Retrieval-Augmented Generation (RAG) Explained | AI Engineer Bootcamp Day 20 | Chitra Karanam

Welcome to Day 20 of the 30 Days AI Engineer Bootcamp! 🚀 In today's session, you'll learn Retrieval-Augmented Generation (RAG)—one of the most important technologies behind modern AI applications like ChatGPT, AI search engines, document chatbots, and enterprise AI assistants. Instead of relying only on what an LLM has learned during training, RAG retrieves relevant information from external knowledge sources and uses it to generate more accurate, up-to-date, and context-aware responses. 📚 In this video, you'll learn: ✅ What is Retrieval-Augmented Generation (RAG)? ✅ Why Large Language Models hallucinate ✅ How RAG solves hallucination problems ✅ RAG Architecture Explained ✅ Embeddings & Vector Databases ✅ Chunking Documents ✅ Similarity Search ✅ Real-world RAG Applications Whether you're an AI Engineer, Machine Learning Engineer, Data Scientist, Software Developer, or AI enthusiast, this video will help you understand one of the most in-demand AI concepts in 2026. 🎯 30 Days AI Engineer Bootcamp Playlist Learn AI from scratch and become job-ready by following this complete bootcamp. 📌 Don't forget to: 👍 Like the video 💬 Comment your questions 🔔 Subscribe for the next day's lesson Hashtags #AI #ArtificialIntelligence #RAG #RetrievalAugmentedGeneration #LLM #GenerativeAI #LangChain #OpenAI #MachineLearning #AIEngineer #PromptEngineering #VectorDatabase #Embeddings #DataScience #Python #ChatGPT #Techonquer #AIBootcamp