RAG Implementation

About

This skill provides a comprehensive framework for building production-ready RAG systems, enabling Claude to architect applications that access and reason over proprietary or domain-specific datasets. It guides users through the entire RAG lifecycle, including document ingestion, sophisticated chunking strategies, embedding generation, and advanced retrieval patterns such as hybrid search and semantic reranking. By leveraging these patterns, developers can significantly reduce hallucinations and build high-accuracy Q&A systems, documentation assistants, and research tools that provide verifiable source citations.

Key Features

• Advanced document chunking and recursive splitting strategies
• Comprehensive evaluation metrics for groundedness and retrieval quality
• Hybrid search patterns combining semantic and keyword retrieval
• Multi-vector store integration including Pinecone, Chroma, and Weaviate
• 81 GitHub stars
• Contextual compression and reranking for high-precision results

Use Cases

• Reducing AI hallucinations by grounding responses in verified knowledge bases
• Building enterprise-grade Q&A systems over private document repositories
• Creating documentation assistants with automated source citations