How to Use RAG with LLMs | Make Your AI Smarter & More Accurate

Learn how to enhance Large Language Models (LLMs) using RAG (Retrieval-Augmented Generation) for smarter, more accurate AI responses! In this tutorial, we’ll show you how RAG allows your AI to fetch real-time, relevant information before generating answers, reducing hallucinations and outdated knowledge. 📥 Download the complete source code: 🔗 GitHub: https://github.com/codewithaarohi/Generative_AI/blob/main/langgraph_rag_new.ipynb 🌐 Website: https://codewithaarohi.ai/downloads/generative_ai/ Both links contain the exact code used in this tutorial. Email - aarohisingla1987@gmail.com In this video, you’ll learn: ✅ Why traditional LLMs have limitations (outdated knowledge, hallucinations, costly retraining) ✅ How RAG fetches fresh information from external sources ✅ The three key steps of RAG: Retrieval → Augmentation → Generation ✅ Hands-on tutorial with LangGraph, RAG, and a Hugging Face LLM (no API key required!) Tools & Technologies Used: 🚀 LangGraph – Manage retrieval and response flow 📚 RAG – Retrieve relevant information dynamically 🤖 Hugging Face LLM – Generate AI responses without an API key Why RAG is Powerful: ✅ Keeps AI updated with real-time knowledge ✅ Reduces hallucinations and misleading answers ✅ Answers private or custom queries efficiently ✅ Saves time & cost by avoiding frequent retraining 🎯 By the end, you’ll be able to enhance any LLM with RAG for more reliable and intelligent AI! 🔔 Subscribe for more AI tutorials and projects! 💬 Questions or feedback? Drop them in the comments!