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Interview Preparation — Overview

This section prepares you for technical interviews at companies hiring LLM engineers, AI product engineers, and ML platform engineers.

What to expect

Most LLM engineering technical screens have 4 parts:

  1. Background and projects (10–15 min) — walk through your work, explain design decisions
  2. Technical Q&A (20–30 min) — LLM fundamentals, RAG, agents, deployment
  3. System design (15–25 min) — design an LLM system at scale
  4. Coding (15–20 min) — write a function involving LLM API calls, async, or data processing

Study resources in this section

The Week 2 Day 5 mock interview section has the most practical preparation:

Interview question topics by frequency

Based on what companies actually ask:

High frequency (study these first)

  • How does RAG work? When would you use it vs fine-tuning?
  • Explain the function calling cycle (OpenAI tools API)
  • What is temperature and when do you use temperature=0?
  • How do you handle rate limits in production?
  • Why use AsyncOpenAI instead of the sync client in FastAPI?
  • What is LoRA and why is it memory-efficient?

Medium frequency

  • HyDE, reranking, multi-query retrieval
  • LangGraph nodes, edges, conditional routing
  • RAGAS metrics: faithfulness, answer relevancy
  • Exact-match vs semantic caching tradeoffs
  • Serverless cold starts for LLM apps

System design (one of these will appear)

  • Design a customer support bot backed by documentation
  • Design a batch document processing pipeline (1M docs/day)
  • Design a coding assistant for an internal codebase
  • Design an LLM evaluation pipeline

08-mock-interview-simulator

The mock interview simulator contains an extended set of practice questions with hints and model answers.

30-day study plan

Week Focus
Week 1 Complete the course content; build 2 projects
Week 2 Practice technical Q&A out loud; build 2 more projects
Week 3 System design practice (2 problems/day); polish portfolio
Week 4 Full mock interviews; apply aggressively