TLDR Tech / Data
Maintaining AI Model Reliability: Addressing Model Drift
AI model drift refers to the degradation of model quality when production data, user behavior, prompts, retrieval corpora, or business rules diverge from the initial training assumptions. Teams should continuously monitor for data drift, concept drift, pipeline failures, and specific drifts in LLM and RAG systems (prompt, embedding, or output). Detection strategies involve combining baselines, distribution checks, telemetry, task completion analysis, and user feedback.
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