Understand RAG Basics

Overview

"Understand RAG Basics" is an intermediate course for developers and data scientists who want to build more powerful and trustworthy AI applications. While Large Language Models (LLMs) are revolutionary, they often lack specific, up-to-date knowledge and can hallucinate answers. This 2-hour course provides the fundamental solution: Retrieval-Augmented Generation (RAG). You will need to be familiar with basic Python, API, and LLMs. You will also need Python and a code editor like VS Code installed locally. Focused on practical application, this course transitions from theory to execution. You will begin by learning to diagram the core components of a RAG architecture (the retriever, the generator, and the vector database) to understand its data flow. Then, you will translate that knowledge into a functioning application. Through a hands-on project that mirrors a real-world job task, you will use Python to build a minimal RAG pipeline, complete with a local vector store, to successfully ground an LLM with external facts. By the end, you'll be able to build intelligent systems that provide accurate, context-aware answers derived from your own data.

Syllabus

The Architecture of RAG

This foundational module demystifies Retrieval-Augmented Generation. You will learn why RAG is essential for creating reliable AI systems and explore the role and function of each component in its architecture. You will finish by sketching a RAG data flow diagram to solidify your theoretical understanding.

Building a Minimal RAG Pipeline

Moving from theory to practice, this module is all about execution. You will learn how to use Python to build the core components of a RAG system: Embedding text, creating a local vector store, and constructing a prompt that enables an LLM to answer queries using your data.