When given a sequence of tokens e.g. "an apple tastes " an LLM performs a mathematical transformation that modifies the input tokens using the stored weights within the model to predict the most statistically likely next token(s) (which may be "sweet and crisp...").
It's important to understand how these things work - and how different they are from a human brain. Despite it being a common point of confusion, the architecture of a transformer is massively different from the neural architecture of a human brain - and the human brain isn't just neurons. Just as a start, if you ever look at some of the attempts to map synaptic networks in brains you will notice they are full of recurrent loops, while a transform is feed-forward only. There's also tons of mediating chemicals, several (known) distinct kinds of synapses, and other kinds of cells as well.
Just one example of the consequences of these differences: LLMs stop "experiencing" anything once a stop token has been generated. Meanwhile, Humans in sensory deprivation tanks may hallucinate vividly - but keep thinking.
This is pretty simple, CS101 stuff.