Course overview
Retrieval-Augmented Generation (RAG) lets you build AI systems that answer questions grounded in real documents rather than relying on model memory alone. In this course, you’ll build a RAG pipeline from scratch, improve retrieval with techniques like query expansion and reranking, and learn to diagnose common failure modes. The focus is on practical implementation: understanding how each stage of the pipeline works and how to make the system reliable.
Key skills
- Building a functional RAG pipeline from scratch in Python
- Implementing query expansion and reranking to improve retrieval precision
- Managing context windows to stay within token limits
- Applying systematic debugging workflows to identify pipeline failures
- Distinguishing retrieval failures from generation failures and resolving each
Course outline
Introduction to Retrieval-Augmented Generation (RAG) [3 lessons]
Basic RAG Architecture and Implementation 2h
Lesson Objectives- Understand what RAG is and problems it solves
- Build a four-stage RAG pipeline from scratch
- Design effective prompts for grounded generation
- Implement source attribution with citation validation
- Connect vector retrieval to language model generation
RAG Retrieval and Context Management 2h
Lesson Objectives- Implement query expansion techniques to improve retrieval recall
- Apply reranking using Cohere's API to prioritize relevant documents
- Manage context windows by selecting chunks within token limits
- Compare recall@5 versus latency to identify optimal candidate pool size
Diagnosing Common RAG Failure Modes 2h
Lesson Objectives- Inspect intermediate outputs to locate pipeline failure sources
- Distinguish retrieval failures from generation failures systematically
- Identify vocabulary mismatch and source-type mismatch issues
- Detect hallucinated content, parametric override, and citation errors
- Apply systematic debugging workflow to RAG pipelines
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