I am reading about generative adversarial networks (GANs) and I have some doubts regarding it. So far, I understand that in a GAN there are two different types of neural networks: one is generative ($G$) and the other discriminative ($D$). The generative neural network generates some data which the discriminative neural network judges for correctness. The GAN learns by passing the loss function to both networks.
How do the discriminative ($D$) neural nets initially know whether the data produced by $G$ is correct or not? Do we have to train the $D$ first then add it into the GAN with $G$?
Let's consider my trained $D$ net, which can classify a picture with 90% percentage accuracy. If we add this $D$ net to a GAN there is a 10% probability it will classify a image wrong. If we train a GAN with this $D$ net then will it also have the same 10% error in classifying an image? If yes, then why do GANs show promising results?