Generative AI relies on complex algorithms and neural networks to create new content. One popular approach is generative adversarial networks (GANs), where two neural networks compete, a generator and a discriminator. The generator network produces synthetic content, such as images or text, while the discriminator network assesses the authenticity of the generated content. Through an iterative process, the generator network learns to improve its output, aiming to fool the discriminator into considering the generated content as real. This feedback loop allows the generative AI to refine its creations, gradually generating increasingly convincing content that is indistinguishable from human-created content.