Design an LLM agent with RAG and tools
Company: Walmart Labs
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: easy
Interview Round: Onsite
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?
Quick Answer: 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.