How would you build a representative evaluation dataset for an AI feature?

Combines production-shaped, expert-authored, edge, and adversarial cases with versioned labels and privacy controls.

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TL;DR

I begin with real task categories and production-shaped inputs, then add expert-authored cases for important behavior that is rare or not yet observed. The dataset includes ordinary requests, ambiguous inputs, edge cases, unsupported requests, and adversarial variations. Each case records its source, expected behavior, applicable scoring guide, and important slice labels so results can be interpreted rather than treated as one anonymous average.

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Practice focus

Dataset coverage, slicing, labeling, privacy, and resistance to synthetic-only evaluation.

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Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
An evaluation result is only as relevant as the inputs, labels, and slices behind it.
I begin with real task categories and production-shaped inputs, then add expert-authored cases for important behavior that is rare or not yet observed. The dataset includes ordinary requests, ambiguous inputs, edge cases, unsupported requests, and adversarial variations. Each case records its source, expected behavior, applicable scoring guide, and important slice labels so results can be interpreted rather than treated as one anonymous average.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
Production logs can reveal natural wording, long conversations, misspellings, and unusual combinations, but they must be sampled with consent, redaction, retention, and access controls. Synthetic generation is useful for broadening a slice, not for declaring that slice representative by itself. Domain experts review difficult labels and examples, and disagreements become scoring guide clarifications instead of being discarded as noise.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Using only synthetic happy-path prompts.
Removing ambiguous cases because annotators disagree.
Sending raw production conversations into an eval store.

How would you build a representative evaluation dataset for an AI feature?

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Overview

Review signals

Follow-up prompts

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