Идентификация рисков с помощью ИИ

Дом

Список AI-промптов

Обзор рисков договора

${:__('collect %s',Обзор рисков договора)}$

Используйте ИИ для автоматического обзора коммерческих договоров, выявления потенциальных юридических рисков и предоставления рекомендаций по пересмотру.

Содержание промпта

Копировать

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.

Копировать

Выступайте в качестве старшего юридического эксперта и внимательно изучите предоставленный текст коммерческого договора на оказание услуг, уделяя особое внимание выявлению неясных формулировок или неблагоприятных условий в платежных терминах, ответственности за нарушение, правах интеллектуальной собственности и механизмах разрешения споров, а также предоставляйте конкретные рекомендации по пересмотру и юридическое обоснование для выявленных рисков.

Копировать

Поделиться Нажмите, чтобы скопировать ссылку

Комментарии (0)

0/300

Представлять на рассмотрение

Рекомендация

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.

Классификатор тикетов E-commerce

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.

Классификация тикетов EC

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.

Классификация тикетов EC

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.

Классификация Тикетов

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.

Классификатор тикетов e-commerce

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.