Certified Generative AI Engineer AssociateDemo

Question

A Generative AI Engineer is building an agent-based LLM system for their favorite monster truck team. The system can answer text-based questions about the monster truck team, look up event dates through an API call, or query tables containing the team’s latest standings.

How could the Generative AI Engineer best incorporate these capabilities into their system?

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.

Explanation

An agent system can expose the event-date API and standings-table query as available tools, with system instructions that guide their use. The LLM can choose whether to answer directly or invoke the appropriate tool, and can use tool results in subsequent calls to produce a response. Function calling is designed to connect an LLM to external APIs, databases, and other current information sources.

Learn more

Introduction to function calling | Generative AI on Vertex AI

Certified Generative AI Engineer AssociateDemo

AstroTutor

Notes

Readiness

QuestionQ85

Community Discussion