Prepending the filename to each text chunk (e.g., filename + "|" + section) in the Python code (A) preserves contextual metadata, linking each chunk—and its resulting vector—to its source file. This aids retrieval in RAG applications by allowing the application to trace back to the original document, enhancing response context (e.g., “from Book1”). While it differentiates chunks (B), its impact goes beyond identification, affecting retrieval usability. It doesn’t speed up vectorization (C); embedding models process text regardless of prefixes. It also doesn’t train the LLM (D); it’s metadata for retrieval, not training data. Oracle’s RAG examples emphasize metadata preservation for context-aware responses.