Offline evals, online monitoring, quality tracking, and failure analysis.
Evaluation and Observability focuses on how AI systems are measured in practice, from offline evals and traces to calibration and production monitoring.
A mechanism-level reading of the MIT study, arguing that the deeper problem is group-conditional calibration failure rather than generic chatbot bias.
Read articleA technical breakdown of Claude Code permission modes, sandbox controls, approval gates, and operational risk boundaries.
Read articleBuild a trace-level evaluation stack that links wrong answers to missing context, weak reranking, or reasoning drift.
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