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During retrieval-augmented generation (RAG) quality testing, an Agentforce Specialist notices that tabular information from a custom Data 360 Document Ingestion Pipeline is losing its context because the data is scattered across multiple separate chunks. What is the most appropriate approach to resolve this?
Correct Answer: A
The correct answer is A. The problem is not that the retriever lacks enough sources; the problem is that the document parser is breaking structured tabular content into chunks that lose layout context. Salesforce Data 360 includes parsing and preprocessing options for unstructured content, and the Docling parser is specifically intended for stronger layout understanding, including table-heavy or complex document structures. Option B is wrong because an ensemble retriever searches multiple retrievers or sources; it does not repair poor chunking caused by weak document parsing. Option C is also wrong because keyword-only scoring changes retrieval ranking behavior but does not preserve table relationships during ingestion. The correct fix is upstream: improve parsing before vectorization and indexing so the chunks preserve table context for RAG grounding.