Training a neural network follows a simple loop:

• Forward pass — data flows through the network and produces a prediction
• Loss calculation — the output is compared to the correct answer and the error is measured
• Backpropagation — the error is traced backward to find which weights caused it
• Weight update — weights are nudged to reduce the error

This loop repeats millions of times. Training GPT-3 meant processing roughly 300 billion tokens over weeks on thousands of GPUs. Gradually the weights settle into values that capture the patterns in the data and produce accurate predictions.