Large Language Models (LLMs) are increasingly embedded in software engineering workflows, yet the quality of the prompts used to guide these models remains largely unassessed. Poorly formulated prompts can lead to ambiguity, degraded output quality, or biased and unsafe model behavior. In this paper, we conduct a preliminary exploratory study aimed at establishing an initial foundation for systematic promptquality evaluation. First, we perform a rapid review of the literature on prompt engineering and analyze real-world prompts to derive a preliminary taxonomy of prompt smells—recurring patterns that signal potential quality issues. Each smell is operationalized through a quantitative metric capturing aspects such as grammatical correctness, readability, contextual adequacy, complexity-length, formality mismatch, and bias. We then apply these metrics to an existing corpus of developerwritten prompts and analyze their relationships using correlation analysis and multiple regression models. Our results show that syntactic metrics (e.g., grammar, readability) are strongly interrelated and largely explain the composite Prompt Quality Score, whereas higher-level smells such as contextual relevance, formality mismatch, and bias exhibit weak correlations and low explained variance. These findings suggest that many important prompt-quality issues operate independently of surface-level text properties and may require more semantically informed assessment techniques. Overall, this study offers an initial conceptual and empirical basis for the systematic evaluation of prompt quality, paving the way for more robust prompt engineering practices and automated quality-checking tools.
Porta et al. (Tue,) studied this question.