In this Part 13 of my AI Masterclass series, I break down AI Safety in very simple terms. AI safety is basically making sure our AI model doesn’t generate harmful content, avoids bias, and respects privacy. This matters because your model can interact with anyone—young users, older users, or someone trying to push it into revealing passwords, privacy keys, or producing damaging outputs. The goal is straightforward: the AI should behave responsibly in all situations. I like to explain it with an analogy: think of your AI as an intern. A good intern doesn’t spread rumors, doesn’t reveal secrets, and doesn’t suggest unsafe actions. AI safety is that intern’s moral compass—how governed and self-governed the system is. In the video, I cover the main ways we enforce safety: system prompts (core rules), guardrails (what to do and what not to do), and filtering/monitoring (detecting and blocking harmful prompts and outputs). When safety is working, the model won’t engage with harmful requests—it will stop the interaction and may suggest reaching out for help instead. That’s how we keep AI outputs safe, reliable, and ethical.