May 12
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Why does AI give wrong answers even after you upload PDFs, documents, notes, or company data? In this short, I explain RAG (Retrieval-Augmented Generation) in the simplest way possible. You’ll understand why large language models hallucinate, how modern AI apps search documents before answering, and why tools like Perplexity, Microsoft Copilot, Notion AI, and many custom AI chatbots use RAG systems to improve accuracy. If you are learning Generative AI, LLMs, AI engineering, vector databases, semantic search, embeddings, AI agents, LangChain, or building your own chatbot using private data, this video will help you understand one of the most important concepts behind modern AI applications. I also cover how AI reads PDFs, why uploaded files alone are not enough, and how retrieval pipelines help models generate better responses instead of guessing. This is a beginner-friendly explanation of RAG architecture for students, developers, AI engineers, and anyone exploring machine learning, NLP, and AI tools. If you want to build smarter AI apps using your own documents, knowledge base, or business data, understanding Retrieval-Augmented Generation is essential. Full breakdown on the channel covering advanced RAG techniques, chunking strategies, vector search, reranking, hallucination reduction, and real-world AI system design. #RAG #GenerativeAI #LLM #AIEngineering #MachineLearning #ArtificialIntelligence #LangChain #VectorDatabase #AIChatbot #PerplexityAI