Master the transition from simple prototypes to production-grade RAG systems by addressing the critical scaling, debugging, and security challenges that standard tutorials often ignore.

We just posted a comprehensive course on the freeCodeCamp.org YouTube channel that covers the entire RAG pipeline—from vector database optimization and observability to advanced agentic and multimodal architectures. You will learn to make sure your AI applications are robust, secure, and ready for deployment. Paulo Dichone created this course.

Here are the sections in the course:

• Intro

• Full RAG Overview

• Development Environment Setup

• Document Loader - Overview

• Document Processing Pipeline - RAG Indexing Pipeline

• Embedding Dimensions - Deep Dive

• Hands-on - Create a Vector DB Using Chroma

• Similarity Search with Scores

• Building a Basic RAG System

• Debugging RAG Systems

• Hybrid Search

• Token Budgeting

• Observability - Introduction

• LangSmith Setup

• RAG Optimization

• Scaling RAG Systems

• The Real Costs of Vector Search

• Production Hosting

• Supabase and PGVector - Set up and Introduction

• Three Pillars of Production Visibility

• Production Project

• Set up the Security Layer

• Set up the LangGraph Agent and the FastAPI API - Testing and LangSmith Observability Dashboard

• Test the Security Layer

• Security Checklist

• Advanced RAG Topics - Long Context Models vs RAG

• Contextual Retrieval

• Late Chunking vs Early Chunking

• Agentic RAG - Self-Correcting Retrieval

• GraphRAG - Multi-hop Reasoning

• Multimodal RAG - ColPali - Vision-Based Document RAG

• Summary - Advanced RAG (Current State)

• RAG Evolution - Overview

• Outro

Watch the full course on the freeCodeCamp.org YouTube channel (8-hour watch).