Other answers here are mostly wrong. They are almost certainly NOT passing in the prompt as English and having the model reread the prompt again each time. What

Other answers here are mostly wrong. They are almost certainly NOT passing in the prompt as English and having the model reread the prompt again each time. What they’ll be doing is passing in the vector representation of the prompt at run time, not “compile time”. The thing is that passing in the vector embedding representation of the prefix prompt leaves the LLM in the same position as if it had read the prompt in English. The embedding IS the prompt. So you can’t tell whether it literally reran the computation to create the vector each time. But it would be much cheaper to not do the same work over and over and over. Passing in the prompt as an embedding of English language is more or less free and is very easy to change on the fly (just type a new prompt and save the vector representation). Fine tuning the model to act as if that prompt was always a given is possible but expensive and slow and not really necessary. You don’t want to retrain a model to not use seaborn if you can just say “don’t use seaborn”