...

• 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

The chatbot will be designed around a modular architecture, with each department being assigned it’s own model trained and fine-tuned on it’s specific data. This will allow for modular development and training of models, minimizing the impact to models of other departments.

On top of the department-specific models will be a routing model, trained on a set of distinct chat scenarios for each department for the purposes of routing chat to the correct department model.

User will interact with the AI ensemble via chat interface which can be embedded on University web pages or made available via mobile app.

...