精选高质量 AI 提示词列表

利用自然语言处理技术，对电商平台用户提交的售后咨询与投诉工单进行语义分析与意图识别，自动归类至物流、质量或退款等具体业务模块，以提升客服响应效率与处理准确率。

Analyze the following e-commerce customer support ticket text to identify the core issue and determine its business category. The content involves common scenarios such as damaged goods, delayed shipping, or refund requests. Extract key entities like order numbers, product names, and problem descriptions. Map the ticket to the most appropriate category based on predefined standards including logistics issues, product quality, after-sales service, and others. If the user expresses strong emotions or uses sensitive language, flag it as high priority. The final output should include the classification result, confidence score, and brief handling suggestions, ensuring clear logic that aligns with actual business operations to help the customer service team quickly triage and prioritize urgent cases.

基于自然语言处理技术，自动识别并分类电商客户咨询工单，提升客服响应效率与准确率。

Analyze the following raw customer inquiry text submitted via e-commerce channels and classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. If the text does not clearly indicate a specific business scenario, assign it to 'Other Inquiry'. Output only the category label name without explaining the reasoning process or providing additional suggestions.

基于自然语言处理技术，自动识别并分类电商客户咨询工单，提升客服响应效率与准确率。

Act as an intelligent customer service assistant for an e-commerce platform. Receive the raw text of a user's inquiry, analyze its core intent and sentiment, and categorize it into specific business scenarios such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entities like order numbers, product names, and problem descriptions, then output a structured ticket summary to facilitate quick handling by human agents.

利用自然语言处理技术，对电商平台用户提交的售后咨询与投诉工单进行语义分析与意图识别，自动归类至物流、质量或退款等具体业务模块，以提升客服响应效率与处理准确率。

Analyze the following e-commerce support ticket text to identify the user's core request and emotional tone, then automatically categorize it into specific business modules such as logistics delay, product quality issue, return/refund application, or general inquiry, while extracting key entities like order ID, product name, and problem description for subsequent manual agent intervention.

指导AI根据用户留言内容，自动识别并分类电商客服工单类型，如物流查询、退换货申请或产品咨询，以提升处理效率。

Please read the following customer service message submitted by the user, analyze its core request, and categorize it into one of the predefined ticket types: Logistics Inquiry, Return/Exchange Request, Product Quality Issue, Account Anomaly, or Other. Output only the classification label without explaining the reasoning process, ensuring accurate classification to assist subsequent manual handling.

针对电商客服场景，生成用于自动评估对话质量、识别违规及优化服务体验的AI提示词。

Act as a senior e-commerce customer service quality monitoring expert to analyze the provided customer-service dialogue records. Focus on checking if the customer service accurately understood the customer's needs, used standard polite language, effectively resolved complaints or inquiries, and avoided shirking responsibility or emotional responses. Identify key risk points in the dialogue, such as unfulfilled promises, incorrect information, or poor attitude, and provide specific improvement suggestions. The final output should include a dialogue quality score, a description of main issues, and an optimized reply example to ensure improved customer satisfaction and compliance.

为电商客服提供基于订单数据的智能回复建议，提升客户满意度。

Act as a senior e-commerce customer service expert. Based on the provided order details and common after-sales issues, generate a professional, friendly, and efficient reply text. The reply must include confirmation of the order status, proactive answers to potential issues, and specific solution suggestions. Maintain a polite and empathetic tone, avoiding mechanical template language, ensuring the content is natural, fluent, and effectively resolves customer concerns.

基于自然语言处理技术，自动识别并分类电商客户咨询工单，提升客服响应效率与准确率。

Act as a senior customer service manager for an e-commerce platform. Analyze the incoming customer inquiry text, determine its business category based on semantics such as logistics inquiry, return or exchange application, product consultation, or complaint suggestion, and output a standardized classification label along with brief handling suggestions, ensuring the classification results are accurate and comply with platform standards.

基于自然语言处理技术，自动识别并分类电商客服收到的用户咨询工单，提升响应效率。

Please analyze the following user inquiry text received by e-commerce customer service, determine its business category based on semantics, such as logistics inquiry, return and exchange application, product consultation, or complaint suggestion, and output the corresponding category label and confidence score for subsequent handling by corresponding specialists.

利用自然语言处理技术，对电商平台用户提交的售后咨询与投诉工单进行语义分析，自动识别问题类型并分配至对应处理部门，提升客服响应效率与用户满意度。

Analyze the following e-commerce support ticket text to identify its core issue type (e.g., logistics delay, damaged goods, refund request, usage inquiry), determine urgency, and recommend the most suitable handling department or solution. Output only the JSON formatted classification result, including ticket ID, issue category, urgency level (high/medium/low), and suggested action, without additional explanation.

利用自然语言处理技术，对电商客户咨询与投诉内容进行语义分析，自动识别问题类型并分配至对应业务部门，提升响应效率与解决率。

Act as an intelligent customer service assistant for an e-commerce platform. You will receive raw text from customer inquiries or complaints. Analyze the semantic intent to determine the core issue. If the text mentions delivery delays, categorize it as a logistics issue. If it involves product quality or description mismatches, categorize it as an after-sales dispute. If it concerns account login or payment anomalies, categorize it as technical support. Output a standardized ticket category label along with brief handling suggestions based on key entities and sentiment, ensuring accuracy and compliance with business protocols.

为电商客服提供基于产品知识库的自动回复建议，提升响应速度与满意度。

You are a senior e-commerce customer service expert. Based on the provided customer inquiry and information from the product knowledge base, generate a professional, friendly, and accurate reply. The reply should directly address the customer's issue, avoid templated language, maintain a natural and fluent conversational style, and appropriately express care.