Build user feedback loop and analytics for intent classification errors

To continuously improve intent recognition accuracy, implement a feedback and analytics system that captures both explicit and implicit signals of misclassification, surfaces recurring confusion patterns, and feeds high-quality corrections back into the training pipeline. This creates a data-driven loop for detecting, diagnosing, and remedying intent errors in production.

Acceptance Criteria:

1. Define and instrument implicit feedback signals (e.g., user rephrases after an answer, “No, I meant…” corrections, repeated clarification requests, low engagement after response) that indicate potential intent misclassification.
2. Add an explicit, lightweight feedback widget/dialog in the user interface for cases of uncertainty or post-response: e.g., “Did I understand you correctly?” with quick options and optional free-text correction.
3. Build an analytics dashboard tracking intent error indicators: confusion matrices, top misclassified intent pairs, feedback volume by intent, trends over time, and severity spikes.
4. Establish a human-in-the-loop validation workflow to vet and label high-confidence feedback for use as augmented training examples.
5. Automate alerts/notifications when predefined thresholds of misclassification or feedback volume are exceeded for a given intent (e.g., sudden jump in “wrong intent” signals).
6. Create a pipeline to ingest validated feedback into the intent training dataset, including metadata (timestamp, source type implicit/explicit, contextual snippet) while respecting privacy and anonymization policies.
7. Document feedback interpretation heuristics, dashboard metrics definitions, and the process for incorporating corrections into model retraining cadence.

Tasks (checklist):

• Specify implicit signal heuristics and implement telemetry capture.
• Design and integrate explicit feedback UI component.
• Develop the analytics dashboard for intent performance and feedback.
• Define validation workflow and tooling for human review of feedback.
• Set up alerting for abnormal error/feedback patterns.
• Build the feedback-to-training-data ingestion pipeline with privacy safeguards.
• Write documentation for the feedback loop, metrics, and retraining integration.

Estimated effort: ~1–2 sprints