The mechanics of how generative AI transforms legal workflows remain opaque to many practitioners, even as adoption accelerates across major corporate law firms. Unlike traditional legal software that executes predefined rules, generative AI models process natural language, learn from vast datasets of case law and contracts, and produce original outputs that mirror human-drafted legal documents. Understanding these underlying mechanisms is essential for legal teams evaluating implementation strategies, especially as firms managing high-volume M&A transactions or complex litigation portfolios seek to optimize billable hours while maintaining quality standards. The application of Generative AI in Legal Operations begins with foundational models trained on legal corpora that include statutes, regulatory texts, judicial opinions, and anonymized transactional documents. These models employ transformer architectures that enable contextual understanding across lengthy documents—a critica...