For years, I’ve relied on a straightforward method to identify sudden changes in model inputs or training data, known as “drift.” This method, Adversarial Validation1, is both simple and effective. The best part? It requires no complex tools or infrastructure. Examples where drift can cause bugs in your AI: Your data for evaluations are materially different from the inputs your model receives in production, causing your evaluations to be misleading. Updates to prompts, functions, RAG, a...
