Beginner’s Guide to RAG Micro-Credential

Beginner’s Guide to RAG Micro-Credential

Duration: 3-4 Hours

Description

This micro-credential provides a hands-on introduction to Retrieval-Augmented Generation. Participants explore the fundamentals of RAG, learn how to create embeddings, configure vector stores, and construct a complete RAG pipeline. Through practical labs, they gain the skills to evaluate performance, troubleshoot common issues, and apply best practices for reliable, scalable RAG solutions.

Audience

This micro-credential is ideal for developers, data scientists, and technology enthusiasts who want to understand and implement RAG systems from the ground up. It suits those with basic programming knowledge who are eager to enhance language models with external data sources, whether for research projects, AI-driven applications, or enterprise use cases.

Objectives

  • Understand the core components and a simple workflow of a RAG system
  • Build a complete, functional RAG pipeline from a knowledge base
  • Identify and address basic issues in a RAG system
  • Use a RAG system to answer questions based on custom data

Prerequisites

Developers with basic Python proficiency who are new to the concept of Retrieval-Augmented Generation (RAG) are perfect for this micro-credential. This course assumes some familiarity with Large Language Models (LLMs), but no prior experience with information retrieval or vector databases is required.

Course Outline

Module 1: The RAG Fundamentals

  • Introduction to RAG
  • RAG vs. Fine-Tuning
  • The Core Components (Loader/Chunker, Retriever, and Generator)
  • Lab – Loading and Chunking a Simple Text Document

Module 2: Building Your First RAG Pipeline

  • Create Embeddings
  • Setting Up the Vector Store
  • Putting It All Together
  • Lab – Building a Functional RAG Application

Module 3: Troubleshooting and Best Practices

  • Basic Evaluation
  • Common Pitfalls
  • Improving the System
  • Exploring Frameworks