Generative AI primarily relies on neural networks, particularly architectures like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer models. For example, GANs work by pairing two networks—a generator and a discriminator—that compete to improve each other, producing highly realistic outputs. Transformer models, such as GPT, leverage attention mechanisms to process sequences of data, enabling them to generate coherent and contextually relevant text or code. These approaches require extensive training on large datasets to identify intricate patterns and relationships within the data.