Debugging AI With Adversarial Validation

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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 Validation 1 , 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, and simi