LLMs are a stochastic tool that generalizes patterns imprinted into the neural network during training. And then these patterns are expressed in the given context during inference. The stochastic aspect is what makes it possible for the LLM to adopt generalized training to the specific problem being solved. So, the outputs LLMs produced are not random in a sense of playing bingo because randomness is not the dominant property of the system. It is predicting the most likely next token within the given context.