Microsoft collects an immense amount of data from the users of their product-self-help documentation. Employees use this data to identify these self-help articles' performance trends and measure their impact on business Key Performance Indicators (KPIs). Microsoft uses various tools like Power BI and Python to analyze this data. The problem is that their analysis and findings are summarized manually. Therefore, this research will improve upon their current analysis methods by applying the latest prompt engineering practices and the power of ChatGPT's large language models (LLMs). Using VBA code, Microsoft Excel, and the ChatGPT API as an Excel add-in, this research will help Microsoft employees more easily identify trends in self-help article metrics, understand the drivers of these trends, and make business decisions that provide the highest return on investment.
Herrin, Ryan; Stodgel, Luke; and Raffety, Brian
"A Prompt Engineering Approach to Creating Automated Commentary for Microsoft Self-Help Documentation Metric Reports using ChatGPT,"
SMU Data Science Review: Vol. 7:
3, Article 3.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss3/3
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