I am trying to find out how I can teach the content of a whole, multiple hundert pages book to an LLM so that it "knows" all details and can be queried, give summaries etc. The book is one consistent story, private and has never been published. I thought training LLM on a long book would be a common use case, but I found surprisingly little information about this.
Most use cases these days regarding own content seems to be stuff like "chat with your documents" or such. But this seems much easier due to context length and the lack of coherence between documents.
I am not an ML expert but know the basics of embeddings and fine tunings. Is either of these approaches better suited? How could a raw book be turned into a proper training data set for fine tuning a model? This could not be done manually, as the length is almost a million words. Or could it work "simply" by splitting the text into chunks and embedding them?