Generates production-grade Retrieval-Augmented Generation (RAG) pipeline boilerplate with industry best practices.
RAG Scaffold is a specialized Claude Code skill designed to accelerate the development of AI applications by generating modular and scalable RAG architectures. It automates the creation of complex data pipelines, including document chunking, embedding generation, vector store integration, and LLM orchestration. By providing a standardized foundation across multiple frameworks like LangChain and LlamaIndex, it allows developers to focus on domain-specific logic while ensuring the underlying retrieval and generation layers are built for high performance, maintainability, and production readiness.
Key Features
01Standardized project structures with environment configuration and Docker support
02Seamless integration with major vector stores like Qdrant, Pinecone, and ChromaDB
03Built-in advanced retrieval patterns such as hybrid search and reranking
041 GitHub stars
05Ready-to-use templates for streaming responses and source citations
06Multi-framework support including LangChain, LlamaIndex, and Vanilla Python/TypeScript
Use Cases
01Implementing specialized AI code assistants for proprietary repositories
02Building enterprise-grade document search and internal knowledge bases
03Prototyping customer support bots with private data retrieval
