Prompt engineering is a process of designing and crafting prompts or instructions for natural language processing models (NLP) like ChatGPT and other similar AI tools. The goal of prompt engineering is to elicit specific and desired responses from these NLPs by carefully crafting the input text or query.
When crafting your query, consider the following:
Lo (2023) suggests the CLEAR framework for prompt engineering to facilitate more effective generated content.
For example, instead of requesting, "Please provide me with an extensive discussion on the factors that contributed to the economic growth of China during the last few decades", use a concise prompt like, "Identify the factors behind China's recent economic growth."
For example, a logically structured prompt could be, "Describe the steps in the scientific method, starting with forming a hypothesis and ending with drawing conclusions."
For example, instead of, "What are some renewable energy sources?", and explicit prompt would be, "Identify five renewable energy sources and explain how each works."
For example, if asking, "What are some ways to conserve water?" leads to generic responses, try a more targeted and adaptive prompt like, "List household practices for conserving water and their potential impact."
For example, after receiving AI-generated content, evaluate the response's accuracy, relevance, and completeness. Use insights from the evaluation to refine prompts, such as asking for more specifics or focusing on certain aspects.