From their perspective they don't really know who put the tokens there. They just caculated the probabilities and then the inference engine adds tokens to the context window. Same with user and system prompt, they just appear in the context window and the LLM just gets "user said: 'hello', assistant said: 'how can I help '" and it just calculates the probabilities of the next token. If the context window had stopped in the user role it would have played the user role (calculated the probabilities for the next token of the user).
On one machine I run a LLM locally with ollama and a web interface (forgot the name) that allows me to edit the conversation. The LLM was prompted to behave as a therapist and for some reason also role played it's actions like "(I slowly pick up my pen and make a note of it)".
I changed it to things like "(I slowly pick up a knife and show it to the client)" and then just confront it it like "Whoa why are you threatening me!?", the LLM really tries hard to stay in it's role and then tells things like it did it on purpose to provoke a fear response to then discuss the fears.
Interestingly you can also (of course) ask them to complete for System role prompts. Most models I have tried this with seem to have a bit of an confused idea about the exact style of those and the replies are often a kind of an mixture of the User and Assistant style messages.
Yeah, the algorithm is a nameless, ego-less make-document-longer machine, and you're trying to set up a new document which will be embiggened in a certain direction. The document is just one stream of data with no real differentiation of who-put-it-there, even if the form of the document is a dialogue or a movie-script between characters.
