Parameters
Parameters are the adjustable internal values that a model learns during training. Each one is a small number that the model tunes as it works through examples, and together they store everything the model has learned. When you read that a system has "billions of parameters", that figure is a rough measure of its size and capacity, in the same spirit that engine size hints at the power of a car.
Inside a neural network are countless connections, and each connection has a strength attached to it. Those strengths are the parameters. At the start of training they are set randomly, so the model’s answers are useless. As it sees more and more examples, it repeatedly nudges these values up or down until its answers improve. By the end, the specific pattern of billions of tuned numbers is what makes the model good at its job. In a very real sense, the parameters are where the model’s knowledge lives.
A loose analogy is the dials on an enormous mixing desk. Each dial on its own does something tiny and unremarkable, but with billions of them set to just the right positions, the overall sound, or in this case the model’s behaviour, becomes rich and capable. Nobody sets these dials by hand; the training process finds good settings automatically.
Parameter counts are quoted so often because they give a quick, if imperfect, sense of scale. More parameters generally mean a model can capture more complex patterns, which is why the headline models have grown from millions to hundreds of billions of parameters. But bigger is not always better: a larger model costs more to run and train, and clever design or good data can let a smaller model outperform a bloated one. Treat parameter counts as a useful hint about size, not a definitive score of quality.