Generative AI refers to a category of artificial intelligence systems that are designed to generate new content, whether it be text, images, audio, or other types of data. These systems are capable of creating new and original content by learning patterns and structures from existing datasets during their training phase.

One prominent example of generative AI is the class of models known as Generative Adversarial Networks (GANs). GANs consist of two neural networks - a generator and a discriminator - that are trained together in a competitive manner. The generator creates synthetic data, and the discriminator evaluates whether the generated data is real or fake. Through this iterative process, the generator improves its ability to produce content that is increasingly difficult for the discriminator to distinguish from real data.

Generative AI can be applied in various fields, such as image and video generation, text-to-speech synthesis, and even the creation of realistic human-like chat responses. However, it's important to note that while generative AI has shown impressive capabilities, ethical considerations and potential biases in the generated content are also areas of concern that need to be addressed in its development and application.