Part 4: Advanced RAG
This part takes you past the basics of Retrieval-Augmented Generation and into the techniques that make RAG robust enough for real-world use. You’ll go beyond simple chunking and keyword retrieval to tackle the deeper issues that cause weak or inconsistent answers—vague queries, poor indexing, and shallow retrieval logic. Instead, you’ll learn how to design smarter indexing pipelines, rewrite and route questions more effectively, and refine results with post-retrieval reranking. The goal is to build retrieval systems that consistently surface the right evidence, maintain meaningful context, and empower the model to generate clear, reliable answers.
You’ll explore advanced embedding and indexing strategies that account for structure and hierarchy in your data—whether it’s text, HTML, Markdown, tables, or even multimodal sources. You’ll experiment with methods like multi-vector retrieval and context expansion to make sure that the model doesn’t lose coherence across chunks. On the query side, you’ll refine your system’s understanding with rewriting, step-back reasoning, hypothetical document generation (HyDE), and question decomposition. You’ll also learn how to route questions intelligently between different data stores—vector databases, SQL systems, document stores, or knowledge graphs—and how to merge their outputs using techniques like Reciprocal Rank Fusion (RRF).