Optimizing Human–AI Collaboration in Chemistry: A Case Study on Enhancing Generative AI Responses through Prompt Engineering
Year: 2024
Type: article
Abstract: “Are we asking the right questions?” seems cliché, but for ChatGPT, it is a pivotal tool to ensure the accuracy of responses. While ChatGPT-3.5’s training on the vast database promises to revolutionize STEM education and research, this investigation shows the importance of precise communication and prompt engineering in guiding ChatGPT-3.5 toward reliable and accurate responses, particularly in chemistry. For instance, emphasizing context, clearly defining symbols, and focusing on field-specific instructions can dramatically improve its performance. Furthermore, avoiding open-ended prompts and strategically using repetition can further enhance its accuracy. The iterative prompt design, demonstrated through a series of adjustments, illustrates how seemingly minor refinements, such as substituting “least” for “lowest”, profoundly impact the output. This study highlights the essential role of human oversight, including the construction of well-crafted prompts, in guarding reliable information and nurturing a productive “Human–AI” (HAI) partnership.
Source: Chemistry
Authors: Dinesh V. Vidhani, Manoharan Mariappan
Institution: Miami Dade College
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,046
OpenAlex ID: W4401579646