I Analyzed 300 LLM Drift Checks: Here's What I Found

I analyzed 300 LLM drift checks across 6 months of production data. Here is what I found. The Dataset 6 months of monitoring LLM outputs in production. Multiple models: GPT-4, GPT-3.5, Claude 2, Claude 3. Multiple use cases: classification, extraction, generation. 300 data points. What Is LLM Drift? LLM drift is when your model's outputs change over time without you changing the model or prompts. The model is the same. The outputs are different. This happens because model providers update model weights behind the scenes, context distributions shift, and fine-tuning updates degrade quality. The Results Drift Is More Common Than You Think 23% of monitored endpoints showed measurable drift within 30 days 8% showed significant drift (>0.3 cosine distance from baseline) Drift is most common in: classification tasks, structured extraction, multi-step reasoning Drift Varies By Task Type Task Type Drift Rate Average Severity Classification 31% Low-Medium Extraction 24% Medium Generation 18%