LLM hallucinations, a critical issue for enterprise adoption, can be mitigated by working with suitable models, prompts, data, and context through vector databases. Hallucination in AI refers to the creation of text that sounds real but is incorrect or unrelated to the context, often due to biases, incomplete understanding, or training data issues. This problem affects various tasks such as abstractive summarization, dialogue generation, machine translation, data-to-text generation, and vision-language model generation. Understanding and addressing hallucinations are crucial for increasing the reliability and usability of AI systems in real-world applications. Researchers are working on mitigating hallucinations through techniques like noise robustness, negative rejection, information integration, and counterfactual robustness. Tools like Galileo GenAI Studio provide platforms for rapid evaluation, experimentation, and observability to identify and mitigate hallucinations. Several research-backed evaluation metrics and papers, such as Chainpoll, Survey of Hallucination in Natural Language Generation, The Curious Case of Hallucinations in Neural Machine Translation, and Detecting Hallucinated Content in Conditional Neural Sequence Generation, offer insights into addressing this issue.