Top Questions for AI Interview Candidates

Been interviewing for GenAI roles lately and wow - the questions have evolved FAST. Sharing what's actually being asked right now (not the basic "what is machine learning" stuff from 2020). What they're really asking: The Foundation Questions: 1. Explain transformer architecture. 2. Why did we move from RNNs to transformers? What problem did attention solve? 3. What's the difference between encoder-only, decoder-only, and encoder-decoder models? 4. Walk me through what happens during pre-training vs fine-tuning The Practical Stuff: 1. How would you reduce hallucinations in a customer service chatbot? 2. Your RAG system is returning irrelevant documents. Debug this for me. 3. We want to fine-tune Llama for our legal documents. What's your approach? 4. How do you evaluate if your generated content is actually good? The Curveballs: 1. Explain prompt engineering strategies that actually work 2. What's the difference between few-shot and zero-shot learning? 3. How would you implement function calling in a production system? 4. Tell me about RLHF and why it matters for alignment Business Reality Check: 1. Our GenAI POC costs $YY K/month in API calls. How do you optimize this? 2. How do you handle PII in training data? 3. What's your strategy for keeping models updated with fresh information? Here's the thing about GenAI knowledge: It's not just about knowing the tech. Companies need people who get the whole picture - the capabilities, limitations, costs, and risks. When you understand transformers deeply, you can: Spot when someone's overselling what AI can do Design systems that actually work at scale Debug weird model behaviors Make smart build vs buy decisions Have real conversations about AI ethics and safety What actually helped me: Build something real - Even a simple RAG chatbot teaches you more than any tutorial Read the papers - Attention Is All You Need isn't just a meme, it's required reading Play with different models - GPT, Claude, Llama, Gemini all have different strengths Understand the economics - Token costs, inference speed, model size trade-offs The interviews I've done well in weren't about memorizing definitions. They were conversations about solving real problems with GenAI. Reality check: If you're still studying ML algorithms from 2018, you're preparing for the wrong interviews. The field moved. GenAI is eating everything and companies need people who actually understand this stuff. Not trying to gatekeep - just sharing what I'm seeing out there. What's the weirdest GenAI question you've gotten in an interview? #GenerativeAI #LLM #Transformers #AIJobs #TechInterview #MachineLearning #InterviewTips Follow Sneha Vijaykumar for more...

Burning AI Questions by Role (updated for 2025) – AI Researchers: How do we truly interpret LLMs' genuine internal logic to reliably predict or modify their complex behaviors? How do we rigorously test for and prevent harmful emergent behaviors before deployment? How can we access more GPU capacity given global shortages and escalating training costs? – AI Product Managers and ML Engineers: Which foundation models should we standardize on and how long will this last before we need to upgrade? What’s the optimal balance between RAG, orchestration, fine-tuning, and letting models figure things out to accelerate the path to production? How do we continuously monitor and evaluate? – CEOs: What are our moats and opportunities in an AI-native economy? How do we fund bold AI bets while also delivering immediate wins and quarterly financial results? Who in my C-suite owns AI strategy and execution end-to-end, and what should I be holding my other leaders accountable for when it comes to AI transformation? – CIOs / CTOs: How do we securely integrate LLMs with internal systems without leaking sensitive data? How do we access more GPUs and which AI workloads should we run on-premise vs on our private cloud, public cloud, or on the edge? When should we use AI features from our existing software providers vs building our own directly on top of model providers? – Functional Leaders (Marketing, Design, Sales, Support, Finance, Legal, HR, Operations): What’s my AI transformation roadmap for the next 6 months? How do we separate hype from practical use cases in my function? How do we lead teams through AI-driven role changes without compromising morale or key talent? – Employees: Which of my recurring tasks or aspects of important projects and analysis can be automated without risking accuracy or compliance? How do I build AI skills that are transferable across roles and industries? How do I check AI-generated work so I remain the final authority on quality? What is my new job description in the AI era? – Governments / Regulators: How do we set guardrails for AI that prevent harm without stifling innovation or global competitiveness? How do we monitor and respond to deepfakes, misinformation, and emerging threats in real time? How do we ensure equitable access across society to the benefits AI will bring?

Don't listen to the haters: Having now delivered a piece of production software written with agentic AI tooling (Claude Code) I can say that the tech is transformative. It has massively accelerated my work, sometimes up to 10x. This also means that the role of a software engineer (at least at an early stage startup) has profoundly changed, to the extent that I wouldn't consider hiring somebody who is not deeply engaged with AI tooling. That means that I need to understand that about them in the interview. How would I make sure I was hiring somebody who "gets it"? Here are types of questions I would ask: -What are your favourite MCP servers, and why? -Describe your AI-driven software dev workflow. What have the challenges been? How have you addressed them? -What's been the AI dev moment that blew your mind? -Where are the areas you have found agentic AI tools to be weakest, and how have you mitigated those weaknesses? -What's your approach to testing AI-generated code? Good answers will show a realistic understanding of the power and perils of agentic coding, and an application of good software engineering practices to this new way of doing things.

💼 Preparing for your next LLM Engineer interview? Don’t just memorize theory — practice what companies are actually asking in 2025 interviews! From fine-tuning and RAG to prompt injection and LoRA — these 20 real-world questions will help you build confidence and sharpen your AI skills. Whether you're just starting out or already building GenAI systems, this list covers the must-knows for any LLM engineering role. 📌 Save it. Share it. Prep like a pro. — 🔍 Topics covered: Transformer architecture Prompt engineering RAG & reranking LoRA & PEFT AI safety & deployment — ✅ Bonus: These questions also help you build better side projects, portfolios, and open-source contributions. — #LLMEngineering #MachineLearning #AIJobs #GenAI #InterviewPrep #PromptEngineering #RAG #FineTuning #TechCareers #AISafety

Would you pass the AI fluency test in your next CS leadership interview?😳 Over the last few months, I’ve spoken with dozens of CCOs, VPs, and recruiters. One pattern is clear. If you’re interviewing for a post-sale leadership role today, you’re expected to answer questions like: → How are you using AI to improve team productivity? → Where has AI actually changed a workflow, not just added a tool? → What have you learned from experimenting with AI in your organization? You don’t need to be a futurist. You don’t need to build agentic AI workflows. But you do need to be AI fluent enough to lead. That means being able to make practical decisions about where AI adds value, and pointing to real examples that show it’s working. So here’s a simple test for CS and other post-sale leaders: 👉 Can you name two concrete ways your team works better today because of AI? If the answer is no, that’s not a failure. It’s your starting point. Start small. Pick one workflow. Test it. Learn. Over the past several months, I’ve been collecting real-world input from post-sale leaders who are navigating this shift right now. Clear patterns are starting to emerge. I’ll be sharing more soon. I’m curious, what’s one workflow where you’ve already seen AI make a difference, even a small one?