We compiled 42 of the Generative & Agentic AI interview questions (and how to actually answer them).

Hey Everyone, The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?" They want to know if you can architect production-grade multi-agent systems, prevent RAG hallucinations, and manage state across LLM calls. I’ve been building a visual learning sandbox for multi-agent workflows (**agentswarms.fyi**), and today I just launched a completely free **AI Interview Prep Module** inside it. I compiled 42 top interview questions specifically for GenAI and Agentic AI roles. But instead of just giving a generic answer, the module breaks down the *"Standout Answer"* and teaches you the mental model of *how* to answer it like a senior architect. Here are two examples from the list: **Question 1: When would you use a Multi-Agent Swarm instead of a single LLM with multiple tools?** * ❌ **The average answer:** "When the task is too complex, multiple agents are better than one." * ✅ **The standout answer:** "You use a swarm to prevent context dilution and enforce the Principle of Least Privilege. If you give one 'God Agent' 15 tools and a 4k-word system prompt, its reliability drops and hallucination risk spikes. By routing to specialized sub-agents with narrow instructions (e.g., separating the 'Data Extraction Agent' from the 'Customer Chat Agent'), you isolate failure points and allow for parallel execution." **Question 2: How do you handle hallucinations in a financial RAG pipeline?** * ❌ **The average answer:** "I would lower the temperature to 0 and give it a better system prompt." * ✅ **The standout answer:** "I would decouple data extraction from text generation. I'd use a deterministic node or a strict JSON-enforced agent to only extract the hard numbers from the retrieved context. Then, I would pass that structured data to a separate Synthesis Agent. Finally, I'd implement an 'LLM-as-a-judge' evaluation loop before returning the final output to the user." **What's in the full list?** The 42 questions cover: * RAG Architecture & Vector Databases * Agentic Routing (ReAct vs. Planner-Executor) * Evaluation metrics for non-deterministic outputs * Security (Prompt injection prevention in multi-agent loops) For those of you who have interviewed for AI Engineering roles recently, what is the hardest system design question you've been asked? I'd love to add it to the list.