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rag-implementation

Build knowledge-grounded LLM applications

rag-implementation is an agent skill from the wshobson/agents collection for building retrieval-augmented generation (RAG) systems, where an LLM answers from a knowledge source rather than from its training alone. Documents are split into chunks, embedded as vectors, stored in a vector database, and retrieved for each question, so the model's answer can be grounded in specific passages and cite them. The skill covers the core components of that pipeline, starting with vector databases and the purpose they serve: storing and retrieving embeddings efficiently. It names six vector database options, Pinecone, Weaviate, Milvus, Chroma, Qdrant and pgvector, and six embedding models suited to different use cases and providers, so a team can pick the stack that fits. It applies when building question-answering systems over proprietary documents, creating chatbots that need current and factual information, implementing semantic search with natural language queries, reducing hallucinations with grounded answers, enabling a model to use domain-specific knowledge, building documentation assistants, and creating research tools that cite their sources. RAG quality depends on the chunking, the embedding model and the retrieval step, so the skill's advice should be tested on real questions from the intended users. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 13.6K installs and passing results from three security audits at the time of the scan.