What is RAG and CAG In LLMs?

Start Testing Free: https://accounts.lambdatest.com/register?utm_source=youtube&utm_medium=organic&utm_campaign=rag_tutorial In this video, join Jaydeep as he deep dives into hallucinations in Large Language Models (LLMs) and explores the concepts of Retrieval Augmented Generation (RAG) and Cache Augmented Generation (CAG). Learn the types of hallucinations, including factual errors, sentence contradictions, and nonsensical outputs, and understand how we can control them. Jaydeep explains how we can reduce hallucinations in AI models by creating knowledge boundaries and how retrieval-based techniques can improve the accuracy of AI-generated responses. Learn about the importance of vector embeddings, vector stores, and how models can retrieve up-to-date information to enhance their responses. If you're building AI-driven systems, this video is a must-watch for understanding how to prevent hallucinations and create more accurate, reliable AI systems. 𝐕𝐢𝐝𝐞𝐨 𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 👀 00:00 Introduction 01:20 What Are AI Hallucinations? 03:40 Why LLMs Sometimes Give Wrong Answers 06:10 The 4 Types of LLM Hallucinations 09:00 Why Hallucinations Are a Big Problem 11:40 How Companies Try to Fix Hallucinations 14:00 Introduction to Retrieval Augmented Generation (RAG) 17:00 How RAG Works 20:00 What Are Embeddings? 23:00 How Text Becomes Vectors 26:00 Vector Databases Explained 29:00 Similarity Search 31:30 The Complete RAG Pipeline 33:30 Advanced Retrieval (GraphRAG / CAG) 35:00 Final Thoughts