Everything starts with data. A picture becomes a grid of brightness and color values; a sentence becomes a sequence of tokens that are mapped to numbers. These numbers flow through the model. You can imagine the model as many tiny calculators arranged in layers. Each calculator multiplies its inputs by adjustable weights, adds a small offset, and applies a simple squashing step so that extreme values don’t explode. The model’s final layer produces a prediction—perhaps “cat” with 92% confidence or the next word in a sentence. Immediately after a prediction, we measure how far it is from the correct answer using a loss function, a single number that says “how wrong” the model is. Now comes the key step: using calculus, the computer figures out how each weight contributed to the error and in which direction to nudge it. This automatic feedback process is known as backpropagation; the repeated small nudges are often called gradient descent. If the learning rate—the step size of each nudge—is chosen well, the model steadily improves instead of bouncing around or getting stuck. For language tasks, modern systems also use attention, a mechanism that lets the model focus on the most relevant parts of the input. For example, in the sentence “The bolt is loose—tighten it,” attention helps link “it” back to “bolt.” Training proceeds in rounds on small batches of data, over and over, until the loss on fresh, unseen examples stops improving. At that point, the model is ready to be tried in the real world, where it will face inputs it has never seen before.