AI MODEL DRIFT: HOW TO KEEP MODELS RELIABLE

Summary from the TLDR Data newsletter

AI model drift is declining quality as production data, user behavior, prompts, retrieval corpora, or business rules diverge from training assumptions. Teams should monitor data drift, concept drift, pipeline failures, and prompt, embedding, or output drift in LLM and RAG systems. Detection combines baselines, distribution checks, telemetry, task completion, and user feedback.