According to the source, a Penn State University study reports that using blunt or rude prompts led large language models to produce sharper, more accurate answers versus polite phrasing in controlled evaluations. Source: Penn State University. This finding challenges the common assumption that polite prompts improve model accuracy and instead highlights tone as a measurable lever in prompt engineering. Source: Penn State University. Prior research shows prompt strategy materially affects LLM task performance, reinforcing that instruction style can shift accuracy outcomes in reasoning and QA tasks. Source: Google Research, Chain-of-Thought Prompting (Wei et al., 2022) and Kojima et al., 2022. Because a majority of institutional traders cite AI and machine learning as the most influential technology in markets, prompt techniques that measurably raise model accuracy are operationally relevant to research workflows, trading assistants, and crypto-market analytics tied to the AI narrative. Source: J.P. Morgan e-Trading Trends Survey 2024.