Advanced RAG Engineering

About this course

Important Prerequisite Notice: This is not a course for beginners. It strictly demands prior experience with TypeScript (backend development) and a foundational understanding of Large Language Models (LLMs).

Most Retrieval-Augmented Generation (RAG) systems fail when they hit production. You have likely experienced the core frustration: the right document exists in your database, it gets retrieved, but it never actually reaches the model's reasoning—or your system simply collapses under the "Lost in the Middle" phenomenon.

This course bridges the gap between basic AI prototypes and enterprise-grade distributed systems. We focus entirely on advanced retrieval and context engineering techniques to build smarter and more contextual AI systems, deliberately avoiding bloated agent orchestration frameworks.

Using a modern backend stack featuring TypeScript, the Anthropic SDK (Claude API), and Voyage API for embeddings, you will deconstruct and optimize every single stage of the RAG pipeline. Throughout the modules, you will:

Master Advanced Chunking: Move beyond basic fixed-size splits to implement structural and contextual chunking strategies that preserve semantic meaning.

Engineer Hybrid Search: Fuse BM25 lexical matching with vector search using Reciprocal Rank Fusion (RRF), ensuring that queries for exact codes, acronyms, and identifiers no longer fail in the vector space.

Optimize Context & Reranking: Implement cross-encoder reranking, intelligent token budgeting, and pre-retrieval metadata filtering driven by lightweight intent classifiers.

Benchmark, don't guess: Use Metrics to understand the search and the answer results! Do not believe in guesses. Learn how to create an automated benchmark and apply it to your RAG pipeline.

Read the full description on Udemy on Udemy (opens in a new tab)

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.