AI engineering
AI integration needs evaluation before confidence
Why AI features need examples, measurement, monitoring, and human review paths before they become dependable product behavior.
1 min read
Evaluation is product work
An AI feature is not done when the first prompt works. Teams need representative examples, expected behavior, known failure cases, quality thresholds, and a plan for drift.
Questions to answer before release
- Does the model have the right context and permission boundaries?
- Are users told when output is uncertain or needs review?
- Can the team inspect failures without exposing sensitive data?
- Is there a fallback path when the model should not decide?