Enhancing Control and Responsiveness in ChatGPT: A Study on Prompt Engineering and Reinforcement Learning Techniques
Neelesh Mungoli · Zenodo (CERN European Organization for Nuclear Research) · 2023
ChatGPT, based on the GPT-4 architecture, has demonstrated remarkable capabilities in generating coherent, contextually relevant, and engaging responses in conversational AI tasks. However, there are still challenges related to the consistency, reliability, and responsiveness of the model’s outputs. This research paper aims to investigate the effectiveness of prompt engineering and reinforcement 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 applications.