RAG Explained in 60 Seconds | Simplest Explanation of Retrieval-Augmented Generation
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YouTubeShishir Srivastav
RAG Explained in 60 Seconds | Simplest Explanation of Retrieval-Augmented Generation
Confused about RAG (Retrieval-Augmented Generation)? In this short, I break it down in the simplest way possible — under 1 minute! RAG combines retrieval of external knowledge with generative AI models like GPT, making responses more accurate, up-to-date, and reliable. Perfect for AI beginners, developers, and enthusiasts who want to ...
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Retrieval-augmented Generation Explained
RAG explained !! RAG (Retrieval-Augmented Generation) controls how AI systems find and use information before generating answers. It combines information retrieval with text generation so models don’t rely only on training data. It includes document ingestion, chunking, embeddings, vector databases, similarity search, and context injection. Good RAG design improves accuracy, relevance, trust, and scalability, and significantly reduces hallucinations. With RAG, you control what data the AI can ac
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RAG explained !! RAG (Retrieval-Augmented Generation) controls how AI systems find and use information before generating answers. It combines information retrieval with text generation so models don’t rely only on training data. It includes document ingestion, chunking, embeddings, vector databases, similarity search, and context injection. Good RAG design improves accuracy, relevance, trust, and scalability, and significantly reduces hallucinations. With RAG, you control what data the AI can ac
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