In this thesis work, a methodological approach is proposed for application in Natural Language Processing (NLP) tasks exploiting the capabilities of latest Generative Artificial Intelligence (Generative AI) text-to-text models. With this methodology, it will be possible to delineate data features scope effectively, enhance model prompting based on expected input and output, and appropriately select the most suitable Generative model for the required NLP task. The dissertation begins with an analysis of the foundations of the subject, providing an overview of both NLP architectures, from Recurrent Neural Networks to today’s Transformer, and Generative AI state-of-the-art models from GoogleAI, MetaAI and OpenAI. Then the methodology is presented describing its phases, the different options to treat different features of data (such as data sensitivity and input formats) along with the generative model selection process and performance evaluation criteria.