Training vs Using

Training is the learning job: the machine studies lots of examples and builds its model. It is slow and usually happens just once, done by the makers before you ever get it. Using is the answering job: the finished model takes one new thing and gives a quick answer. You use the same trained model over and over, many times.

Usually not. When you ask an app a question, you are using its finished model to get an answer — you are not training it. The learning happened earlier, during training. Your one question does not change the model, so using it again and again does not by itself make it smarter.

Before training, the model has not learned any patterns yet, so it has nothing to answer with. Training builds the model from examples; using applies that finished model. You always train first, then use — you cannot use a model that was never trained.

Training is the slow part. It needs lots of examples and lots of computer power, and it happens once to build the model. Using is the quick part — the model already exists, so it just gives an answer to one new thing, as many times as you like.

Usually the makers (a company or a team) train the model once, using huge piles of data. Then everyone else uses the finished model — you give it one new thing and it answers. Training is done by the makers; using is done by you.

It is a Stage 5 lesson for roughly ages 10–11 in KAVI Academy's AI-literacy track. It builds on knowing what a model is and the data-train-predict steps, and uses a story, a compare, sorting and reasoning games with light reading.

Yes — this overview and the printable PDF handout are free. The full interactive lesson and the endless practice are part of KAVI Academy.