Evaluation Sets from Real Work Artifacts
Communications & Media • ~7–8 min read • Updated Mar 7, 2025
Context
Most evaluation datasets fail the reality test: they are synthetic, oversimplified, or divorced from the edge cases that derail production AI systems. The richest evaluation material already exists in your organization’s work artifacts—tickets, support chats, meeting transcripts, and documentation. Mining these sources yields evaluation sets that truly reflect day-to-day challenges.
Core Framework
- Source Inventory: Identify artifacts—tickets, internal wiki pages, support emails, call transcripts—and classify by sensitivity and format.
- Data Protection: Apply automated redaction for PII/PHI, contractual terms, and security credentials before further processing.
- Representative Sampling: Stratify by frequency, severity, and domain coverage. Balance “happy path” cases with failure and escalation examples.
- Golden Annotation: Use SMEs to label correct outputs, rationales, or preferred responses for each case.
- Evaluation Harness: Integrate into CI/CD pipelines so models are automatically scored against these cases pre-release.
Recommended Actions
- Establish an automated artifact ingestion process with pre-processing hooks.
- Define clear retention and disposal rules for sensitive evaluation data.
- Maintain a versioned catalog of evaluation sets with change logs.
- Use blinded reviews to reduce annotation bias.
- Track longitudinal performance to spot drift.
Common Pitfalls
- Failing to de-identify properly, risking compliance breaches.
- Over-representing common, easy cases while missing critical outliers.
- Losing track of dataset provenance, making results irreproducible.
- Building static sets that do not evolve with product and user behavior.
Quick Win Checklist
- Identify two artifact sources and run a redacted extraction.
- Create 50–100 golden cases from real incidents.
- Wire evaluation into pre-deployment checks.
- Review and refresh quarterly.
Closing
Evaluation sets grounded in real work artifacts turn AI testing from a lab exercise into a production safeguard. With proper governance and regular refresh, they become a living asset that raises quality, resilience, and trust.