The piece outlines a framework for creating a self-optimizing football chatbot designed to assist defensive coordinators. The chatbot leverages Databricks technologies to analyze play-by-play data and other relevant statistics, helping coaches anticipate opponent strategies. The project involves two main phases: building the chatbot and optimizing it continuously through expert feedback. In the build phase, data is ingested from trusted sources like nflreadpy into Delta tables, and SQL functions are defined to enable the bot to query this data accurately. The optimization process captures expert feedback through structured MLflow labeling sessions. Subject Matter Experts (SMEs) evaluate the chatbot's outputs, which allows for the ongoing refinement of its responses. The MLflow system includes an alignment function to calibrate the chatbot's judges based on expert input, ensuring that the bot's understanding of what constitutes effective analysis is precise and domain-specific. This is essential, as generic evaluators often fail to grasp the subtleties of football tactics. Key technologies involved include Unity Catalog for data governance and MLflow for managing the feedback loop. The chatbot is built on a model that combines probabilistic language understanding with deterministic SQL functions, ensuring accuracy in data retrieval while maintaining conversational fluency. The architecture allows for rapid iterations and adjustments, enabling the assistant to adapt to changing game conditions and enhance its utility for coordinators, especially in the lead-up to games.