Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications’ Deployment
This paper was Published in the International Journal of Soft Computing and Engineering (IJSCE) - July 2024. In the dynamic realm of artificial intelligence, the emergence of Generative Artificial Intelligence (GAI) has marked a revolutionary stride, in particular in the context of project execution models.
This paper delves deep into the sophisticated architectures of GAI, mainly focusing on Large Language Models (LLMs) such as GPT-3 and BERT, and their practical applications. Central to the proposed exposition is the innovative "Forward and Back Systematic Approach" designed for executing GAI projects to enhance efficiency and ensure harmonious alignment with diverse application requirements.
We dissect various strategies, including leveraging Private Generalized LLM APIs, in-context learning (ICL), and fine-tuning methodologies, to empower these models to adapt and excel. Through this comprehensive exploration, the study serves as a beacon for enthusiasts and professionals, contributing a substantial framework and critical analysis to steer the course for future AI innovations.