#  Introduction   AI Explainability (XAI) has dominated the real-world AI systems landscape over the past few years, with large language models (LLMs) being no exception. In these highly complex and powerful models, transitioning from static to dynamic evaluation becomes imperative to better understand how these black-box systems generate natural language outputs. In addition, synthesizing dynamic evaluation with robust statistical approaches and affordable, production-ready frameworks for observability are also pivotal trends under the radar in the industry. This article discusses LLM explainability and outlines the advances, trends, and ongoing developments in this important field of study that attempts to measure, interpret, and better manage one of the most sophisticated forms of AI systems to date.   Even though LLMs have revolutionized the AI field as a whole, their inner workings remain largely opaque.…