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Retraining & Feedback Loops

Production AI models degrade over time as data distributions shift, user behavior evolves, and business requirements change. A structured retraining and feedback loop process ensures models remain accurate, safe, and aligned with organizational objectives.

Retraining Trigger Criteria​

Retraining SHOULD be initiated based on objective criteria rather than ad-hoc schedules.

Performance-Based Triggers​

TriggerThresholdAction
Primary metric degradation> X% below baseline (defined per model)Initiate retraining evaluation
Drift detection alertStatistical test exceeds threshold (see Production Monitoring & Drift Management)Investigate root cause, schedule retraining if confirmed
Safety violation increaseAny increase above accepted rateImmediate review; emergency retraining if needed
User feedback signalNegative feedback exceeds rolling average by > 2 standard deviationsQueue for retraining evaluation

Scheduled Triggers​

  • Calendar-based — retrain on a regular cadence (e.g., weekly, monthly) when data volumes are sufficient
  • Data-volume-based — retrain when a threshold of new labeled data has been accumulated
  • Event-based — retrain after significant product changes, new market launches, or regulatory updates

Trigger Evaluation​

Before committing to retraining, evaluate:

  1. Root cause — is the degradation caused by data shift, concept drift, upstream pipeline changes, or a labeling issue?
  2. Retraining feasibility — is there sufficient new data to improve the model?
  3. Cost-benefit — does the expected improvement justify compute, labeling, and validation costs?
  4. Risk — could retraining introduce regressions in other segments or languages?

Document the decision (retrain / defer / investigate further) with rationale.

Operational Feedback Loop Architecture​

Feedback Sources​

  • Explicit feedback — user thumbs up/down, ratings, corrections, escalation to human agent
  • Implicit feedback — click-through rates, task completion rates, session duration, abandonment
  • Operational signals — latency changes, error rates, fallback trigger rates
  • Human review — quality auditor assessments, subject matter expert evaluations

Feedback Pipeline​

User interaction → Logging → Feedback extraction → Quality filtering → Labeling queue → Training dataset

Each stage SHOULD have:

  • Schema validation — ensure feedback records contain required fields (session ID, timestamp, input, output, feedback signal, metadata)
  • PII filtering — remove or redact personal data before feedback enters training pipelines (per PRD-STD-014)
  • Deduplication — prevent the same interaction from being counted multiple times
  • Bias mitigation — monitor feedback demographics to avoid skewing toward vocal user segments

Feedback Latency Targets​

Feedback TypeTarget Latency (to training dataset)
Explicit corrections< 24 hours
Implicit behavioral signals< 48 hours
Human review assessments< 1 week
Aggregate metric signalsReal-time dashboards, batch to training weekly

Continuous Learning Pipeline​

Pipeline Architecture​

A continuous learning pipeline automates the path from feedback to retrained model.

Feedback data → Data validation → Feature engineering → Training → Evaluation → Approval → Deployment

Pipeline requirements:

  • Reproducibility — every training run SHOULD be reproducible given the same data snapshot, code version, and hyperparameters
  • Versioning — data snapshots, code, and model artifacts SHOULD be version-controlled (see Model Registry & Versioning)
  • Idempotency — re-running the pipeline with the same inputs SHOULD produce the same outputs
  • Observability — pipeline runs SHOULD emit logs, metrics, and alerts for failures at each stage

Data Validation Gates​

Before training, validate the new data:

  • Schema check — all required fields present and correctly typed
  • Distribution check — compare new data distribution against training baseline; flag significant shifts
  • Label quality check — verify inter-annotator agreement meets thresholds (see Training Data Governance)
  • Volume check — ensure minimum sample sizes per class, language, and segment
  • Contamination check — verify no test/evaluation data leaked into training

Training Configuration​

  • Hyperparameter management — track hyperparameters alongside model artifacts
  • Compute budgets — set maximum training time and cost limits per run
  • Early stopping — use validation set performance to prevent overfitting
  • Baseline comparison — every candidate model MUST be compared against the current production model on the same evaluation set

Human-in-the-Loop Data Collection​

When Human Review Is Required​

  • Safety-critical domains (healthcare, finance, legal)
  • Ambiguous or edge-case inputs where model confidence is low
  • New intents, categories, or languages being introduced
  • Post-incident review of model failures

Collection Workflow​

  1. Sampling — select interactions for review using stratified sampling (by confidence score, segment, language)
  2. Annotation — trained annotators label interactions following documented guidelines
  3. Adjudication — disagreements resolved through adjudication by senior annotators or domain experts
  4. Quality audit — random sample of annotations reviewed for consistency (target: > 95% adjudication agreement)
  5. Integration — approved annotations merged into the training dataset with provenance metadata

Annotator Management​

  • Annotators SHOULD receive domain-specific training before labeling production data
  • Inter-annotator agreement SHOULD be measured and reported per labeling campaign
  • Annotator performance SHOULD be tracked over time with calibration exercises
  • Guidelines SHOULD be versioned and updated when new categories or edge cases emerge

Retraining Governance​

Approval Workflow​

Model Risk TierApproval RequiredApprover
Tier 1 (Low risk)Automated if evaluation gates passPipeline automation
Tier 2 (Medium risk)ML lead review of evaluation reportML Engineering Lead
Tier 3 (High risk)Cross-functional review boardAI Safety + Product + ML Lead

Evaluation Gates for Retrained Models​

Before a retrained model can proceed to deployment:

  1. Primary metric — meets or exceeds current production model performance
  2. Safety metrics — no regression on safety evaluation suite
  3. Fairness metrics — no regression on fairness evaluation suite (see Fairness & Bias Assessment)
  4. Language parity — no regression on any supported language (see PRD-STD-015)
  5. Latency — inference latency within SLO bounds
  6. A/B test or canary — staged rollout per A/B Testing & Canary Deployment

Retraining Decision Log​

Maintain a log for each retraining decision:

FieldDescription
Decision dateWhen the retraining decision was made
TriggerWhat triggered the retraining evaluation
DecisionRetrain / Defer / Investigate
RationaleWhy this decision was made
Data snapshotVersion identifier for the training data used
Model versionVersion of the resulting retrained model (if applicable)
Evaluation summaryKey metric results comparing candidate vs. production
ApproverWho approved the retraining and deployment
Deployment dateWhen the retrained model entered production

Guardrails Against Feedback Loops​

Uncontrolled feedback loops can cause model collapse or reinforcement of biases:

  • Diversity preservation — ensure training data includes sources beyond model-generated outputs
  • Holdout monitoring — maintain a persistent holdout group not influenced by model updates to measure long-term drift
  • Output diversity metrics — monitor whether model outputs are becoming less diverse over successive retraining cycles
  • Human baseline comparison — periodically compare model decisions against human-only decisions on the same inputs
  • Circuit breakers — automatically halt retraining if evaluation metrics degrade by more than a defined threshold from the historical best

Cross-References​

Last updated on by BalrogEg