Which prompt engineering technique most effectively guides ChatGPT through the multi-step process of developing a persuasive advertising narrative?

Chain-of-thought prompting is the most effective prompt engineering technique for guiding ChatGPT through the multi-step process of developing a persuasive advertising narrative. Chain-of-thought prompting involves explicitly instructing the model to break down the task into a series of intermediate reasoning steps before arriving at the final advertising narrative. This technique enhances the model's ability to generate more coherent and logically sound outputs by forcing it to think through each stage of the narrative development process. For example, instead of simply asking ChatGPT to 'write an ad about a new coffee brand,' a chain-of-thought prompt would guide it through steps like: 'First, identify the target audience's key needs related to coffee. Second, define the unique selling proposition of the coffee brand. Third, construct a narrative arc that highlights a problem, introduces the coffee as a solution, and showcases the positive outcome of using the coffee. Finally, write the ad copy based on these elements.' By breaking down the task into these distinct steps, the model is less likely to produce generic or nonsensical content and more likely to generate a well-reasoned and persuasive advertising narrative. The prompts encourage the model to think more methodically, improving the quality and effectiveness of the output.