Evaluation & Monitoring Frameworks for Retrieval Systems

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Measuring ranking quality: recall@k, MRR, precision, and when each matters Designing human labeling workflows that scale and stay reliable Running online experiments: A/B testing, interleaving, and practical metrics Detecting distribution and performance drift, and automating root-cause analysis Operational dashboards, SLAs, and SLOs for retrieval quality Practical checklist: templates, code, and monitoring playbook Sources Fonte originale