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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🔥 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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