You are preparing a large internal knowledge base for a RAG application. The documents are Markdown files with clear hierarchical headers (H1 for product areas, H2 for features, H3 for individual procedures). Users typically ask specific procedural questions like 'How do I reset the API key for the billing service?'. You want each retrieved chunk to be self-contained and topically coherent so the LLM has focused context. Which chunking approach best fits these documents?