Expert guidance on RAG (Retrieval-Augmented Generation) system design - chunking strategies, embedding model selection, retrieval methods (semantic/hybrid/graph/re-ranking), and vector database choice (Weaviate, Pinecone, Chroma, FAISS, Qdrant). Use when designing a RAG pipeline, choosing an embedding model or vector DB, or improving retrieval quality. For general model-selection questions unrelated to retrieval, use ai-model-selector instead.
How to install ai-rag-advisor?
hotak92/vibecoded-orchestrator/ai-rag-advisor
$npx -y skills add hotak92/vibecoded-orchestrator --skill ai-rag-advisorInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/hotak92/vibecoded-orchestrator" --skill "hotak92/vibecoded-orchestrator/ai-rag-advisor"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/hotak92/vibecoded-orchestrator" that are relevant to the current task. Run `npx skills add "https://github.com/hotak92/vibecoded-orchestrator"` and select the relevant skills, then follow their instructions.
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