Two teams are tasked with developing a system to classify customer feedback emails into 'Positive', 'Negative', or 'Neutral' categories.

Team Alpha uses a large, general-purpose language model. For each email they want to classify, they provide the model with a detailed set of instructions, including definitions of each category and several examples of correctly classified emails.

Team Bravo starts with the exact same general-purpose language model. However, before using it for classification, they perform an additional training phase using a dataset of 50,000 customer emails, each already labeled with the correct category.

After Team Bravo completes its training, analyze the fundamental difference in how each team's system accomplishes the classification task. Specifically, explain why Team Bravo's system will likely require far simpler instructions than Team Alpha's system to achieve high accuracy.