What's the difference between training a model and just using ChatGPT?

Using ChatGPT is like borrowing a friend's car. Training a model is like building your own engine. When you use ChatGPT, you're talking to a model someone else already trained on a massive pile of internet text. You give it a prompt, it gives you an answer. You can't change how it thinks. You can only change what you ask. Training your own model means you start with a blank slate — or more commonly, a pre-built skeleton — and feed it your own data so it learns patterns specific to your task. For example, if you run a bakery and want to predict tomorrow's croissant demand based on weather and day of the week, you'd train a small model on your own sales records. You wouldn't ask ChatGPT for that. It doesn't know your shop. The practical tip here: most people don't need to train a model from scratch. That's expensive and requires thousands of examples. What you'll actually do is called fine-tuning. You take an existing model — like an open-source one from Hugging Face — and show it a few hundred examples of your specific thing. It's like teaching a chef who already knows how to cook your grandmother's exact recipe. They get it fast because they already have the skills. Training from scratch is for when you're doing something truly new, like recognizing a rare medical condition in X-rays where no existing model applies. For 99% of projects, start with what's already built and tweak it.