Large Language Models (LLMs) have become integral in various fields, from customer support to complex applications in healthcare and finance, yet they face challenges such as hallucinations, where they produce factually incorrect or fictional responses. These hallucinations arise due to factors like limitations in training data, model architecture constraints, and overfitting, potentially leading to serious consequences in critical applications. Strategies to mitigate hallucinations include using retrieval-augmented generation techniques to cross-check facts, employing effective prompting techniques, and maintaining human oversight for continuous monitoring and refinement. Selecting appropriate models and training them on high-quality, diverse datasets can enhance reliability, and involvement of user feedback ensures models evolve to meet specific needs. Gladia, a company offering audio intelligence APIs, emphasizes the importance of such strategies in maintaining the accuracy and dependability of LLMs in their applications.