Design an LLM agent with RAG and tools

Q: Design an LLM agent with RAG and tools

This question evaluates proficiency in designing LLM-based agents, covering retrieval-augmented generation, tool/API orchestration, safety and evaluation considerations, and model behavior concepts such as randomness and temperature.

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Question

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You’re asked to describe how you would build an LLM-based agent that can converse with a user (e.g., an interviewer) and answer questions using an internal knowledge base.

Requirements

The agent should use retrieval-augmented generation (RAG) over a document corpus.
It may call tools/APIs (search, calendar, database lookup) when needed.
Discuss safety/guardrails and evaluation.

Follow-up

Where does the randomness in LLM outputs come from?
What does the temperature parameter do, and how do top-k / top-p relate?

Design an LLM agent with RAG and tools

Quick Overview

Requirements

Follow-up

Solution

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