## 1 Introduction  
Generative artificial intelligence (AI) has emerged as a prominent field of study, revolutionizing various domains, such as computer vision, natural language processing, and creative arts. This research aims to delve into the fundamental aspects of generative AI, including requirements, models, generative types, and evaluation metrics, to gain a comprehensive understanding of this evolving discipline [1]. The field of generative AI focuses on developing algorithms and models capable of generating synthetic data that closely resemble real-world data. The ability to generate realistic and novel data has immense implications across multiple industries, including entertainment, healthcare, finance, etc. Generative AI has opened up new avenues for applications such as image synthesis, text generation, music composition, and even human-like chatbots [2]. The increasing availability of large-scale datasets, coupled with advancements in deep learning techniques, has propelled the rapid development of generative AI. The ability to generate data that mimics real-world characteristics has the potential to address various challenges, including data augmentation, anomaly detection, and creative content generation. By understanding the requirements, models, generative types, and evaluation metrics within generative AI, researchers, and practitioners can make informed decisions when designing and implementing generative systems. Recent statistics highlight the growing interest and impact of generative AI. Precedence Research has reported that the worldwide market for generative AI was worth USD 10.79 billion in 2022. It is projected to reach approximately USD 118.06 billion by 2032, with a compound annual growth rate (CAGR) of 27.02% during the period from 2023 to 2032 [3]. This surge in market demand reflects the recognition of generative AI as a powerful tool with immense potential for various industries. Generative AI encompasses a wide range of applications, and two famous examples that have made significant contributions to the field are StyleGAN and OpenAI's GPT. StyleGAN [4], developed by NVIDIA, has revolutionized image generation by producing highly realistic and diverse images. It employs a style-based approach, manipulating specific visual attributes and enabling artists to explore new dimensions of creativity in digital art. On the other hand, OpenAI's GPT series [5], with GPT-3 as its flagship model, has reshaped natural language processing. GPT-3's massive scale and transformer architecture enable it to generate human-like text with impressive fluency and coherence, making strides in tasks such as question answering, essay writing, and conversational responses. These examples exemplify the remarkable potential of generative AI in transforming creative industries, content generation, and human-machine interaction, paving the way for further advancements in image synthesis, text generation, and beyond. Generative AI represents an integral part of the evolving landscape of Web 3.0, characterized by advancements in technology and user experiences. Table 1 compares the attributes of Web 1.0, Web 2.0, and Web 3.0 and provides a comprehensive overview of the evolution of the web, highlighting the significant changes in user interaction, content creation, technology, data management, communication, innovation, data access, computation resources, storage capacity, and examples from Web 1.0 to Web 3.0.