How to Distinguish User Behavior and Data Drift in LLMs

Large language models (LLMs) often provide inconsistent responses over time due to changes in user behavior, system behavior, or real-world phenomena. Distinguishing between these causes can be challenging without strong monitoring tools. The article presents four scenarios demonstrating how these issues may present themselves and provides methods for monitoring them. These include detecting changes in input data (Scenario A), identifying system behavior change (Scenario B), diagnosing changes in predictive model performance (Scenario C), and recognizing fundamental changes in the real world (Scenario D). The article emphasizes the importance of effective monitoring solutions that can identify and distinguish between these different causes.