The rapid evolution of agile software devel-opment practices has generated increasingly complex codebases, creating a persistent gap between active source code and its corresponding technical documen-tation. This research investigates the application of generative artificial intelligence directly within ver-sion control workflows, examining methodologies for automating the extraction, generation, and continu-ous maintenance of context-aware project documenta-tion. Through the development of DocuMate—an event-driven framework integrating Large Language Mod-els (LLMs), structural project analysis, and native Git hooks—this study evaluates the effectiveness of auto-mated configuration management for critical project files, including READMEs, Dockerfiles, and environ-ment templates. Our implementation demonstrates that combining local codebase heuristics with dynamically prompted LLM generation achieves highly accurate, repository-specific documentation, significantly reduc-ing manual developer overhead compared to traditional static templating. Furthermore, the research addresses critical challenges including \"documentation drift,\" context preservation, and deployment safety through a human-in-the-loop \"pending-to-approved\" state mecha-nism. The implications of this work extend to technical debt reduction, open-source maintainability, automated infrastructure configuration, and streamlined devel-oper onboarding processes.