Feb 12
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Ever wonder how AI chatbots know about news that happened yesterday when their training data is years old? The secret is RAG (Retrieval-Augmented Generation). In this video, we break down RAG from a basic concept to an advanced architecture. We explain how it solves the "confident but clueless" problem of LLM hallucinations by giving AI an open-book test. We also dive into the technical details that make or break a RAG system, including chunking strategies and evaluation metrics. In this video, you will learn: Why LLMs hallucinate and how RAG fixes it. The difference between "Naive" and "Advanced" RAG. The 3-step workflow: Retrieve, Augment, Generate. Why "Chunking" is the secret sauce to performance. How to measure success using the RAGAS framework. ⏱️ Timestamps: 00:00 - Introduction: How does AI know recent info? 00:20 - The Problem: The "Confidently Clueless" LLM 01:17 - The Solution: Giving AI a Library 01:57 - How it Works: The 3-Step Workflow 02:50 - Naive vs. Advanced RAG 03:28 - Chunking: The Critical Step 04:38 - Chunking Strategies (Fixed vs. Semantic) 05:12 - How to Measure Success (RAGAS Framework) 06:10 - The Future of Intelligent Reasoning #AI #RAG #MachineLearning #LLM #GenerativeAI #TechExplained