The RAG Architecture skill provides comprehensive architectural guidance for building robust, knowledge-grounded AI systems. It equips developers with industry-standard patterns for document ingestion, advanced chunking strategies, embedding model selection, and multi-stage retrieval techniques. Whether you are implementing hybrid search (dense + sparse), optimizing context assembly to avoid the 'lost-in-the-middle' problem, or exploring advanced patterns like HyDE and Self-RAG, this skill ensures your AI applications are accurate, relevant, and scalable.
Key Features
01Standardized RAG pipeline design patterns and ingestion workflows
02Strategic chunking comparisons including semantic and recursive methods
03Embedding model selection and optimization for performance and cost
04Hybrid search implementation using Reciprocal Rank Fusion (RRF)
05Multi-stage retrieval strategies including cross-encoder reranking
065 GitHub stars
