Refining Argumentative Prompts

Study Context

The full study aimed to evaluate AI models' skills in language comprehension, rhetorical ability, and logical reasoning. The AI task required models to craft a well-reasoned argument on why prompt engineering should be seen as an essential skill in AI work rather than a distinct profession. The results of this assignment showed how these AI models handle complex and argument organising.

Purpose of substudy

In this substudy, we analysed the original prompt, developed an improved version, and tested both prompts on the models for comparison.

Initial Prompt Design

The initial prompt, as used in our full study, was as follows:

This prompt incorporated several techniques:

• Instructional prompting: Clear instructions guided the AI to construct a specific type of response - a convincing argument.
• Specificity: Focusing on why prompt engineering is a skill rather than a standalone job made the task narrowly defined, encouraging precision.
• Contextual prompting: The prompt was framed within the context of AI technology, encouraging the models to use their understanding of the field.
• Role-based prompting: Implicitly, the AI was expected to act as an argumentative writer, persuading the reader through well-reasoned points.
• Style transfer: By asking for convincing arguments, the prompt influenced the AI's tone to be persuasive.
• Knowledge generation: The AI was tasked with generating or accessing information about the relevance of prompt engineering in the job market.
• Complexity-based prompting: The challenge - arguing that prompt engineering is essential but not a standalone job - required a nuanced approach.
• Open-ended prompting: The prompt allowed the AI to explore different angles and construct its reasoning creatively.

Revised Prompt for Enhanced Performance

Based on the output of the models, the prompt was revised to provide clearer instructions and a more structured approach for developing the argument. The revised version aimed to guide models toward a well-organised, evidence-based, and audience-appropriate response:

Key Improvements in the Revised Prompt

The revised prompt showed clear advantages over the initial one, including:

• Enhanced clarity: The revised prompt provided explicit instructions, ensuring that the AI models understood the task more precisely. For example, the thesis clearly emphasised why prompt engineering should be viewed as a core skill, preventing digressions into unrelated topics - a problem seen in the earlier prompt due to its informal tone.
• Comprehensive coverage: By instructing the AI to use examples, logical reasoning, and counterarguments, the revised prompt led to more well-rounded arguments. Responses effectively used analogies (e.g., comparing prompt engineering to communication skills) and addressed counterarguments, enriching the analysis by anticipating objections.
• Audience appropriateness: Specifying that the argument should target an audience familiar with AI ensured the responses were relevant and technical, making the essays more credible. The initial prompt's casual tone, by contrast, often reduced its effectiveness with professional readers.
• Improved persuasiveness: The inclusion of counterarguments made the responses more compelling. One example rebutted the idea that prompt engineering should be a standalone profession, adding intellectual depth to the argument. This contrasted with the earlier prompt, where responses often lacked counterarguments or presented weakly developed ones.
• Better organisation: The revised prompt emphasised structure, resulting in more coherent essays that logically built their arguments. Responses progressed step by step, improving readability. The initial prompt's responses, in comparison, often suffered from fragmented reasoning and abrupt transitions, weakening the arguments.

Conclusion

This substudy clearly demonstrates that refining prompts can significantly enhance AI performance, particularly in tasks requiring advanced reasoning and rhetorical skills. The revised prompt produced more persuasive and logically sound responses than the initial, less-focused prompt. This highlights the pivotal role of prompt design, not just as a method for eliciting responses, but as a factor that shapes the quality, depth, and effectiveness of AI-generated content.