A big reason for 'content drift' is that LLM's are like a sliding context window of the input text plus the prompt, and for each new token generated, the next t

A big reason for 'content drift' is that LLM's are like a sliding context window of the input text plus the prompt, and for each new token generated, the next token prediction is using the previously LLM generated tokens as well. Giving a LLM too much context causes the same effect, as the sliding window moves on from the earliest tokens. It's also why summarization is bad. It's not exactly linear though according to the text from start to finish, bit's of context get lost from throughout the input text at random and will be different each time the same input is run. A good way to mitigate this is to break up the text and execute in smaller chunks, even with models boasting large context, results drop off significantly with large inputs so using several smaller prompts is better.