Model Drift

Model drift is the degradation of an AI model's accuracy over time as the real-world data distribution it is applied to shifts away from the distribution it was trained on. Drift is detected through statistical monitoring of model outputs and business KPIs, and corrected by retraining or retrieval-layer updates.

Data drift (input distribution changes) and concept drift (the relationship between inputs and correct outputs changes) require different remediation. LLM deployments experience drift primarily in the retrieval layer as organizational knowledge changes — regular knowledge-base updates are the first line of defense.