AI Engineering/Phase 11/Lesson 07

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.

Build 1.5 hr Python

Prerequisites: Phase 11, Lesson 06 (RAG)

#Learning Objectives

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

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.