool": "lookup_shipment"}

"Golden" means the labels are ground truth written or approved by people, not model output.

Two things matter most. Keep it frozen, or scores stop being comparable run to run. When the product rules change, re-check the labels as a new version, since a stale label fails answers that are now correct.

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Likely follow-ups

Walk me through how you would build one from production data. Sample real traffic across intents, add known failures, strip PII, and have a person write or approve every expected answer.
How do you decide what goes in it, and how many rows is enough? Cover the main intents, edge cases and past failures, with enough rows per slice to see a change. Size follows the slices you need, not a fixed number.
Some questions have many acceptable answers. How do you grade those rows? Use grading criteria instead of exact-match strings, such as must-include facts, expected tool calls or a rubric, with any LLM judge checked against human labels.
The return window changes to 45 days. What happens to your dataset and your scores? Re-label the affected rows as a new version, rerun the current system on it for a fresh baseline, and never compare scores across versions.
How do you keep prompt tuning from overfitting to the golden set? Hold out rows you never tune on, keep adding fresh production failures, and confirm wins with online metrics, not only the offline score.

What you can say

A golden dataset is a small set of test cases with known-good answers that people have written or checked.
It's the fixed reference I score every change against, whether that's a new prompt, a model swap or a retrieval tweak.
Each row pairs an input with an expected output or grading criteria, plus any context the system needs, like the policy text for a returns question.
Some rows check the answer text, like requiring the 30-day window, and others check behavior, like expecting a call to the shipment lookup tool.
Golden means the labels are ground truth from people. A model didn't generate them.
It has to stay frozen or scores stop being comparable, but when product rules change you re-check labels as a new version, because a stale label fails answers that are now correct.

Weak answers to avoid

Confuses it with a training set The golden set is for scoring changes, not training. If its rows leak into fine-tuning or prompt examples, scores go up without the product getting better.
Lets the model write the labels Using model output as the expected answer copies the model's mistakes into the reference. Golden means a person wrote or approved each label.
Edits rows in place between runs If rows change quietly, this week's score and last week's measure different things. Freeze the set and put changes in a new version.
Never re-checks labels when rules change When a policy changes, old labels fail answers that are now correct, and the score drops for no real reason. Re-check the affected labels as a new version.

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