Recent advancements in artificial intelligence (AI), such as the Large Language model (LLM), are revolutionizing all industries, but their “black box” nature creates anxiety and concern among users, hindering trust and adoption. Explanation can help alleviate these concerns by translating complex system decisions into simple, understandable terms for users. Moreover, explanations help managers and engineers identify, debug, and address potential flaws in the AI system. This paper addresses the challenges and opportunities of explaining LLM system decisions. We explore the limitations of existing explanation techniques (LIME, SHAP) for LLMs and propose a novel framework based on counterfactual Chain-of-Thought (CoT) reasoning. Our framework addresses the context-dependent nature of language by enabling the LLM to generate step-by-step explanations for user input and system-generated counterfactual scenarios. This explanation fosters trust and allows human evaluation of the model’s reasoning process. We contribute to the explainable AI literature by demonstrating the feasibility and advantages of counterfactual CoT explanations, paving the way for safer and more reliable LLM use.