When you do that you are just putting the whole book into context for openAI to reason about. That works if the work of smaller than the context.

For longer documents or for groups of documents you need a kind of search to extract the most relevant passages to throw in the context.

That is RAG. That search you do.

It's usually a semantic search using embedded data created by a specialized model to create these embeds and a specific algorithm to chunk the document into smaller pieces to derive meaning from.

So you have multiple pieces involved into the job. A chunker, an embbeder model, a vector database, etc.