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Hybrid RAG is the difference between a confident AI and a correct one. If your RAG system sounds smart but keeps answering the wrong thing, the problem isn’t the LLM — it’s retrieval. In this guide, you’ll learn how to build Hybrid RAG using BM25, vector search, fusion, and reranking — the same retrieval pattern used in real production AI systems. We’ll break down Hybrid RAG in simple language, explain why vector-only RAG fails, and show how fusion + rerank dramatically improves answer accuracy. You’ll also get a clean Python blueprint for Hybrid RAG that you can adapt to any dataset. If you’re building RAG applications and care about accuracy, trust, and real-world performance, this article will change how you design retrieval forever.