Summary:
This blog post is part of a series aimed at helping educators harness the power of AI through effective prompt engineering. Generative AI can tailor backstories to fit the specific needs and interests of different student groups, making the learning experience more relevant and personalized. Additionally, AI serves as a source of inspiration, offering new ideas and perspectives that professors might not have considered, thereby enriching the overall educational experience. You can use any Generative AI App. I’m using Microsoft Copilot (Opens in a new tab). Type, Copy/Paste the prompts below and review the results.
Prompt 1a: Problem set data
I need a fake data set for testing for an computer science assignment. Could you give me a fictional data set for how AI is trained? I’d like dataset to be designed to train an AI model to classify customer reviews into positive, neutral, or negative sentiments.
Response 1a:
Sure! Here’s a fictional dataset designed to train an AI model to classify customer reviews into positive, neutral, or negative sentiments:
| Review ID | Customer Review | Sentiment |
|---|---|---|
| 1 | “Absolutely loved the product! Exceeded my expectations.” | Positive |
| 2 | “The service was okay, nothing special.” | Neutral |
| 3 | “Terrible experience. The item arrived broken and customer service was unhelpful.” | Negative |
| 4 | “Great quality and fast shipping. Will buy again!” | Positive |
| 5 | “It’s fine, but I’ve seen better.” | Neutral |
| 6 | “Very disappointed. Not worth the money.” | Negative |
| 7 | “Fantastic! Highly recommend to everyone.” | Positive |
| 8 | “Meh, it was alright. Not too impressed.” | Neutral |
| 9 | “Worst purchase ever. Completely dissatisfied.” | Negative |
| 10 | “Excellent product and amazing customer support.” | Positive |
Feel free to expand or modify this dataset as needed for your assignment. If you need more examples or specific types of reviews, just let me know!
Prompt 1a: Feedback for a .csv file
Could you make that into a .csv file?