This paper explores the field of AI prompt engineering, specifically understanding Large Language Model (LLM) training in order to optimize response efficacy, emphasizing the critical stages of data collection, preprocessing, and annotation. Our research outlines key principles of effective prompt engineering, including clarity, specificity, conciseness, engagement, and goal orientation. Through various experiments, we demonstrate how specificity in prompts enhances the detail and accuracy of LLM responses. We also examine the impact of techniques like "Chain of Thought" prompting paired with complementary strategies to extract even more productive responses. Finally, we provide a formula for crafting effective prompts and discuss the broader implications of prompt engineering in fields such as education and programming, showcasing its transformative potential. This comprehensive survey serves as a practical guide for navigating the complexities of AI model training and prompt engineering.
I B Singh (2024) studied this question.