Training an AI to generate images based on a set of examples involves using a type of neural network called a Generative Adversarial Network (GAN). A GAN consists of two neural networks, a generator and a discriminator. The generator creates images, while the discriminator evaluates them to determine if they are real or fake. During training, the generator is fed a set of example images and learns to generate new images that are similar to the examples. The discriminator is trained to distinguish between real and generated images. The two networks are trained together, with the generator trying to fool the discriminator and the discriminator trying to correctly identify real and fake images. This process continues until the generator is able to produce images that are indistinguishable from the real ones. Once the training is complete, the generator can be used to generate new images based on a given text prompt or a set of example images.