A Generative Adversarial Network (GAN) is a type of artificial intelligence model used to generate new data that resembles existing data. It consists of two main components: a generator that creates new samples and a discriminator that evaluates them. The generator tries to produce data that is indistinguishable from real data, while the discriminator attempts to differentiate between real and generated samples. During training, the generator and discriminator engage in a competitive process. The generator improves its ability to create realistic data, while the discriminator becomes better at identifying fake data. This adversarial relationship helps the GAN produce high-quality outputs, such as images or audio.