The AI engineer role — building applications on top of large language models — is one of the fastest-growing in tech. Interviews test how LLMs work at a practical level, retrieval-augmented generation (RAG), prompt engineering, embeddings, and how you evaluate and ship reliable AI features. Here are the AI engineer interview questions that actually get asked. (See also our ML engineer guide.)
LLM fundamentals
- How do LLMs work at a high level (tokens, context window, next-token prediction)?
- What is a context window and why does it matter?
- Temperature and other generation parameters.
- What causes hallucinations and how do you reduce them?
RAG & embeddings
- What is retrieval-augmented generation (RAG) and when do you use it?
- What are embeddings and a vector database?
- Chunking strategies and retrieval quality.
- RAG vs fine-tuning — when to choose each.
Prompting, agents & evaluation
- Prompt engineering — system prompts, few-shot, chain-of-thought.
- Function/tool calling and AI agents.
- How do you evaluate an LLM application reliably?
- Handling latency, cost, and guardrails in production.
How to prepare
AI engineering rounds are design conversations about LLM systems. Practise explaining RAG, evaluation, and trade-offs out loud. Greenroom runs spoken technical interviews that follow up on your reasoning. Pair it with our ML engineer and system design guides.
Frequently asked questions
What questions are asked in an AI engineer interview?
AI engineer interviews cover LLM fundamentals (tokens, context windows, next-token prediction, temperature, hallucinations), retrieval-augmented generation and when to use it, embeddings and vector databases, chunking and retrieval quality, RAG vs fine-tuning, prompt engineering (system prompts, few-shot, chain-of-thought), tool calling and agents, evaluation of LLM applications, and production concerns like latency, cost and guardrails.
What is retrieval-augmented generation (RAG)?
RAG augments an LLM's responses with relevant information retrieved from an external knowledge source at query time. You embed your documents into vectors, store them in a vector database, retrieve the most relevant chunks for a user's question, and include them in the prompt so the model answers grounded in that context. RAG reduces hallucinations and lets the model use up-to-date or proprietary data without retraining.
When should you use RAG vs fine-tuning?
Use RAG when you need the model to access up-to-date, proprietary or frequently changing knowledge and to ground answers in sources you can cite — it's cheaper and easier to update. Use fine-tuning when you need to change the model's style, format, or behavior, or teach it a specialized task that prompting can't reliably achieve. They're complementary: RAG supplies knowledge, fine-tuning shapes behavior.
How should I prepare for an AI engineer interview?
Understand LLM fundamentals, RAG and embeddings, prompt engineering, agents, and especially how to evaluate non-deterministic LLM applications and handle reliability, cost and latency in production. Practise explaining RAG vs fine-tuning trade-offs and evaluation approaches out loud with a voice-based mock interview that follows up, since AI engineering rounds are design conversations.