This thesis addresses the design and implementation of an intelligent system built on a Retrieval-Augmented Generation (RAG) framework coupled with a Large Language Model (LLM), engineered to autonomously generate football match commentary from minimal structured input. The architecture fuses advanced neural language generation with dynamic retrieval of player- and team-specific statistics, enabling the production of detailed and contextually relevant narratives. The approach encompasses the development of an event annotation framework and a user-friendly interface that enables users to select match events and provide relevant contextual details. A thoughtfully engineered prompting strategy, complemented by few-shot examples, directs the LLM to generate coherent and contextually precise commentary while maintaining factual integrity, including information such as goal scorer, assist provider, type of shot, and event timing.