Evaluation is only as good as the dataset behind it. Many teams have the right metrics and rubrics, but the dataset is stale, unrepresentative, or too sensitive to share safely. The outcome is predictable: optimistic scores that fail to predict real-world incidents.
Dataset management is an operating capability. It sits alongside observability, drift monitoring, and release discipline as a core part of running AI systems in production.
Start with a coverage map
A useful dataset is not “a pile of prompts”. It is an intentional slice of reality. Create a coverage map across:
- Intents. The main task types users attempt.
- Risk tiers. Low-risk informational flows vs high-stakes operational flows.
- Data sensitivity. Public vs internal vs restricted contexts.
- Edge cases. Ambiguous language, long context, adversarial inputs.
For RAG, include golden queries and expected sources (see retrieval quality).
Maintain multiple dataset types
Most mature programs maintain a small set of distinct datasets:
- Regression set. Stable “must pass” cases that protect key behaviours.
- Incident set. Test cases created from real incidents and near-misses.
- Adversarial set. Prompt injection attempts, jailbreak styles, and tricky edge cases (see red teaming).
- Freshness set. Recent examples sampled from production to detect drift (see drift monitoring).
Version and govern the dataset
Datasets need the same discipline as code:
- Versioning. Changes should be diffable and reviewable.
- Provenance. Track where each example came from (trace, synthetic generation, manual authoring).
- Owners. Named owners for high-impact datasets.
When datasets change, document what changed and why. Otherwise teams can’t compare scores meaningfully across time.
Privacy and safety controls for evaluation data
Evaluation datasets often contain production-like context. Apply privacy controls before sharing them:
- Minimise and redact. Remove sensitive fields and identifiers (see data minimisation).
- Retention rules. Define how long evaluation traces are stored (see retention and deletion).
- Access controls. Restrict who can download raw examples vs aggregated results.
- Synthetic augmentation. Use synthetic data for coverage, but keep a real holdout set for honesty (see synthetic data).
Connect datasets to release decisions
Datasets should drive promotion gates and canary thresholds, not just dashboards. Pair dataset-based evaluation with sandboxes and replay so you can reproduce failures quickly (see evaluation sandboxes and canary rollouts).
In the end, evaluation is not a metric problem. It is a dataset and operating-model problem. Teams that treat evaluation datasets as first-class assets learn faster and ship safer.