Evaluating and Improving Prompt Quality in LLM-Based Assistants: A Synthesis of Criteria and Indicators

Abstract

Generative AI (GenAI) assistants, particularly large language models (LLMs), are gaining increasing relevance across domains. The quality of outputs generated by these systems is highly contingent on the input prompts, giving rise to new professional roles such as prompt engineers. In this study, we systematically examine evaluation criteria and optimization methods that can improve prompt quality. Drawing on a systematic literature review, we identify key criteria, including clarity, accuracy, and precision, and initial measurement techniques. In addition, we synthesize common optimization methods such as iterative refinement and shot-based prompting. Our work contributes to the growing efforts to standardize the evaluation and improvement of prompts in interactions with LLM-based assistants, thereby fostering a more rigorous and coherent understanding of the prompt quality construct.