Large language models are highly capable of generating fluent and human-like responses, but fluency does not always guarantee factual accuracy. When a model receives questions about information outside its training data or recent events beyond its knowledge cutoff, it may produce incorrect or fabricated responses with high confidence. Retrieval Augmented Generation (RAG) was introduced to solve...