Systematic Study of Prompt Engineering
The study categorizes prompt engineering techniques into instruction-based, information-based, reformulation, and metaphorical prompts, and addresses ethical considerations in prompt engineering, emphasizing the need to mitigate bias and discrimination while ensuring transparency.
Abstract
Abstract: Now a days Generative Artificial Intelligence is the buzz in the field of technology and science it is the implementation of the Artificial intelligence to generate different types of contents with the help of its models and ease the human life to a extend. Prompt Engineering is one of the arts of crafting instructions to guide large language models (LLMs), and has emerged as a critical technique in natural language processing (NLP). This systematic study delves into the intricacies of prompt engineering, exploring its techniques, evaluation methods, and applications. The study categorizes prompt engineering techniques into instruction-based, information-based, reformulation, and metaphorical prompts. It emphasizes the importance of evaluating prompt effectiveness using metrics like accuracy, fluency, and relevance. Additionally, the study investigates factors influencing prompt effectiveness, including prompt length, complexity, specificity, phrasing, vocabulary choice, framing, and context. The study highlights the impact of prompt engineering in enhancing LLM performance for NLP tasks like machine translation, question answering, summarization, and text generation. It underscores the role of prompt engineering in developing domainspecific LLM applications, enabling knowledge extraction, creative content generation, and addressing domain-specific challenges. The study concludes by addressing ethical considerations in prompt engineering, emphasizing the need to mitigate bias and discrimination while ensuring transparency