Feedback channels
Explicit signals
Thumbs up / down
Star ratings (1-5)
Free-text comments
Correction submissions
Report harmful
Implicit signals
Response regeneration
Session abandonment
Follow-up clarification
Copy/share actions
Time-on-response
Task completion rate
Agent sources
Prael conversations
Credit agent sessions
Remittance flows
Attribution queries
1 Signal collection & normalization
Event ingestion
Kafka stream consumer
Webhook receivers
SDK event capture
Batch file upload
Normalization
Schema validation
Deduplication
Timestamp alignment
Session correlation
Enrichment
User segment tag
Model version link
Prompt template ID
Context window snap
Quality filters
Bot detection
Spam filtering
Rage-click filter
Duplicate suppression
2 Feedback classification & prioritization
Category detection
Accuracy issue
Hallucination report
Tone / style
Missing information
Wrong format
Severity scoring
P0: Safety violation
P1: Factual error
P2: Quality issue
P3: Style preference
P4: Enhancement
Pattern detection
Recurring failures
Regression detection
Cluster analysis
Topic drift
Segment correlation
Priority queue
Immediate (safety)
Next sprint (quality)
Backlog (enhancement)
Auto-resolved
Won't fix (noise)
3 Reward modeling & preference learning
Preference pairs
Chosen vs rejected
A/B comparison data
Expert annotations
Human ranking
Reward model training
Bradley-Terry model
Reward classifier
Helpfulness score
Safety score
Dataset curation
Quality threshold
Diversity sampling
Domain balance
Recency weighting
Validation
Held-out test set
Inter-annotator agree
Reward hacking check
Distributional shift
4 Fine-tuning triggers & model updates
Trigger conditions
Reward score drop > 5%
Negative rate > 3%
New data > 10K pairs
Weekly scheduled
Training actions
RLHF / DPO update
Prompt template fix
Guardrail rule add
RAG index refresh
Deployment strategy
Shadow evaluation
Canary (5% traffic)
Staged rollout
Instant rollback
Impact measurement
Before/after metrics
User satisfaction delta
Error rate change
Cost impact
5 Continuous improvement loop & reporting
Dashboards
Satisfaction trend
Category breakdown
Resolution time
Agent comparison
Alerting
Safety spike alert
Quality degradation
Weekly digest
Monthly review
Stakeholder reports
Engineering (bugs)
Product (features)
Compliance (risks)
Executive (KPIs)
Outcomes
+12% satisfaction (Q1)
-40% hallucinations
3.2 day avg fix time
Target: 95% positive
Feedback loop cycle: 7 days avg
System config
Processing pipeline
Kafka (ingestion)
Flink (streaming)
PostgreSQL (store)
S3 (training data)
Redis (real-time)
ML infrastructure
SageMaker (training)
MLflow (tracking)
Label Studio (annot.)
Weights & Biases