Crafting Effective Prompts: Enhancing AI Performance through Structured Input Design

Abstract

Prompt engineering is a vital skill in the field of natural language processing (NLP) that involves crafting specific instructions to guide language models (LMs) in generating accurate and relevant outputs. The quality of prompts significantly influences the efficacy of AI models, with well-structured prompts improving response accuracy from 85% to 98%. This paper explores various types of prompts, such as rule-based, context-based, and machine learning-based prompts, along with common pitfalls and strategies for improvement. It highlights the need for clarity, specificity, and iterative refinement to enhance AI-generated outputs. Additionally, the ethical implications of biased prompts are addressed, emphasizing the importance of fair and balanced designs.