In AI, a generative model is one that can create new and varied information and outcomes based on current data. It processes fresh variables that are computed with binary options and the  probability variables of a given template.  A discriminative model, on the other hand, is one that has been trained on labelled data and can categorise incoming data into one or more predetermined categories. Generative models are better at creating new data, but discriminative models are better at classifying data. The trade-off between the two approaches is that generative models can demand significantly more data and computational capacity, whereas discriminative models are faster and require less data.
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