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The RAG Systems Architect skill bridges the gap between raw document repositories and LLM understanding by optimizing the entire retrieval pipeline. It provides expert guidance on complex tasks such as semantic chunking, embedding model selection, and hierarchical retrieval to ensure AI applications receive high-context, relevant data. By focusing on the 'garbage in, garbage out' principle, this skill helps developers eliminate hallucinations and improve generation quality through hybrid search patterns, reranking strategies, and metadata pre-filtering.