  Large language models (LLMs) like ChatGPT demonstrate the remarkable progress
of artificial intelligence. However, their tendency to hallucinate -- generate
plausible but false information -- poses a significant challenge. This issue is
critical, as seen in recent court cases where ChatGPT's use led to citations of
non-existent legal rulings. This paper explores how Retrieval-Augmented
Generation (RAG) can counter hallucinations by integrating external knowledge
with prompts. We empirically evaluate RAG against standard LLMs using prompts
designed to induce hallucinations. Our results show that RAG increases accuracy
in some cases, but can still be misled when prompts directly contradict the
model's pre-trained understanding. These findings highlight the complex nature
of hallucinations and the need for more robust solutions to ensure LLM
reliability in real-world applications. We offer practical recommendations for
RAG deployment and discuss implications for the development of more trustworthy
LLMs.
