In this episode of my AI Masterclass series, I break down one of the most important beginner concepts: training vs inference. I keep it simple with the mental model I use throughout the series—think of a “digital intern.” Training is the learning phase: the intern reads massive amounts of data, studies examples, learns patterns, and improves by seeing millions of question-and-answer pairs. Inference is the answering phase: you ask a question and the intern responds based on what it already learned. The key takeaway I want you to remember is this: when you chat with AI, it is not learning from you live. That’s inference, not training. From a developer perspective, training is like building the model, while inference is like calling an API to get an output back. Training is expensive and rare, inference is fast and frequent. If you lock this in—training = filling the brain, inference = using the brain—then everything from ChatGPT to AI agents will start making a lot more sense.