Module Overview
This module moves beyond baseline retrieval into the techniques that make RAG production-reliable. It covers query-side transformations, reranking for precision, the self-correcting and adaptive retrieval families, contextual retrieval, agentic RAG where the model decides when to retrieve, and systematic evaluation.
Learning Objectives
- Apply query transformations to bridge the query-document gap.
- Use rerankers to improve top-k precision.
- Distinguish Self-RAG, Corrective RAG, and Adaptive RAG.
- Explain contextual retrieval and agentic RAG.
- Evaluate RAG pipelines systematically.
Topics Covered
Advanced RAG Systems
- Hybrid RAG and Meta Hybrid RAG
- Query transformations
- RAG evaluations
- Rerankers
- Self-RAG
- Corrective RAG (CRAG)
- Adaptive RAG
- Contextual retrieval
- Agentic RAG (the LLM decides when and how to retrieve)
Key Concepts & Terminology
Multi-query and HyDE-style transformations, cross-encoder reranking, retrieval self-assessment, corrective re-retrieval, adaptive routing, retrieval-as-a-tool.
Tools & Frameworks Referenced
Cross-encoder rerankers, RAG evaluation frameworks, orchestration via LangChain/LangGraph.
Prerequisites
Module 16 (RAG foundations).