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

Описание: 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 →    • RAG in 10 Minutes: Retrieval-Augmented Gen...  
Chunking Strategies →    • 10 Chunking Strategies for RAG - Every Met...  
Observability Deep Dive →    • OpenTelemetry is Changing Everything — Obs...  
Advanced RAG (this video)

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