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The rag-implementation skill provides a comprehensive framework for building LLM applications that can access and reason over proprietary or domain-specific data. It guides developers through the entire RAG pipeline, including sophisticated document chunking, embedding generation, and integration with leading vector databases like Pinecone and Chroma. By utilizing advanced retrieval strategies such as hybrid search, reranking, and contextual compression, this skill helps developers eliminate hallucinations and build high-precision Q&A systems, research tools, and documentation assistants that cite their sources.