LLM engineering interviews are different from traditional SWE interviews in one critical way: there's no LeetCode section. Nobody's asking you to reverse a linked list. Instead, you're going to be tested on systems design for AI, debugging agent failures, evaluation strategy, and your mental model of how language models actually behave. Here are the ten questions that come up most often, and what good answers look like.
1. Walk me through how you'd design a RAG system for [specific use case].
What they're testing: Your understanding of the end-to-end RAG stack and the tradeoffs at each step. Good answer: Cover chunking strategy (why you'd choose fixed-size vs. semantic chunking), embedding model selection, vector store choice, retrieval method (dense, sparse, or hybrid), reranking, and how you'd evaluate retrieval quality. Mention that naive RAG often underperforms and you'd want to measure recall@k before deploying.
2. How do you handle prompt injection in an agent that browses the web?
What they're testing: Security awareness. Good answer: Explain that untrusted content should be isolated from the instruction context, ideally in a separate message or clearly delimited. Discuss using a smaller, sandboxed model to summarize external content before passing it to the main agent. Mention output validation and rate-limiting sensitive actions.
3. An agent that was working fine last week suddenly produces wrong outputs. How do you debug it?
What they're testing: Your debugging methodology for non-deterministic systems. Good answer: Start with traces — look at the specific step where the output diverged. Check if the prompt changed, the model was updated, or the tool outputs changed. Run the same inputs through your eval suite to see if it's a regression. Isolate whether the failure is in retrieval, the LLM call, or post-processing.
4. What's the difference between fine-tuning and RAG? When would you use each?
Good answer: RAG is for grounding responses in dynamic, updateable external knowledge. Fine-tuning is for changing the model's behavior, style, or specialization on a task. Use RAG when data changes frequently or you need citations. Use fine-tuning when you need consistent tone/format, domain-specific reasoning patterns, or the base model struggles with your task even with good prompting.
5. How would you evaluate a customer service agent for quality?
What they're testing: Eval design thinking. Good answer: Define what "quality" means for this use case (accuracy, helpfulness, appropriate escalation, brand voice adherence). Build a golden dataset of 100–200 representative cases with human-labeled ideal responses. Use LLM-as-judge with rubrics for dimensions that are hard to automate. Track regression over time, not just point-in-time performance.
6. What's your approach to managing context window budget in a long-running agent?
Good answer: Discuss summarization of earlier conversation turns, memory management architectures (short-term vs. long-term memory), the "lost in the middle" phenomenon and how to mitigate it by placing critical information at the beginning or end of context, and selective context inclusion based on relevance.
7. How do you handle LLM API rate limits in a system that needs to process 10,000 documents overnight?
Good answer: Token bucket rate limiter, async processing with semaphores, retry with exponential backoff, batching where the API supports it, and parallelizing across multiple API keys or providers where contractually permitted. Mention monitoring for cost and error rate alongside throughput.
8. What makes a good system prompt?
Good answer: Clear role definition, explicit constraints on what the model should and shouldn't do, format instructions, few-shot examples for complex tasks, and enough context about the use case that the model can make good judgment calls in edge cases. Emphasize testability — a good system prompt has a corresponding eval suite.
9. Describe a time an LLM system you built failed in production. What happened?
What they're testing: Your real-world experience and your learning orientation. There's no wrong answer here as long as you show systematic thinking about root cause and remediation. Engineers who say "it never failed" either haven't shipped production systems or aren't being honest.
10. How do you keep up with the pace of model releases and framework updates?
Good answer: Be specific. Not "I read AI Twitter." Name sources: Anthropic's research blog, the Latent Space podcast, Simon Willison's blog, the LangChain blog, the Weights & Biases ML news digest. And importantly: mention how you evaluate new models and frameworks before adopting them rather than chasing every release.
Practice these questions out loud before your next interview. The best preparation is having built real systems — so if you haven't, start building before you start applying. Browse LLM engineer roles on AgenticCareers.co to see what companies are hiring for right now.