How to Use RAG with LLMs for Better AI Responses ?
🔍 Want to make your AI smarter and more accurate? In this video, we explore how RAG (Retrieval-Augmented Generation) improves LLMs (Large Language Models) by allowing them to fetch and use real-time, relevant information before generating responses.
GitHub: https://github.com/AarohiSingla/Generative_AI/blob/main/langgraph_rag.ipynb
📌 What You’ll Learn:
✅ Why traditional LLMs have limitations (outdated knowledge, hallucinations, expensive retraining).
✅ How RAG helps AI retrieve fresh information from external sources.
✅ The three key steps of RAG: Retrieval, Augmentation, and Generation.
✅ Hands-on tutorial using LangGraph, RAG, and an LLM from Hugging Face (no API key required!).
🔧 Tools & Technologies Used:
🚀 LangGraph – To manage retrieval and response flow.
📚 RAG – To fetch relevant information dynamically.
🤖 Hugging Face LLM – No need for an API key!
📌 Why RAG is Powerful?
✅ Keeps AI updated with real-time knowledge
✅ Reduces hallucinations (wrong or misleading answers)
✅ Helps AI answer private/custom queries
✅ Saves time & cost by avoiding frequent retraining
🎯 By the end of this video, you'll know how to enhance LLMs with RAG to create a more reliable and intelligent AI assistant!
🔔 Subscribe for more AI tutorials!
💬 Have questions? Drop them in the comments!