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What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances the responses of a Large Language Model (LLM) by dynamically retrieving relevant information from an external knowledge source before generating an answer.

Unlike fine-tuning, which requires retraining the model on new data, RAG allows an LLM to access up-to-date or domain-specific information without modifying its underlying parameters. This makes RAG highly efficient for applications requiring real-time, accurate, or specialized knowledge.

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