This tutorial highlights effective strategies for Developer Advocates to manage user forum feedback using AI. By leveraging large language models (LLMs) for semantic clustering, users can streamline insights from vast amounts of data. The author shares methods for data organization, including sentiment analysis of Discord messages, effectively categorizing discussions into conversation turns. Utilizing vectorDB technologies like Zilliz/Milvus, the tutorial presents practical steps to create meaningful clusters of user help requests, enhancing the understanding of user queries across various tech platforms.
As a Developer Advocate, it's challenging to keep up with user forum messages and understand the big picture of what users are saying.
In this tutorial, I'll show you an AI hack to perform semantic clustering simply by prompting LLMs!
I also added a simple, hard-coded binary sentiment score (0 for not happy and 1 for happy) based on whether the user said thank you anytime in their thread.
The first step was to convert the anonymized data into a pandas data frame with columns: score, user, role, message, timestamp, thread, user_turns.
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