"Prompt engineering is the process of constructing queries or inputs (i.e. prompts) for AI language models so as to elicit the most precise, coherent, and pertinent responses. In essence, it is the art of fine-tuning the questions or commands provided to AI models in order to optimize their performance and guarantee that they produce the desired results" (Lo, 2023).
A YouTube playlist on prompt engineering for generative AI by Aleksandar Popovic, teaching assistant at the Illinois Institute of Technology
The CLEAR Framework is one way to structure prompts and comes from Dr. Leo S. Lo, Dean and Professor of the College of University Libraries and Learning Services at the University of New Mexico.
Brevity and clarity in prompts
Use a more concise and explicit prompt such as “Explain the process of photosynthesis and its significance” instead of “Can you provide me with a detailed explanation of the process of photosynthesis and its significance?”
Structured and coherent prompts
A logically structured prompt could be, “Describe the steps in the scientific method, starting with forming a hypothesis and ending with drawing conclusions.”
Clear output specifications
Instead of, “Tell me about the French Revolution,” an explicit prompt would be, “Provide a concise overview of the French Revolution, emphasizing its causes, major events, and consequences.”
Flexibility and customization in prompts
If an initial prompt such as “Discuss the impact of social media on mental health” elicits responses that are too general, consider a more focused and adaptable prompt such as “Examine the relationship between social media usage and anxiety in adolescents.”
Continuous evaluation and improvement of prompts
After receiving AI-generated content on the benefits of a plant-based diet, evaluate the response's accuracy, relevance, and completeness. Use insights from the evaluation to refine future prompts, such as asking for more specific benefits or focusing on certain aspects of a plant-based diet.
Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship 49(4). https://doi.org/10.1016/j.acalib.2023.102720