Build a Mini RAG System: Hands-On Workshop
By Dorian Laurenceau
Updated
📅 Last reviewed: April 24, 2026. Updated with April 2026 findings and community feedback.
Theory without practice is just trivia. In this workshop, you will build a complete RAG system, a knowledge base that answers questions about your own documents. By the end, you will understand every component of the RAG pipeline and how to tune it.
Workshop Overview
What We Are Building
A document Q and A system in 5 steps
Step 1, Load Documents
// Pseudocode for the workshop
const docs = loadDocuments('./knowledge-base/')
console.log('Loaded', docs.length, 'documents')💡 Start small: 5-10 documents. You can scale later.
Tuning Your RAG System
Embedding Models for RAG
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Why does accuracy decrease when K (number of retrieved chunks) is too high?
Evaluation: How Good Is Your RAG?
Common Issues and Fixes
RAG Troubleshooting Guide
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Scaling Beyond the Workshop
Test Your Understanding
Question 1 of 5.
Mini RAG Workshop Check
0 / 5 answered
Q1.What are the 5 steps to build a RAG system?
5 questions remaining
Continue Learning
You have built your first RAG system. In the next module, you will learn about AI Agents, systems that can reason, use tools, and take actions autonomously using the ReAct framework.
Continue to ReAct Method: AI Agents Explained to understand autonomous AI systems.
Module 5 — RAG (Retrieval-Augmented Generation)
Ground AI responses in your own documents and data sources.
Dorian Laurenceau
Full-Stack Developer & Learning DesignerFull-stack web developer and learning designer. I spent 4 years as a freelance full-stack developer and 4 years teaching React, JavaScript, HTML/CSS and WordPress to adult learners. Today I design learning paths in web development and AI, grounded in learning science. I founded learn-prompting.fr to make AI practical and accessible, and built the Bluff app to gamify political transparency.
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FAQ
What will I learn in this RAG guide?+
Build a complete Retrieval-Augmented Generation system from scratch. Load documents, create embeddings, implement vector search, and generate grounded answers step by step.