RAG Framework Explained: Building Hallucination-Free AI Systems | CodeCraft Academy posted on the topic | LinkedIn
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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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Unlock the benefits of RAG with open source tools, explore RAG reference architecture, key components and services and the complete deployment guide. | Ubuntu
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🔥 I spent 2 weeks simplifying RAG. Here’s the clearest breakdown you’ll ever see .. RAG turns a general LLM into a domain expert using your knowledge. 💡 What is RAG ? Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. RAG has 3 major steps: 1️⃣ Retrieve: Find relevant information from your own data using embeddings vector searc
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New JavaScript short course! Build a full-stack web application that uses RAG in “JavaScript RAG Web Apps with LlamaIndex,” taught by Laurie Voss, VP of Developer Relations at LlamaIndex and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like
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🔥 How to Learn RAG in 2024: Go from Beginner to Expert (Step by Step) 🚀
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