PROMPT ENGINEERING AND STYLISTIC CONTROL IN LARGE LANGUAGE MODELS: A QUANTITATIVE ANALYSIS OF FORMALITY, EMOTIONAL TONE, AND PERSUASION

Hossain Md. Sayem, Arif Md. Ismail Hossain

Abstract


This study investigates the extent to which stylistic features of Large Language Model (LLM) output can be controlled through prompt engineering. Drawing on established frameworks in stylistics, we examine three core stylistic dimensions formality, emotional tone, and persuasive style across six controlled prompt conditions using the DeepSeek LLM. Through a mixed-methods approach combining qualitative textual analysis with quantitative measurement of six linguistic metrics (average sentence length, technical term density, emotion marker frequency, modal verb usage, contraction frequency, and a composite formality index), we demonstrate that systematic variations in prompt structure produce measurable and linguistically interpretable stylistic shifts in generated text. Our findings indicate that explicit stylistic instructions yield stronger effects than implicit contextual cues, and that different stylistic dimensions respond differently to prompt manipulations. Specifically, formal prompts produced texts with 137% longer sentences and 185% more technical terms than informal prompts; emotional prompts generated up to 7.19 emotion markers per 100 words compared to zero in neutral conditions; and directive persuasive prompts exhibited 445% more modal verbs than formal neutral texts. These results provide empirical support for applying classical stylistic theory to AI-generated text and offer practical guidance for linguistically informed prompt engineering.

Keywords


large language models, prompt engineering, stylistics, computational linguistics, DeepSeek

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DOI: http://dx.doi.org/10.46827/ejals.v9i2.746

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