Evaluating and Improving Prompt Quality in LLM-Based Assistants: A Synthesis of Criteria and Indicators
Journal
Hawaii International Conference on System Sciences (HICSS)
Type
conference paper
Date Issued
2026-01-06
Author(s)
Research Team
IWI6
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.
Language
English
Keywords
large language models
prompt engineering
prompt quality
evaluation criteria
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher place
Maui, Hawaii, USA
Pages
10
Event Title
Hawaii International Conference on System Sciences (HICSS)
Event Location
Maui, Hawaii, USA
Event Date
06.01.2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
JML_1061.pdf
Size
566.28 KB
Format
Adobe PDF
Checksum (MD5)
1b57264aa268c147bf23a18a2788759b