Course · Intermediate
Introduction to Retrieval-Augmented Generation (RAG)
Language models hallucinate and lack access to your data. Retrieval-Augmented Generation fixes this by grounding responses in retrieved documents. This course walks you through building RAG pipelines from scratch, improving retrieval with query expansion and reranking, and diagnosing common failure modes so your systems are reliable in practice.
- Intermediate friendly
- 6 hrs
- 3 lessons
- Premium
Course overview
Learn to build Retrieval-Augmented Generation (RAG) systems in Python, covering pipeline architecture, prompt design, query expansion, reranking, and debugging common failure modes.
What's inside
3 lessons
- 01 120 minBasic RAG Architecture and Implementation
Learn to build Retrieval-Augmented Generation (RAG) systems. Create GitQuest, a Git support agent that retrieves documentation, generates grounded answers with citations, and handles unanswerable queries. Cover pipeline architecture, prompt design, and source attribution.
- 02 120 minRAG Retrieval and Context Management
Learn retrieval techniques (multi-query, HyDE, reranking) and context management to improve RAG precision. Measure recall and optimize candidate pool size through experimentation.
- 03 120 minDiagnosing Common RAG Failure Modes
Learn to diagnose RAG pipeline failures by inspecting retrieval, reranking, and generation stages. Identify vocabulary mismatches, source-type errors, hallucinations, parametric override, and citation issues using systematic debugging workflows.
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