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https://github.com/Tufts-Technology-Services/jumbo-bot
Overview
As we seek to improve and streamline user experiences across departments of the University, chatbots have been proposed by various departments as a means to accomplish this goal. While there are many different chatbot offerings in the market today, TTS seeks to create a single, unified chatbot in-house to serve all departments, leveraging Generative AI.
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Data: “Garbage in, garbage out” The training of any model hinges on accurate, high quality data in an optimal format. If a department lacks this, it can hinder development and even worse, lead to unreliable model output. Data can broken down into the following aspects:
Accuracy: The data for a process is accurate and up-to-date
Volume: There’s an adequate amount of data to train, per model requirements
Format: Data is in an adequate format for training
Hallucinations: If a model lacks adequate data on a particular topic it can potentially provide false information. Tuning of model parameters (e.g temperature) can help minimize hallucinations.
Infrastucture Costs
Design
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