One of chatgpt's hidden parameters is what timerange of knowledge it can use to answer. I imagine implementing something similar for 'paging' through the plot could work well. Conversation starts at the beginning of the book and then either explicit syntax or revealing particular information in the conversation 'unlocks' further plot from the bot to draw answers from.
The idea of 'unlocking' information for a chatbot to use in answering feels very compelling for non-fiction as well. Ex. maybe the chatbot requires a demonstration of algebraic knowledge before it can draw from calculus in answering questions. Would feel kind of like a game 'achievement system' which could incentivize people exploring the extent of contained knowledge. And you could generate neat visual maps of the users knowledge.
The date in ChatGPT's prompt is there so the model can know when its training data ends. So if you ask it about something that happens in 2023, it can tell you that its training data cuts off in 2021 and it doesn't have knowledge of current events. Current LLM architectures do not enable functionality like "answer this question using only data from before 2010". It is possible future architectures might enable this, though.
The idea of 'unlocking' information for a chatbot to use in answering feels very compelling for non-fiction as well. Ex. maybe the chatbot requires a demonstration of algebraic knowledge before it can draw from calculus in answering questions. Would feel kind of like a game 'achievement system' which could incentivize people exploring the extent of contained knowledge. And you could generate neat visual maps of the users knowledge.