This is about the eval-to-production gap. Synthetic benchmarks look good, but real users see hallucinations, outdated info, or irrelevant retrieval. How do you monitor, detect drift, and fix it proactively?
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- Production Monitoring: Log retrieval sources, latency, and user feedback (thumbs up/down). Track “answer groundedness” scores in real-time.
- Shadow Evaluation: Run a subset of production queries through an offline eval pipeline daily using LLM-as-a-judge (RAGAS, TruLens).
- Drift Detection: Monitor embedding distribution drift (covariate shift) and query distribution changes. Alert if cosine similarity scores drop significantly.
- A/B Testing: Test new chunking strategies or embedding models on 5% traffic before full rollout.
- Feedback Loop: Use user corrections to fine-tune retrieval or create hard negative mining datasets.
Askgenai In Answered question