Retrieval Augmented Generation (RAG): Enhancing Large Language Models with External Knowledge Large Language Models (LLMs) have revolutionized natural language processing, demonstrating impressive capabilities in generating human-like text, answering questions, and performing various creative tasks. However, LLMs are inherently trained on a fixed dataset, meaning their knowledge is static and can become outdated. This limitation can lead to the generation of inaccurate, irrelevant, or hallucinated information, particularly when dealing with specialized domains or recent events. This is where Retrieval Augmented Generation (RAG) emerges as a powerful paradigm, significantly enhancing LLMs by integrating external, up-to-date, and domain-specific knowledge. The Core Problem: LLM Limitations Imagine asking an LLM a question about a niche scientific discovery made last week.…