質問で説明されているタスクは、LLMを活用したAIソリューションを使用して、顧客からの一般的な問い合わせを自動的に処理することで、顧客体験を向上させることです。そのためには、システムが入力データ（顧客からの問い合わせ）を処理し、パーソナライズされた関連性の高い回答を効率的に生成する必要があります。ここでは、質問応答、要約、検索拡張生成（RAG）などのタスクにLLMを活用することを重視するDatabricks Generative AI Engineerの原則に照らし合わせながら、各オプションを段階的に評価してみましょう。
* オプションA：入力：顧客レビュー、出力：レビューをユーザーごとにグループ化し、ユーザーごとの平均評価を集計して、応答する
このオプションは、顧客レビューを分析してユーザーごとの平均評価を算出することに重点を置いています。これは感情分析やユーザープロファイリングには役立つかもしれませんが、一般的な顧客からの問い合わせへの対応や、パーソナライズされたやり取りにおける応答時間の改善という目的には直接対応していません。顧客レビューは通常、フィードバックデータであり、即時の対応を必要とするリアルタイムの問い合わせではありません。
* Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.
* Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond
* This option uses chat logs as input, which aligns with customer service scenarios. However, the output-grouping by users and summarizing interactions-focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.
* Databricks Reference: Per Databricks' "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.
* Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary
* This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks' LLM workflows.
* Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states:"For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses."This matches Option C's approach of finding answers and summarizing them.
* Option D: Input: Customer reviews; Output: Classify review sentiment
* This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context.
It's more suited for feedback analysis than real-time customer service.
* Databricks Reference: Databricks' "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it's not highlighted for real-time conversational applications like customer support.
Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks' recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.