Build active learning pipeline for ambiguous and low-confidence intent examples

To systematically improve intent classification, especially on borderline or ambiguous queries, implement an active learning pipeline that continuously surfaces low-confidence or high-ambiguity user interactions from production, routes them for human review/labeling, and ingests validated examples back into the training set. This will prioritize the most informative edge cases, reduce blind spots, and accelerate model improvement with focused data collection.

Acceptance Criteria:

1. Production inference logs are analyzed to detect queries with low confidence, near-equal scores across multiple intents, frequent user rephrases, or fallback triggers.
2. A sampling/prioritization strategy selects representative, high-value ambiguous/low-confidence examples for human review.
3. Review tooling displays the original query, predicted intent(s) with confidences, context snippet, and allows reviewers to correct labels.
4. Corrected examples are versioned with metadata (source, timestamp, original/confidence, reviewer) and stored.
5. Validated labels are merged into the training dataset with conflict-resolution safeguards (e.g., reviewer consensus).
6. Retraining pipeline incorporates these examples and produces updated classifier artifacts.
7. Metrics show reduced low-confidence predictions and improved accuracy on an ambiguity-focused validation slice.

Tasks:

• Define heuristics for detecting ambiguous/low-confidence inferences.
• Build sampling/prioritization mechanism.
• Implement or extend human review UI with context and correction capability.
• Implement storage/versioning for reviewed examples with metadata.
• Develop ingestion pipeline to merge validated labels into training data with conflict resolution.
• Integrate active learning outputs into retraining process.
• Create evaluation slice for ambiguous intents and track impact.
• Automate reporting on labeling volume, latency, and model improvement.

Estimated effort: ~1.5–2 sprints

Success Metrics:

• Number of high-value ambiguous examples labeled per week
• Reduction in proportion of low-confidence predictions
• Improvement in accuracy/F1 on ambiguity validation slice
• Time from example surfacing to retraining incorporation
• Reviewer throughput and label quality (e.g., inter-reviewer agreement)