How to Build RAG From Scratch in 50 Lines of Python - Full Tutorial

This video is sponsored by TripleTen, a professional education platform designed to help tech professionals and career changers move into higher-paying tech and AI roles. Explore two TripleTen programs for experienced engineers: AI Systems Engineering (22 weeks) https://tripleten.com/systems-engineer/?utm_source=youtube&utm_medium=video&utm_campaign=techwithtim AI and Machine Learning (20 weeks) https://tripleten.com/ai-machine-learning/?utm_source=youtube&utm_medium=video&utm_campaign=techwithtim Both are for engineers with 2+ years of recent, hands-on production experience and require a short skills audit before you can enroll. Both also include career coaching and mentorship to help you stand out in the job market. Ask a normal AI model “what’s the alarm code at my cafe?” and it has no idea, because your handbook, notes, and PDFs were never part of its training data. It either refuses or makes something up. RAG fixes that, and it’s a lot simpler than most people make it sound. In this video I build a complete RAG system from scratch in plain Python and under 50 lines of code. No LangChain, no pre-built vector database, no closed-source models, and everything runs locally on your own computer, so it’s free and completely private. 🚀 Tools I Use Get 10% off with code techwithtim Openclaw setup: https://www.hostinger.com/techwithtim VPS setup: https://www.hostinger.com/techwithtim10 Wispr Flow (Best AI Dictation): https://ref.wisprflow.ai/TechWithTim-oct26 🎞 Video Resources 🎞 Get all of the code in this video: https://www.aiagentbuilders.co/yt-resources/rag-from-scratch ⏳ Timestamps ⏳ 00:00 | Overview 00:26 | The Problem 01:18 | What is RAG 02:03 | How it Works 03:01 | TripleTen 04:48 | Step 0 - Models 13:14 | Step 1 - Chunking 16:25 | Step 2 - Embedding 18:31 | Step 3 - Searching 21:47 | Step 4 - Calling the LLM 25:13 | Next Steps Hashtags #RAG #Python #Ollama UAE Media License Number: 3635141