The final step is to generate the next token by sampling from this distribution, so this process simply rank these tokens by their probability of being that next word.

The algorithm split the prompt into tokens, then vectorize them in a matrix with millions of parameters. This combined representation captures both the possible semantic meaning of the tokens and their position in the input sequence.