This Bachelor thesis investigates the influence of prompt engineering and the integration of an Interaction Quality (IQ) feedback loop on the performance of ChatGPT-3.5 as a voice assistant. By analysing empirical data across multiple configurations, this study explores how these interventions affect response accuracy and efficiency. Findings suggest that prompt engineering tends to enhance system performance, though the benefits of the IQ feedback loop remain less clear and require further investigation. This study contributes to the field by delineating the potential for targeted modifications to improve dialogue system outputs in real-time applications.