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This skill provides comprehensive patterns and best practices for implementing Retrieval-Augmented Generation (RAG) systems in AI applications. It covers the entire lifecycle of RAG development, from document chunking and embedding generation to advanced retrieval strategies like hybrid search and reranking. Whether building documentation assistants, proprietary knowledge bases, or research tools, this skill helps developers minimize hallucinations and ensure accurate, context-aware responses by integrating LLMs with scalable vector databases and efficient semantic search architectures.
