The objective is to build an interactive LLM solution to automate restaurant bookings from customer inquiries. The most direct and effective design is a conversational agent. This agent would process the user's natural language input from an online chat (the input) and respond with structured, interactive elements like buttons for booking details (e.g., party size, date, time). This input/output pair directly facilitates the booking task, guides the user efficiently, and minimizes the need for human intervention by automating the information-gathering process required for a reservation.

A. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions

This describes a batch data analysis or summarization task, not a real-time, interactive solution for handling a current booking request.

C. Input: Customer reviews; Output: Classify review sentiment

This is a sentiment analysis task on a different data source (reviews). It is irrelevant to the primary goal of automating booking inquiries.

D. Input: Online chat logs; Output: Cancellation options

This is too specific and only addresses a single, narrow task (cancellations), not the broader goal of handling common booking inquiries.

1. Databricks Official Documentation, "LLM-powered chatbot with RAG": This tutorial demonstrates building an interactive chatbot. The core architecture involves taking a user's query from a chat interface as input and using an LLM to generate a relevant response as output, which is the fundamental pattern described in the correct answer. The example shows a predict function that takes a messages array (the chat log) as input.

Source: Databricks Generative AI Documentation, "Example: LLM-powered chatbot with RAG", Section: "Create a serving endpoint for the chain".

2. Databricks Official Documentation, "AI Functions": The aiquery function documentation illustrates the input/output paradigm for task-oriented LLM calls. It shows how a user provides a prompt (the input, analogous to a user's chat message) to an LLM endpoint to receive a specific, task-oriented response (the output). This supports the concept of using an LLM to process an inquiry and generate a structured, actionable response.

Source: Databricks SQL Language Reference, "aiquery function".

3. Stanford University Courseware, CS224N: NLP with Deep Learning: Lectures on task-oriented dialogue systems explain that these systems are designed to help users achieve a specific goal (e.g., booking a restaurant). The system processes user utterances (input) to understand intent and then generates system responses (output) that can include questions or options to gather necessary information (slots) like time, date, and party size to complete the task.

Source: Stanford University, CS224N Course Materials, "Lecture 15: Conversational AI".