Prompt engineering is the practice of designing and refining input prompts to guide the behavior of generative AI models, especially large language models (LLMs), toward producing more accurate, relevant, or useful outputs. Because these models rely heavily on the context and phrasing of input text, prompt engineering plays a crucial role in helping users achieve desired results across a wide range of applications—from content generation and summarization to coding, data analysis, and customer support.
Effective prompt engineering can involve techniques like few-shot prompting, instruction tuning, chain-of-thought prompting, or role definition (e.g., “You are a financial analyst…”). This emerging discipline is essential for both technical and nontechnical users looking to operationalize AI tools in business environments, and it can significantly reduce the time and effort needed to get value from generative AI systems.