Large language models (LLMs) can sometimes produce confident but incorrect information, a phenomenon known as hallucination. This issue is significant in industries like law and healthcare where the accuracy of information generated by LLMs is critical. There are two major categories of hallucinations: intrinsic hallucinations and extrinsic hallucinations. Intrinsic hallucinations tend to contrast the source information given to them, while extrinsic hallucinations occur when LLMs generate information that cannot be verified against the provided source data. Hallucinations can have far-reaching societal implications, undermining trust in reliable information sources and contributing to widespread confusion and mistrust among the public. Several methodologies are used to detect LLM hallucinations: self-evaluation, reference-based detection, uncertainty-based detection, and consistency-based detection. Implementing these approaches ensures the responsible deployment of LLMs and other generative AI technologies, maximizing their positive impact on society.