In an era of rapidly expanding applications of Large Language Models (LLMs), ensuring their reliability is paramount. A critical challenge lies in mitigating errors such as hallucinations – instances where LLMs generate factually incorrect or nonsensical outputs. Our recent publication, “Detecting And Classifying Llm Hallucinations: A Framework For Skill-specific Error Analysis,” introduces a novel framework for systematically identifying and categorizing these errors. This research is foundational for developing robust safety protocols as LLMs are integrated into increasingly critical systems.