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RAG stands for Retrieval-Augmented Generation. It helps an LLM answer using external knowledge such as company documents, internal policies, research papers, or private data that the model may not already know. Here is how it works: Documents are split into smaller pieces called chunks. When a user asks a question, the system searches those chunks and retrieves only the most relevant information. That information is added to the prompt, and the LLM uses it to generate the answer. So the basic fl
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