Enhancing Predictive Analysis with Large Language Models in the Digital Innovation World

Abstract

This research paper presents a comprehensive study on enhancing the performance of generative artificial intelligence through prompt engineering for large language models (LLMs). The findings begins with an overview of generative AI and LLMs, detailing their development, underlying deep neural network (DNN) architectures, and specific models like OpenAI's ChatGPT. It also explores the significance of prompt engineering in optimizing LLM outputs and providing examples and case studies to illustrate its impact. Various AI-enabled tools are discussed, highlighting their transformative effects across different industries. A case study demonstrates how carefully crafted prompts can significantly improve the performance and relevance of LLM responses. The challenges associated with LLM deployment, including ethical considerations, data privacy, and model biases, are also addressed. The paper concludes by affirming the potential of prompt engineering to enhance generative AI while advocating for responsible and sustainable practices in the development and deployment of LLMs.