Question

You are building a customer-facing shopping assistant on Databricks. Customers ask two kinds of questions: (1) explanations of the store's return and warranty policies, which are documented in a static PDF knowledge base, and (2) current stock levels and prices for specific SKUs, which change minute-to-minute and live in a transactional database exposed through a REST API. You want a single Mosaic AI agent that answers both accurately. Which design correctly handles both question types?

Accepted Answer

Use a RAG retriever over the policy documents for policy questions, and register a tool (function) that calls the live inventory REST API for stock and price questions, letting the agent choose based on the query.

Answer

Index both the policy PDFs and a nightly snapshot of the inventory table in Vector Search, and answer every question with pure RAG retrieval.

Answer

Fine-tune a foundation model on both the policy PDFs and current inventory data so it can answer all questions from its weights.

Answer

Store the inventory data as few-shot examples in the system prompt and use RAG only for the policy documents.