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A customer support company is using a large language model developed with Amazon Bedrock to enhance its automated chatbot's ability to understand and respond accurately to customer queries. To improve the chatbot's performance, the company wants the model to correctly identify the intent behind various user interactions, such as whether a user is asking for a refund, seeking product information, or needing technical support. To achieve this, the company decides to use few-shots prompting to train the model effectively. Given this goal, what type of data should be included in the few-shots examples to help the model accurately recognize and distinguish the correct user intent?