One simple and exciting use case for LLMs is organizing data into tables. Perhaps you have a bunch of news articles or business reports, and you would like all of the important points to be summarized in a table that you can then put into a spreadsheet or database. Chat bots like ChatGPT can help you do this.
We can extract information from the following report by appending Generate a table containing this information:
to it.
In a recent business report presentation, the CEO of Zana Corp. highlighted their remarkable growth in the past fiscal year. She shared that the company experienced a 15% increase in revenue, reaching $50 million, with a 12% profit margin ($6 million in net profit). The report also showcased a 20% growth in their customer base, now totaling 100,000 customers. Additionally, the company's operating expenses went up by 10%, amounting to $10 million, while the employee headcount increased by 25%, resulting in a current workforce of 500 employees.
Generate a table containing this information:
ChatGPT will output a table like the following:
Metric | Value |
---|---|
Revenue | $50 million |
Profit Margin | 12% |
Net Profit | $6 million |
Customer Base | 100,000 |
Operating Expenses | $10 million |
Employee Headcount | 500 |
Revenue Increase | 15% |
Customer Increase | 20% |
Operating Expenses Increase | 10% |
Employee Headcount Increase | 25% |
You can then copy and paste it into a spreadsheet like Excel/Sheets or even documents and powerpoints.
Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.