Feedback loops
The production system is the best source of future improvement, but only if feedback is captured, triaged, and converted into evals, data, prompts, or product changes.
Sources of feedback
| Source | Signal |
|---|---|
| User corrections | What answer should have been |
| Human review | Approval, edits, rejection reason |
| Trace failures | Tool errors, missing context, invalid output |
| Product metrics | Drop-off, escalation, retry, conversion |
| Support tickets | Painful edge cases |
Feedback is noisy. It needs sampling, labeling, deduplication, and segmentation.
Turn failures into assets
The highest-value loop is:
- Capture a production failure trace.
- Classify the failure mode.
- Add it to an eval or regression set.
- Test a prompt, retrieval, model, or product fix.
- Release with monitoring.
That turns one incident into permanent system memory.
Avoid self-reinforcing loops
If the model's outputs become future training data without review, errors can reinforce themselves. Separate raw feedback from approved training/eval data.
Pitfall
Collecting feedback without ownership is a graveyard. Every feedback channel needs a cadence, owner, and path to change.
Connects to: tracing · error analysis · eval sets