Pre-Processing the Prompt to generate an efficient Output for Unit Test Generation

Abstract

Large Language Models (LLMs) have shown remarkable performance across a wide range of natural language processing tasks, yet their effectiveness remains highly sen sitive to prompt formulation. Manual prompt engineering is often labor-intensive, inconsistent, and difficult to scale. This thesis investigates the landscape of auto matedpromptoptimization,analyzingrecentadvancementssuchasnaturallanguage gradient-based refinement, Bayesian Optimization for discrete prompt tuning, evolu tionary strategies, and joint fine-tuning approaches. Through a comprehensive com parison of these techniques, the study identifies key trends, limitations, and opportu nities in existing methods. Building on these insights, we propose a novel framework that aims to combine the interpretability of natural language-based refinements with the efficiency of automated search strategies. Experimental evaluations demonstrate improvedtaskperformanceandpromptrobustnessacrossmultipleLLMbenchmarks. This research contributes a unified perspective on automated prompt optimization and paves the way for more adaptive and accessible prompt engineering methodolo gies.