The primary goal is to create an AI solution that automatically handles common customer inquiries. Using historical customer service chat logs as the input data source is ideal, as they contain a rich repository of real-world questions and their corresponding correct answers provided by human agents. The LLM's task is to perform a semantic search to find similar, previously answered questions within these logs and then synthesize or summarize the existing answers to respond to the new query. This approach, a core concept in Retrieval-Augmented Generation (RAG), directly addresses the need to provide fast, accurate, and contextually relevant answers to common questions.

A: This describes a user-level analytics task (aggregating ratings), not a system for answering specific customer inquiries in real-time.

B: This focuses on summarizing a single user's entire interaction history, which is useful for personalization but does not directly answer a new, general question.

D: This is a sentiment analysis task. While useful for understanding customer feedback, it does not provide answers to factual or procedural questions.

1. Databricks Documentation, "Build a Q&A chatbot with RAG": This guide details a common LLM use case: building a question-answering chatbot over a proprietary dataset. It states, "The general pattern for RAG is that you retrieve relevant documents from your knowledge base and provide those documents as context to the LLM for it to generate an answer." Customer service chat logs serve as an excellent knowledge base for this pattern. (Found in: Databricks Documentation > AI and Machine Learning > Generative AI and LLMs > RAG)

2. Databricks Blog, "Building a Customer Service Chatbot with Retrieval Augmented Generation (RAG) and Llama 2" (August 23, 2023): This article explicitly outlines the architecture for the scenario in the question. It explains, "RAG enhances LLMs by integrating an information retrieval system that provides ground-truth information from your own knowledge base... This knowledge base can be built from various sources, such as documents, a database, or a knowledge graph (e.g., FAQs, articles, customer support tickets)." This directly supports using chat logs (support tickets) to find and generate answers.

3. Lewis, P., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474: This foundational academic paper introduces the RAG model, which combines a pre-trained retriever with a generator. The retriever finds relevant documents (analogous to finding similar questions in chat logs), and the generator uses this information to produce the final output (the answer). This is the formal methodology described in the correct option. (DOI: Available through major academic search engines like arXiv:2005.11401)