Effective LLM observability is crucial for managing complex applications, providing real-time visibility into model performance and user interactions. As organizations adopt AI models, they face significant challenges in handling the exponential growth of observability data generated by these systems. Efficient solutions are essential to address these complexities and ensure optimal system performance. Key components of LLM observability include understanding and detecting issues such as multimodal model hallucinations, implementing advanced techniques for detection, and utilizing specialized tools that provide insights into model performance and user interactions. The growing importance of real-time observability is critical in addressing challenges like latency and performance issues in deployed LLMs. By implementing robust privacy measures and security protocols, organizations can maintain trust with their users while meeting regulatory requirements. Organizations have successfully implemented observability practices using tools like GenAI Studio, demonstrating the benefits of this approach in achieving high-performance AI applications.