Lesson 259 of 2116
Golden-Dataset Curation
A golden dataset is a curated set of hard, representative examples you trust completely. It is the backbone of every serious eval.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The Set You Bet the Company On
- 2golden dataset
- 3curation
- 4edge cases
Concept cluster
Terms to connect while reading
Section 1
The Set You Bet the Company On
A golden dataset is small, carefully chosen, and labeled by experts. Every example is a mini-specification of what 'correct' looks like. When you run it before a release, you are checking whether the model can still do the jobs you promised it could.
Properties of a great golden set
- Small enough to review by hand (100-500 items)
- Covers the full distribution of real use
- Includes edge cases, not just easy ones
- Each label has a written justification
- Disagreements between annotators are documented and resolved
How to build one
- 1Sample 500-1000 real production requests
- 2Cluster them by type (question types, topics, user segments)
- 3Pick 10-30 representative examples per cluster
- 4Have two annotators independently label
- 5Review disagreements in a meeting; adjudicate
- 6Lock the version — never silently edit labels
Annotator disagreements are data
If two expert annotators disagree on 15 percent of items, that is not a bug — it tells you that 15 percent of reality is genuinely ambiguous. Your model will be in that same fog. Your metrics should acknowledge it.
Compare the options
| Stat | What it tells you |
|---|---|
| Inter-rater agreement above 0.9 | Task is clear; gold labels are trustworthy |
| Agreement 0.7-0.9 | Good task; some ambiguous items need adjudication |
| Agreement below 0.7 | Task is fuzzy or rubric is underspecified |
“Data is the new oil. But like oil, it is valuable only when refined.”
Key terms in this lesson
The big idea: your golden set is your definition of what the product is. Curate it like it is the spec — because it is.
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