Databricks Certified Generative AI Engineer Associate — Question 42

A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team’s latest standings.
How could the Generative AI Engineer best design these capabilities into their system?

Answer options

• A. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
• B. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
• C. Instruct the LLM to respond with “RAG”, “API”, or “TABLE” depending on the query, then use text parsing and conditional statements to resolve the query.
• D. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.

Correct answer: B

Explanation

The correct answer, B, is optimal because it involves creating a detailed system prompt that informs the agent about the tools available, enabling it to make multiple calls to effectively solve various queries. Option A, while involving a vector store, does not fully utilize the agent's capabilities. Option C simplifies the response mechanism without leveraging the full potential of the system. Option D restricts flexibility by embedding all information in the prompt, which may not be efficient for dynamic queries.