Design a continuous improvement pipeline that periodically fine-tunes a model on production feedback.

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Collect candidate training data from production (user corrections, accepted outputs, expert-reviewed failures), but only with consent and policy approval. Filter and clean it (PII scrubbing, deduplication, quality checks, removing eval-set overlap), label it or create preference pairs, then train on a schedule or trigger (LoRA/SFT or DPO), evaluate against the current production model on golden, safety and general-capability suites, register the candidate, and deploy through shadow and canary. Monitor post-deploy and keep lineage for every dataset and model.

Continuous fine-tuning pipeline: production traces become signals, are curated, labelled into pairs and versioned, then trained and evaluated against the production model; a better candidate goes to the registry, shadow and canary, and online monitoring decides promote or rollback, while a worse one stops with a report.

Key safeguards

Feedback is biased: thumbs-up data rewards pleasing answers (sycophancy risk). Prefer expert-reviewed and outcome-verified examples.
Data contamination: keep eval data strictly separate from training data (hash-based overlap checks).
Feedback loops: training on the model's own outputs can amplify errors. Include human-written or verified data and diversity checks.
Regression protection: a general-capability and safety eval suite must not regress (catastrophic forgetting).
Cadence: triggered by data volume or drift, not blindly weekly; compare cost vs gain.
Governance: consent and contractual rights to use customer data for training; data retention and deletion propagated to training sets (and retraining when deletion requires it).

PreviousDesign a self-hosted inference cluster on Kubernetes serving five open models (chat, code, embeddings, reranker, small classifier) with SLOs.

Slow is fine. Stopping is the only problem.

Design a continuous improvement pipeline that periodically fine-tunes a model on production feedback.

Key safeguards

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