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Answers 1.2

Theory

  1. The key components of a message in the context of interacting with OpenAI's GPT models are role and content. The role specifies whether the message is from the system or the user, guiding the AI on how to frame its response. Distinguishing between them is important for simulating a dynamic exchange and for the AI to understand and respond appropriately to the task at hand.

  2. 'System' messages provide instructions, context, or constraints, shaping the AI's behavior, personality, or response style. 'User' messages, on the other hand, are inputs from the user's perspective, such as queries or statements, that the AI responds to. The distinction helps in crafting interactions that elicit desired responses from the AI.

  3. An example of how a 'system' message can dictate the AI's behavior is instructing the AI to respond in the style of a whimsical poet. This message sets the tone and style for the AI's responses, ensuring they match the whimsical, poetic context requested by the user.

  4. The sequence of messages influences the AI model's response by providing a contextually rich background for its replies. It ensures that the AI's responses are aligned with both the direct inputs from the user and the overarching instructions or context provided by the system, enabling more nuanced conversations.

  5. The categories available for classifying customer feedback in the provided example are "Positive", "Negative", or "Neutral". This classification helps in understanding customer satisfaction and areas of improvement.

  6. Classifying the sentiment of a movie review could be beneficial for aggregating consumer opinions on films, helping potential viewers make informed decisions. Categories for classification could include "Positive", "Negative", and "Neutral".

  7. Classifying the topic of a news article helps in content management by organizing articles into categories for easier navigation and in recommendation systems by suggesting articles of interest to readers. Examples of categories include "Politics", "Technology", "Sports", and "Entertainment".

  8. Classifying customer inquiries is crucial in a business setting to efficiently direct queries to the appropriate department, improving response times and customer satisfaction. Categories could include "Billing", "Technical Support", "Sales", and "General Inquiry".

  9. The 'user_message' in AI classification tasks should contain the text that needs to be classified. It should be structured clearly and concisely to provide the AI with enough context to make an accurate classification into predefined categories.

  10. Classifying the tone of social media posts benefits content moderation by identifying and managing inappropriate content and informs marketing strategies by analyzing audience engagement. Tone categories could include "Serious", "Humorous", "Inspirational", and "Angry".