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rag-infrastructure

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Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

When to use

• Building a knowledge base Q&A system over internal documents
• Implementing semantic search over large document collections
• Reducing LLM hallucinations with retrieved context
• Setting up embedding pipelines and vector store infrastructure
• Deploying hybrid search (dense + sparse/BM25)

Skill page

Source