Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes
Mohammad Shabaz, Shanky Goyal, Ismail Mohamed Keshta, Mukesh Soni, Vijay Kumar · 2025
This article delves into the generative artificial intelligence (AI) project's complex ecosystem, with an emphasis on data architecture and model selection for optimal outcomes. Integrating generative AI into diverse academic and commercial domains brings unique challenges and opportunities. Navigating the generative AI project's environment is critical for success. The data architecture is complicated, so select your models wisely. This study seeks to provide complete guidelines to help manage these areas and ensure project success. Our technique consists of two fundamental components: a detailed data architecture research project for generative artificial intelligence is the first step toward identifying common problems and developing solutions. The next step is to analyze and contrast many generative models to see which ones work best for certain projects based on complexity, scalability, and ethics. Researchers verified solutions via case studies and expert talks. The findings highlight the need for a well-structured data infrastructure to boost generative AI endeavors. Studies have shown that carefully selecting generative models, taking into consideration project needs and constraints, results in better and more ethical solutions. To traverse the generative artificial intelligence project ecosystem, a comprehensive strategy must encompass ethical and technical data architecture construction as well as model selection. This study underlines the importance of understanding these issues and outlines a path forward for practitioners and researchers seeking to improve generational AI initiatives. To keep up with the fast progress of generative artificial intelligence, the study suggests further research and development in this area.