Enhancing Control and Responsiveness in ChatGPT: A Study on Prompt Engineering and Reinforcement Learning Techniques

Abstract

ChatGPT, based on the GPT-4 architecture, has demonstrated re-markable capabilities in generating coherent, contextually relevant, and engaging responses in conversational AI tasks. However, there are still challenges related to the consistency, reliability, and re-sponsiveness of the models outputs. This research paper aims to investigate the effectiveness of prompt engineering and reinforce-ment learning techniques in enhancing control and responsiveness in ChatGPT. By exploring novel methods for fine-tuning the model and optimizing user interactions, we strive to improve the overall performance and user experience of ChatGPT in real-world applica-tions.