Why Your RAG System Retrieves Garbage - How to Fix It - Prod RAG: Hybrid Search, Reranking & Eval

Description

Your RAG system retrieved 5 chunks. 3 were irrelevant. The LLM got confused and gave a half-wrong answer. Sound familiar? Basic vector search isn't enough. In this video, I cover the advanced techniques that take your RAG pipeline from demo to production — hybrid search, reranking, query transformation, metadata filtering, and proper evaluation. ⏱️ TIMESTAMPS: 0:00 — The retrieval quality problem 1:45 — Why basic vector search fails 3:30 — Hybrid Search (semantic + keyword with RRF) 7:00 — Reranking (cross-encoders, ColBERT, Cohere) 11:00 — Query Transformation (rewriting, HyDE, multi-query, RAG-Fusion) 14:00 — Metadata Filtering & Context Management 16:00 — Evaluation: The 4 metrics you MUST measure 20:30 — Full production pipeline walkthrough 22:30 — Key takeaways 🔧 TECHNIQUES COVERED: — Hybrid Search (BM25 + Vector + Reciprocal Rank Fusion) — Reranking (Cross-encoders, ColBERT v2, Cohere Rerank, BGE Reranker) — Query Rewriting — HyDE (Hypothetical Document Embeddings) — Multi-Query Decomposition & RAG-Fusion — Metadata Filtering & Autocut — CRAG (Corrective RAG) — RAGAS Evaluation (Context Precision, Recall, Faithfulness, Answer Relevancy) 🛠️ TOOLS MENTIONED: — RAGAS, DeepEval, LangSmith, RAGBench — Cohere Rerank, Jina Reranker, BGE Reranker v2 — Pinecone, Weaviate, Elasticsearch (hybrid search) — LangChain, LlamaIndex 📚 THIS IS PART OF MY RAG SERIES: RAG Explained → https://www.youtube.com/watch?v=ntvmzI5iIuw Chunking Strategies → https://www.youtube.com/watch?v=7NRodhkQppM Observability Deep Dive → https://www.youtube.com/watch?v=JXXSyfTEAqk&t=1s Advanced RAG (this video) 👍 Like & Subscribe for more AI deep dives! #advancedrag #rag #reranking #hybridsearch #ragfusion #hyde #ragas #crossencoder #colbert #llm #ai #devops #langchain #vectordatabase #evaluation