The key message components when working with GPT models are role and content. role (system/user/assistant) identifies the speaker and guides the model’s style or behavior; content holds the message text. Distinguishing roles is essential for a correct dialogue simulation and expected behavior.
system messages set instructions, context, and constraints (style, tone, rules). user messages are the user’s inputs (questions, instructions) that the model should answer. Clear role separation helps control the model effectively.
Example of system influence: “Reply in the style of a playful poet” — the model will follow that style.
Message order shapes context and influences answers: user turns are interpreted in light of earlier system instructions and the conversation history.
In the customer review classification example, the categories are “Positive”, “Negative”, and “Neutral”.
Classifying movie review sentiment is useful for aggregating viewer opinions. Possible categories: “Positive”, “Negative”, “Neutral”.
Classifying news topics helps manage content and recommendations. Possible categories: “Politics”, “Technology”, “Sports”, “Entertainment”.
In classification, the user_message should contain the text to be labeled; keep it clear and concise so the model has enough context for an accurate result.
Classifying social‑post tone helps with moderation (flagging inappropriate content) and marketing (analyzing audience engagement). Example tones: “Serious”, “Ironic”, “Inspiring”, “Irritated”.