Generative AI for Research: Paradigms, Tasks, Evaluation, and Best Practices

Generative Artificial Intelligence (AI) is a quickly evolving sphere that has allowed generating real data and making progress in the area of producing high-quality images, text-to-image translation, and predicting time series. Nevertheless, the growing variety of model architectures has turned the choice of suitable generative methods, the tailoring of these approaches, and their assessment into a significant concern of the researchers. The presented literature review provides a task-based and systematic taxonomy of generative modeling, which provides a comparative summary of the most common paradigms such as the Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), the Normalizing Flows, and the Diffusion Models. The survey is structured in the context of the main research activities: high-fidelity data synthesis, controllable and conditional generation, sequential and structured data modeling, representation learning and disentanglement, data augmentation and simulation, and probabilistic density estimation. In every task, we discuss technical goals, essential practices, measures of evaluation, and best practices. The paper also includes cross-cutting techniques such as utilizing foundation models, data curation, large-scale data processing, computational scaling, reproducibility and responsible AI practices in addition to model selection. Lastly, we present the modern technical issues and areas of future research pointing to outstanding questions concerning controllability, evaluation, efficiency, and ethical applications.This paper serves as a comprehensive resource for researchers and practitioners in the field of generative modeling.