As model environments have become more complex, labs increasingly use agentified processes to test training data against criteria for individual datasets and dataset diversity.

“And then you can pretty easily cross-check that response and understand if the, the data that we are training the model on is good or not. Now it's, uh, really complex environments, so a lot of the, um, the way we test the data in labs has also become agentified, where we would write, uh, a set of, um, criteria that the data needs to meet, both on individual datasets, but as well as on the diversity of datasets.”