Generative AI: A Survey of Concepts, Applications, and Future Prospects

Abstract

 Generative AI comprises machine learning models that create novel content across text, image, audio, code, and multimodal combinations. This survey presents a taxonomy of model classes (autoregressive models, VAEs, GANs, and diffusion models), summarizes transformative architectures (notably transformers and diffusion processes), and reviews major applications in creative industries, scientific discovery, and software engineering. We describe our literature selection and categorization methodology, synthesize trends showing the emergence of large pretrained models and diffusion-based image generation, and analyze core challenges including evaluation, bias, misuse, and environmental impact. The paper concludes with recommendations for research on controllability, standardized evaluation, and governance to steer generative AI toward safe and equitable deployments