You will need to test the model with queries that stand outside of its training set. This means confronting the model with input examples that do not match its training data perfectly, and a simple way of doing this is to open access to the model to a restricted group of people with different interests and personalities. You can put a system in place to collect their feedback, ideally directly inside the user interface, and use this information to adjust the model. Of course, keep in mind that this can be done over multiple iterations.
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