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W-RAC introduces a cost-efficient chunking framework for web document processing in RAG systems that reduces LLM token usage by an order of magnitude through structured content representation and retrieval-aware grouping decisions. The method decouples text extraction from semantic chunk planning, achieving comparable or better retrieval performance than traditional chunking approaches while minimizing hallucination risks.