Jennifer Umoke

Introduction​

Retrieval Augmented Generation (RAG) represents one of the most powerful applications of Large Language Models (LLMs) today. It addresses a fundamental challenge with LLMs: their tendency to hallucinate or generate incorrect information when they lack sufficient context. RAG solves this by grounding model responses in relevant source materials, significantly enhancing the accuracy and reliability of AI-powered applications.

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