An enterprise data source contains documents and identifiers for an enterprise. A RAG data ingestion platform retrieves a document and document identifier from the enterprise data source and divides the document into a first set of chunks. A first LLM query, designed to predict questions associated with the retrieved document based on the first set, is output to a first LLM. The platform executes a first embedding model on a response to the first LLM query and document metadata including the document identifier and stores a result of the first embedding model in the RAG vector database. The retrieved document is also divided into a second set of chunks (with chunks smaller than the first set and including a second chunk identifier). A second embedding model is executed based on the second set of chunks, and a result of the second embedding model is stored in the RAG vector database.