Fundamentals of Retrieval-Augmented Generation with LangChain Course Syllabus

Overview: This course provides a hands-on introduction to Retrieval-Augmented Generation (RAG) using LangChain, guiding you from foundational concepts to building a complete chatbot interface. You'll learn how to combine external data retrieval with LLMs for grounded, context-aware responses. The course is structured into six modules with interactive exercises and coding challenges, totaling approximately 4 hours of content. Each module builds practical skills through real-world implementation scenarios.

Module 1: Getting Started with RAG

Estimated time: 0.5 hours

• Introduction to RAG architecture
• Benefits of RAG over pure LLM approaches
• Understanding retrieval-augmented workflows
• Interactive quiz on RAG fundamentals

Module 2: RAG Basics

Estimated time: 1 hour

• Core components of RAG: retriever and generator
• Index creation and document storage
• Document querying mechanisms
• Building a basic indexing and retrieval pipeline

Module 3: RAG with LangChain

Estimated time: 1 hour

• Implementing document indexing with LangChain
• Constructing augmented queries
• Generating responses using retrieved context
• Validating pipeline output through interactive quiz

Module 4: Frontend with Streamlit

Estimated time: 0.75 hours

• Streamlit app structure and layout
• Integrating LangChain backend with Streamlit UI
• Creating interactive chat elements for RAG system

Module 5: Advanced RAG Challenges

Estimated time: 1 hour

• Handling multiple file formats (e.g., PDFs)
• Switching between vector stores in practice
• Solving real-world ingestion and retrieval challenges

Module 6: Final Project

Estimated time: 0.25 hours

• Final overview quiz
• End-to-end application walkthrough
• Best practices for deploying RAG in production

Prerequisites

• Basic understanding of Python programming
• Familiarity with Jupyter notebooks or interactive coding environments
• Introductory knowledge of large language models (LLMs)

What You'll Be Able to Do After

• Explain core RAG principles and architecture
• Implement a full RAG pipeline using LangChain
• Index and query documents from external sources
• Build a functional chatbot UI with Streamlit
• Solve advanced challenges like multi-format ingestion and vector store switching