Optimization of Prompt Segmentation for Improving Event Recommendation Accuracy Using Generative Artificial Intelligence

As demand grows for event recommendation systems capable of accommodating users with diverse backgrounds and preferences, recent advancements in Large Language Models (LLMs) have attracted significant attention. LLMs offer the ability to flexibly integrate multiple conditions, enabling highly personalized recommendations. However, this flexibility often results in longer prompts, which can degrade response accuracy. Additionally, optimal prompt structures may vary across LLM versions, leading to inconsistent performance.To address these issues, we propose a dynamic prompt optimization method that segments prompts based on LLM evaluations. We conducted experiments using ChatGPT-4o and evaluated the effectiveness of prompt segmentation to verify the feasibility of the proposed method. The experimental results demonstrate that prompt segmentation improves recommendation accuracy but that its effectiveness saturates beyond a certain threshold. These findings suggest that determining the optimal number of prompt segments maximizes output quality while preventing unnecessary segmentation.