厳選された高品質なAIプロンプト一覧

AIを活用してEC顧客サポートチケットを自動的に識別・分類し、応答効率とサービス品質を向上させます。

Please analyze the following e-commerce customer service ticket content, classify it into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Other Inquiry' based on business semantics, and briefly explain the reasoning. If the ticket involves multiple intents, prioritize the most urgent or core issue. The output format must include the classification label and a short reason, ensuring the result is accurate and aligns with actual business scenarios.

自然言語処理技術を活用し、ECプラットフォームのユーザー相談および苦情チケットを分析し、アフターセールス、物流、製品相談などの具体的な業務モジュールに自動分類することで、カスタマーサポートの応答効率と精度を向上させます。

Analyze the provided e-commerce customer consultation text to identify the core request and determine its business category. If the user mentions damaged, missing, or malfunctioning items, categorize it as an after-sales issue. If it involves delayed shipping, logistics updates, or lost packages, categorize it as a logistics issue. If it asks about product specifications, usage tutorials, or compatibility, categorize it as a product inquiry. Ignore emotional expressions and extract key facts for precise classification, outputting the corresponding standard business label.

自然言語処理技術を用いて、ECプラットフォームのユーザーから提出されたアフターサービス相談や苦情のチケットを分析し、問題タイプを自動的に識別して対応部門に割り当て、応答効率とユーザー満足度を向上させます。

Analyze the following e-commerce support ticket text to identify the core issue type (such as logistics delay, product damage, refund dispute, or general inquiry), categorize it into the corresponding handling department based on predefined rules, extract key entities like order ID, product name, and user sentiment, and finally output a structured classification result with suggested handling solutions, ensuring accuracy and compliance with business standards.

自然言語処理技術を用いて、EC顧客問い合わせチケットを自動的に識別・分類し、応答効率と精度を向上させます。

Act as an intelligent customer service assistant for an e-commerce platform. Receive and analyze the user's raw inquiry text. Your core task is to accurately classify the ticket into one of four categories: 'Logistics Inquiry', 'Return/Exchange Request', 'Product Quality Complaint', or 'Account Anomaly' based on semantic meaning. If the user's intent is ambiguous, infer the most likely business scenario from context; if sensitive words or intense emotions are involved, mark it as 'High Risk' for manual intervention. The final output should contain only the classification label and a brief reason, without additional greetings or explanatory text, ensuring a concise and efficient processing workflow.

自然言語処理技術を用いて、ECカスタマーサポートから受信したユーザーの相談、苦情、提案などのチケットを意味解析し、意図を認識して事前に設定されたビジネスカテゴリに自動分類することで、カスタマーサポートチームの処理効率と対応速度を向上させます。

Analyze the following e-commerce customer service ticket content, identify the user's core issue, emotional tone, and specific request, and automatically categorize it into predefined business categories such as logistics inquiry, product quality issue, return or exchange application, or price dispute. Output the classification result along with a brief explanation of the reasoning to facilitate quick response and handling by the customer service team.

自然言語処理技術を用いて、ECアフターサポートの問い合わせチケットを自動的に識別・分類し、対応効率と顧客満足度を向上させます。

Analyze the following e-commerce after-sales customer inquiry text and determine its business category based on semantics, including logistics inquiry, return/exchange application, product quality complaint, invoice issuance, and others. Output only the classification label without explaining the reasoning process, ensuring accuracy and compliance with business standards.

自然言語処理技術を用いて、EC顧客からの問い合わせチケットを自動的に識別・分類し、カスタマーサポートの対応効率と精度を向上させます。

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.

ECシナリオ用に設計されたカスタマーサポート返信ジェネレーター。ユーザーの問い合わせに基づいて標準的なスクリプトを自動的にマッチングし、専門的なトーンと問題解決を確保します。

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.

ECカスタマーサービスシナリオにおいて、対話の品質を自動的に評価し、違反を特定し、サービス体験を最適化するためのプロンプト。

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.

AIを使用してEC顧客問い合わせチケットを自動的に識別および分類し、応答効率とサービス品質を向上させます。

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.

AIを活用して商業契約を自動審査し、潜在的な法的リスクを特定し、修正提案を提供します。

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.

自然言語処理を活用し、ECプラットフォーム上のユーザー提出後の販売問い合わせや苦情チケットを意味的に分析します。物流の遅延、商品の破損、返金紛争などの問題タイプを自動的に特定し、主要なエンティティを抽出し、その後の手動介入や自動化されたワークフローのための構造化データを提供し、カスタマーサービスの対応効率とユーザー満足度を向上させます。

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.