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Lar Lista de Prompts de IA AI Classificação de Tickets EC

Classificação de Tickets EC

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AI
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Use IA para identificar e classificar automaticamente os tickets de consulta de clientes de comércio eletrônico, melhorando a eficiência e precisão do processamento.

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Please analyze the following e-commerce customer inquiry ticket content, automatically categorize it into one of the four categories: 'Logistics Inquiry', 'Return/Exchange Application', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the basis for classification, ensuring that the classification results conform to actual business scenarios and are logically clear.

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Por favor, analise o conteúdo dos seguintes tickets de consulta de clientes de comércio eletrônico, classifique-os automaticamente em uma das quatro categorias: 'Consulta logística', 'Solicitação de devolução/troca', 'Reclamação de qualidade do produto' ou 'Outra consulta' com base na semântica comercial, e explique brevemente a base da classificação, garantindo que os resultados da classificação estejam em conformidade com os cenários comerciais reais e sejam logicamente claros.

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Analyze the following raw customer inquiry text from an e-commerce platform and categorize it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If the text involves multiple intents, prioritize the most urgent or core issue and briefly explain the reasoning. For instance, if a user mentions 'the shirt size is wrong and I want to exchange it', categorize it as 'Return/Exchange Request'. Output only the category and a short rationale without any conversational filler or explanatory prefixes.
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Act as an intelligent customer service system for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and emotional tone, and automatically classify it into standard categories such as pre-sales consultation, after-sales service, logistics inquiry, or complaints. Extract key entities like order numbers, product names, and issue descriptions, then output a structured ticket summary to facilitate quick intervention by human agents.
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