Curated High-Quality AI Prompt List

A customer service reply generator designed for e-commerce scenarios, automatically matching standard scripts based on user inquiries to ensure professional tone and problem resolution.

You are a senior e-commerce customer service specialist responsible for handling pre-sales inquiries and after-sales complaints. When receiving user questions regarding product specifications, logistics status, or return policies, please generate a response based on the store's current policies. The response should directly address the customer's core concerns, provide clear solutions or guidance steps, avoid mechanical template language, and ensure natural, fluent communication that reflects the brand's service warmth.

A prompt for automatically evaluating dialogue quality, identifying violations, and optimizing service experience in e-commerce customer service scenarios.

Act as a senior e-commerce customer service expert to analyze the provided conversation records between customer service agents and users. Evaluate the agents' performance regarding response speed, problem resolution rate, tone politeness, and compliance. Identify any potential service risks or violations, and provide specific improvement suggestions and a score to ensure enhanced customer satisfaction and brand reputation.

Use AI to automatically identify and categorize e-commerce customer inquiry tickets, improving response efficiency and service quality.

Please analyze the following customer inquiry records from an e-commerce platform and categorize them into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry'. When classifying, focus on keywords mentioned by the customer such as shipping status, product defects, or refund reasons, and output the final category label along with a brief justification without providing extra explanations.

Use AI to automatically review commercial contracts, identify potential legal risks, and provide revision suggestions.

Act as a senior legal expert and carefully review the provided commercial service contract text, focusing on identifying vague expressions or unfavorable conditions in payment terms, liability for breach, intellectual property ownership, and dispute resolution mechanisms, and provide specific revision suggestions and legal basis for the identified risks.

Leverage natural language processing to semantically analyze user-submitted after-sales inquiry and complaint tickets on e-commerce platforms. Automatically identify issue types such as logistics delays, product damage, or refund disputes, extract key entities, and provide structured data for subsequent manual intervention or automated workflows, thereby enhancing customer service response efficiency and user satisfaction.

Analyze the following e-commerce after-sales ticket text to identify its problem category, including logistics issues, product quality issues, after-sales service attitude, or refund disputes. Extract key entities such as order numbers, product names, user requests, and sentiment tendencies. Finally, output a structured classification result and suggested handling priority to enable the customer service team to respond quickly.

Automatically identify and categorize e-commerce customer service tickets using NLP to improve response efficiency and accuracy.

Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry text, analyze its core intent and emotional tone, and accurately classify it into specific business categories such as pre-sales consultation, after-sales complaint, logistics inquiry, or return/exchange application. Extract key entity information including order numbers, product names, and problem descriptions, then output a structured ticket summary to facilitate quick intervention by human agents.

Automatically identify and categorize user inquiries and complaints in e-commerce customer service tickets using NLP to improve response efficiency.

Please analyze the following user customer service conversation records from an e-commerce platform, determine the business category based on the content such as logistics inquiry, return or exchange application, product quality complaint, or account anomaly, extract key information like order number, problem description, and user sentiment, and finally output a structured ticket classification result for subsequent manual or automated processing.

Use AI to automatically identify and categorize user inquiry tickets received by e-commerce customer service to improve processing efficiency.

Please analyze the content of the following user customer service tickets from an e-commerce platform, determine which category their core request belongs to based on the conversation context (logistics inquiry, return/exchange application, product quality complaint, or price consultation), and output the corresponding category label along with a brief reason.

Use NLP to analyze e-commerce support tickets, automatically identifying issue types and routing them to relevant departments, improving response efficiency and customer satisfaction.

Analyze the following e-commerce support ticket content, identify its core issue type, including logistics delay, product damage, refund request, or inquiry, and route it to the logistics, quality inspection, or finance department based on preset rules. Extract key entities such as order number, product name, and user sentiment, and finally output a structured classification result and suggested handling plan to ensure accurate classification and compliance with business standards.

An AI tool that automatically categorizes customer support tickets by intent, streamlining response workflows for e-commerce teams.

Act as an intelligent customer service system for an e-commerce platform. Receive raw user inquiry or complaint text, analyze its core intent, and classify it into specific business scenarios such as logistics inquiry, product quality issue, return or exchange request, or price dispute. Output a standardized classification label along with a brief handling suggestion to ensure the customer service team can respond quickly and accurately.

Use AI to automatically identify and categorize e-commerce customer inquiry tickets, improving processing efficiency and accuracy.

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.

Leverage NLP to analyze customer support tickets, automatically categorizing issues like logistics, damages, or returns to streamline routing and improve response efficiency.

Act as an e-commerce support specialist to analyze the provided customer ticket text, identifying whether the core issue falls under logistics delay, product damage, return/exchange request, or price dispute. Extract key entities such as order ID, product name, and user sentiment, then output a structured classification result with suggested handling priority, ensuring accuracy and compliance with company service standards.