Large language models (LLMs) are revolutionizing AI, but their complexity makes them a "black box." We don't always know *why* they generate what they do, which is a problem for trust and reliability. XAI (Explainable AI) aims to shed light on these inner workings. This involves tools and techniques to interpret LLM behavior, from visualizing attention mechanisms (like how an LLM focuses on different words in a sentence) to generating counterfactuals (showing what would happen if the input were slightly different). The goal is to move beyond simply using LLMs for impressive outputs to truly understanding their decision-making processes. This shift is crucial for several reasons: First, it builds trust. If we can understand why an LLM makes a specific claim, we can better evaluate its validity. Second, it improves model debugging. Insights into how LLMs work help us identify and fix errors in their reasoning. Third, it supports ethical AI development. By understanding how LLMs process information, we can address issues of bias and misinformation more effectively. The challenge lies in balancing the need for accuracy with the desire for transparency. Explanations need to be both accurate and understandable to a broad audience, including non-technical users. Current XAI methods are making progress, but we need more research to fully unlock the potential of interpretable LLMs. As LLMs become increasingly integrated into daily life, from medical diagnoses to financial advice, making them explainable isn't just a technical challenge—it's a societal imperative.