RAG Infrastructure Production infrastructure for Retrieval-Augmented Generation: ingest documents, generate embeddings, store in vector databases, and serve grounded LLM responses. When to Use This Skill Use this skill when: - 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) Prerequisites - Python 3.10+ with - A vector database (Qdrant, Weaviate, Pinecone,…