Effective Techniques for Generative AI Precision
Authors:-Sachin Vighe
Abstract-Generative AI systems have demonstrated remarkable capabilities in various domains, such as natural language processing and image and audio generation, yet achieving high precision and accuracy in these systems remains challenging. This paper comprehensively reviews effective techniques for enhancing generative AI precision, focusing on three key areas: data preparation, model architecture optimization, and fine-tuning strategies. We explore advanced data curation, synthetic data generation, and data augmentation methods that improve model accuracy. For model architecture optimization, we examine recent advancements in attention mechanisms, hierarchical structures, and multi-modal integration that promise increased precision. Fine-tuning strategies analyzed include few-shot learning, continual learning, and domain-specific adaptation. Additionally, I will discuss novel framework for evaluating and benchmarking generative AI precision, offering researchers and practitioners a standardized approach for assessing improvements. Case studies and empirical evidence demonstrate these techniques’ efficacy across various generative AI applications. My findings underscore the importance of a holistic approach to precision enhancement, combining multiple strategies for optimal results, contributing to efforts to make generative AI systems more reliable, accurate, and trustworthy.
