Production AI Playbook: Evaluation and Monitoring

Silent drift is a common failure mode in AI systems, where output quality degrades gradually without immediate detection. This issue arises as input patterns shift and model behavior changes, leading to inaccuracies in classifications and responses. Evaluating AI workflows requires different approaches than traditional software testing. Unlike deterministic software, AI outputs can vary, necessitating continuous performance assessment and the establishment of meaningful evaluation criteria to maintain quality.