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Build Real AI Projects, Not Prompt Tricks, for Interviews

You don’t prepare for AI engineering interviews by collecting prompt tricks or LeetCode. You prepare by building something real. Because interviews don’t reward: “I know this technique.” They reward: “I built this. Here’s how it works. Here’s what failed. Here’s what I measured. Here’s what I’d improve.” That’s why I’m stubborn about project-based learning. The real skill is not prompting. It’s the full loop: Define the problem. Choose a baseline. Collect and clean data. Build the pipeline. Evaluate properly. Iterate. Document tradeoffs. Deploy something that runs outside your notebook. If you’ve never gone through that entire cycle, interviews feel abstract and stressful. If you have, they feel familiar. That’s exactly why we built our Full Stack AI Engineering course around one core idea: You don’t just learn RAG, prompting, fine-tuning, and agents in isolation. You build a real LLM product end-to-end. You start with data collection. You build a full RAG pipeline. You evaluate it. You improve it. You deploy it. You submit your own final project. You walk away with a working product, not notes. If you’re serious about transitioning into LLM roles or leading GenAI projects properly, I’ll put the link in the comments. What’s one project you built that changed how you think about AI engineering?