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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

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.

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