We discuss Google's journey in developing and refining two internal AI-based IDE features: code completion and natural-language-driven code transformation (Transform Code). We address challenges in latency, user experience and suggestion quality, all backed by rigorous experimentation. The article serves as an example of how to refine AI developer tools across the user interface, backend, and model layers, to deliver tangible productivity improvements in an enterprise setting.

翻译：本文探讨了Google在开发和完善两项内部基于AI的集成开发环境（IDE）功能——代码补全与自然语言驱动的代码转换（Transform Code）——过程中的实践经验。我们重点分析了在延迟、用户体验及建议质量等方面面临的挑战，所有结论均基于严格的实验验证。本文通过展示如何在用户界面、后端服务及模型层面对AI开发工具进行系统性优化，为企业环境中实现切实的生产力提升提供了范例。