This Article examines the advancements made in GitHub Copilot’s custom model for code completions, highlighting the technical innovations that have improved accuracy, responsiveness, and contextual understanding. By analyzing GitHub’s iterative development process, spanning model evaluation, fine-tuning, and reinforcement learning— this Article provides an overview of how the new system achieves faster and more relevant code suggestions. The discussion focuses on the integration of developer feedback, multi-layered evaluation methods, and optimization strategies that balance throughput, latency, and acceptance rates. The findings illustrate a continued evolution in AI-assisted programming, where data-driven model refinement aligns machine-generated code with real-world developer workflows.