A professor wants to generate a new case study. Analyze the two prompts below, which are designed to accomplish the same task. Which prompt is more effective according to the PARTS framework?
The more effective prompt is: “Act as a bioethics professor. Create a case study for a 200-level undergraduate class. The case must focus on a gene-editing dilemma. It should end with three Socratic questions.”
This prompt succeeds because it carefully follows the structured guidelines of the PARTS framework, a proven method for getting precise and usable outputs from AI models.
First, it establishes a distinct Persona (“Act as a bioethics professor”), ensuring the system adopts the correct academic tone and level of expertise. It clearly identifies the Audience (“200-level undergraduate class”), which allows the tool to adjust the vocabulary and complexity of the scenario to fit that specific group of students.
Additionally, the prompt delivers a direct Request (“Create a case study”) alongside exact Specifics and Structure (“focus on a gene-editing dilemma,” “end with three Socratic questions”). Setting these firm boundaries guarantees the generated material will be highly relevant and formatted exactly the way the instructor needs it.
In contrast, the second prompt fails because it is far too vague. It asks a broad question without assigning a role, defining the student demographic, or outlining any formatting rules. Without these critical constraints, the AI will likely output a generic, unfocused response that requires significant manual editing before it can actually be used in a classroom setting.