PROSE: Prompt Refinement, Optimization and Semantic Evaluation

Abstract: Prompt optimization has emerged as a critical technique for improving the performance, efficiency, and reliability of Large Language Models (LLMs) without modifying their underlying architecture or parameters. Instead of retraining models, optimized prompts guide the model to generate more accurate, consistent, and context-aware responses. This paper presents a systematic approach to prompt optimization by analyzing prompt structures, refinement strategies, and evaluation techniques. The proposed system focuses on improving response relevance, reducing ambiguity, and minimizing token usage through iterative prompt tuning and rule-based optimization. Experimental observations demonstrate that optimized prompts significantly enhance output quality while reducing computational overhead. The study highlights prompt optimization as a cost-effective and scalable solution for real- world AI applications.

Keywords: Semantic Evaluation, Context-Aware Optimization, Natural Language Processing, Automated Prompt Refinement.