Advanced RAG (Chunking, Reranking, Hybrid Search)
Basic RAG retrieves the top-k most similar chunks. That works for simple questions. It falls apart for multi-hop reasoning, ambiguous queries, and large corpora. Advanced RAG is the difference between a demo that works on 10 documents and a system that works on 10 million. Related: Phase 5 · 23 (Chunking Strategies for RAG) covers all six chunking algorithms — recursive, semantic, sentence, parent-document, late chunking, contextual retrieval — with Vectara/Anthropic benchmarks. This lesson builds on top: hybrid search, reranking, query transformation. Implement advanced chunking strategies (semantic, recursive, parent-child) that preserve document structure and context. Build a hybrid search pipeline combining BM25 keyword matching with semantic vector search and a cross-encoder reranker. Apply query transformation techniques (HyDE, multi-query, step-back) to improve retrieval on ambiguous or complex questions. Diagnose and fix common RAG failures: wrong chunk retrieved, answer not in context, multi-hop reasoning breakdown. You built a basic RAG pipeline in Lesson 06. It works for straightforward questions on a small corpus. Now try these: Ambiguous query: "What was revenue last quarter?" Semantic search returns chunks about revenue strategy, revenue projections, and the CFO's thoughts on revenue growth. All semantically similar to the word "revenue." None containing the actual number. The correct chunk says "$47.2M in Q3 2025" but uses the word "earnings" instead of "revenue." The embedding model thinks "revenue…
Advanced RAG (Chunking, Reranking, Hybrid Search): Basic RAG retrieves the top-k most similar chunks. That works for simple questions. It falls apart for…
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