Инструмент автогенерации точных тегов для товаров в екоме

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Автогенерация тегов для товаров

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Автоматически создает точные теги товаров, соответствующие правилам поиска платформы, охватывает основные преимущества и атрибуты, помогает повысить охват и конверсию товаров.

Содержание промпта

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Work through the basic product description, specification parameters and main image text uploaded by e-commerce sellers, sort out the core category, function selling points, applicable scenarios and key attributes of the product, generate accurate product tags that fit platform search habits, keep the number of tags between 8 and 12, use words that match common consumer search terms and avoid repetition and redundancy.

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На основе базового описания товара, спецификаций и текста с главного изображения, загруженных продавцом, выделите основную категорию, функциональные преимущества, сценарии использования и ключевые атрибуты товара, создайте точные теги, соответствующие поисковым привычкам пользователей платформы, оставьте количество тегов от 8 до 12, используйте слова, которые потребители обычно вводят в поиске, избегайте повторений и избыточности.

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Рекомендация

Классификация Тикетов

Analyze the following e-commerce customer inquiry ticket content, automatically classify it into predefined business categories such as logistics inquiry, return or exchange application, product consultation, or complaint suggestion, extract key entity information including order number, product name, and user sentiment, and finally output a structured classification result for the customer service team to handle quickly.

Ответы службы поддержки EC

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.

QA Клиентская Служба

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.

Классификация тикетов поддержки EC

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.

Обзор рисков договора

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.

Классификация тикетов поддержки

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.