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ai-rag-pipeline-architect

Skill3 starsChanged 3 months agoNo licenceView on GitHub

Activate when architecting, implementing, or optimizing Retrieval-Augmented Generation (RAG) systems, semantic vector search, document chunking, hybrid search, and reranking pipelines — trigger phrasings include "build a RAG pipeline for my documents", "how do I chunk PDFs for vector search", "setup pgvector in PostgreSQL", "implement hybrid search with BM25", "reduce hallucinations in my AI assistant", or "audit my RAG retrieval accuracy". Enforces recursive chunking strategies, vector embeddings (text-embedding-3-small, BGE-M3), metadata filtering, and cross-encoder reranking.

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