LLM vs RAG: Understanding AI s Brain and Research Assistant | Phyllis Chow posted on the topic | LinkedIn
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RAG augments a standard LLM pipeline with a retrieval step; injecting external, often domain-specific data into the prompt before generation. This improves factual accuracy, enables source attribution, and avoids the need for expensive retraining. By decoupling retrieval from generation, each component does what it does best. In our new course on Retrieval Augmented Generation, you’ll learn how to build production-grade RAG systems, covering architecture, retrieval strategies, prompt design, and
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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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