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This is a easy difficulty Machine Learning question, commonly asked during Onsite rounds at Walmart Labs.

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This question is commonly asked for Machine Learning Engineer candidates at Walmart Labs during technical interviews.

Design an LLM agent with RAG and tools

Last updated: Mar 29, 2026

Quick Overview

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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Design an LLM agent with RAG and tools

Walmart Labs

Dec 7, 2025, 12:00 AM

easyMachine Learning EngineerOnsiteMachine Learning

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?

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