Optimizing LLM Strategies for Playing Mendikot using Prompt Engineering

This study investigates the integration of a large language model (LLM) enhanced by prompt engineering and game theory to effectively engage in the strategic card game Mendikot. By refining complex prompts and leveraging a tailored visual understanding of game dynamics, we significantly bolster the decision-making prowess of the LLM. Our methodology involved the systematic simplification of game prompts to facilitate deeper learning and faster response times, coupled with the implementation of a visual recognition system to interpret and react to game states dynamically. The results illustrate that the adapted LLM outperforms traditional AI approaches in strategic decision-making tasks, underscoring a substantial improvement in both the accuracy and efficiency of game-play. This research not only demonstrates a viable model for enhancing AI interaction in recreational gaming but also opens avenues for deploying advanced AI strategies in complex strategic environments, offering insights into the broader application of AI in leisure and competitive arenas. The findings suggest that AI can transcend conventional gaming roles, potentially transforming strategic gameplay in digital and physical platforms.