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Expanding High Level pro‎mp‎ts with AI Agents for Detailed LLM Instruction

This paper presents a novel approach to improving large language model (LLM) performance by refining high-level pro‎mp‎ts into detailed, stepwise instructions using a multi-agent system. The method is designed to enhance the accuracy an‎d reliability of LLM outputs across a wide range of tasks. We explore existing research in pro‎mp‎t engineering an‎d propose a modular system that integrates decomposition, expansion, synthesis, an‎d validation to optimize pro‎mp‎t quality. Through experimental eva‎lua- tion, we demonstrate the effectiveness of this system in improving LLM task execution.