AI pilot accuracy looks great because evaluation uses curated samples. Production is mostly missing fields, wrong part numbers, blurry photos, colloquial descriptions. Models never saw these—suggestions drift. Where evaluation sets come from decides whether intelligence can stand watch. Demo data proves demos; historical work orders and real errors prove production.
Treat rejected, rewritten, and complained samples as gold—not another labeling team closer to reality.
How Demo Sets Fool Launch Reviews
Demo sets are balanced, legible, uniquely conclusive. The floor is long-tail categories, multi-intent tickets, conflicting owners. Hitting lab metrics to cover the floor treats laboratory as operations.
Without error samples, models never learn to refuse—they still offer fluent lines when they should not. Fluency is risk in production.
How to Build a Gold Set
- Sample last six months by type—force include rejections, reassignments, complaints.
- Each sample has standard action or standard refusal—disputes arbitrated by business owners.
- New failures enter within one week—regression fail blocks scenario expansion.
- Do not substitute vendor generic sets for your errors—industries err differently.
The XYN digital intelligence system puts work orders and QC in configurable apps—errors can flow back into evaluation. Evaluation from real failure earns production; from demo, it only talks well in meetings.
