Fit quality improves when the system learns from concrete examples of what went right and what went wrong. That means reviewing calibration sets, false-positive examples, targeted reports, and rules that are adjusted one layer at a time.
- Collect examples
Bad matches, strong matches, and user reports become concrete cases to review.
- Identify the failure type
The issue may be a role-family mismatch, location problem, seniority gap, missing salary detail, weak explanation, or stale posting.
- Tune the right layer
Layiq can adjust scoring rules, prompts, gates, or explanation requirements without changing every part of the system at once.
- Check regressions
Known examples are used to make sure a fix does not make earlier behavior worse.
This is why user feedback matters. A report that says 'wrong seniority' or 'not remote despite my criteria' is more useful than a generic thumbs down because it points to the layer that needs adjustment.