Following the flip interaction method, I gave both ChatGPT and Microsoft Copilot the same task using the exact same prompt and details. My prompt was:
“Please act as a travel agent, helping me plan for a summer vacation in Europe. Before you begin, ask me any questions that will help you do a better job of planning an itinerary for my travels.”
And the details I provided were:
“2-week trip in early or mid-June, Budget: $2000, Departure: New York City, No visa needed, Interested in destinations like Paris, Amsterdam, Vienna, Berlin, etc., Prefer fewer countries with more time in each, Priorities: budget travel, sightseeing, cultural and food experiences, Interests: cities, nature, beaches, mountains, historical sites, Solo traveler, Cheapest accommodations, Carry-on only, No food or activity restrictions”
Both language models gave me similar responses. They each provided detailed itineraries with working links, which was impressive. However, I found ChatGPT’s answer to be more detailed, well-structured, and overall more polished. Copilot also did a good job, but its suggestions were a bit less comprehensive.
I also experimented with the persona pattern and noticed that both models responded in similar ways. However, when I asked them, “If you were me, would you be happy with this itinerary?” ChatGPT’s answer felt much more human, thoughtful, and convincing. Copilot’s response, on the other hand, was more generic and lacked a personal touch.
Finally, I asked both of them, “Can you give me links that can help me prompt you better?” ChatGPT understood my question clearly and gave me very accurate and useful resources. Copilot, surprisingly, didn’t seem to grasp the question and gave me an irrelevant response.
Overall, this experiment taught me a lot about the power of prompting. It’s clear that the way we communicate with AI plays a big role in how helpful it can be. Knowing how to ask the right questions and which AI tool to use, can make all the difference when trying to get the best results. One important insight I gained came from reading White et al.’s work on prompt patterns was, “A prompt is a set of instructions provided to an LLM that programs the LLM by customizing it and/or enhancing or refining its capabilities” (White et al., 1). I think prompting is more than a skill. It’s about clearly defining problems and using the right approach to solve them.