一句话：提出“词汇桥”术语契约协议，引导 LLM 用母语解释代码逻辑的同时保留英文技术术语，并实现 VS Code 扩展 BhashaSE。
印度每年培养约 150 万计算机科学本科生，但软件工程工具仍以英语为主；随着行业转向 AI 辅助开发，这种英语中心假设无法覆盖大量非英语开发者。LLM 虽提供了多语言基础，但无约束地使用会在低资源印度语言中产生语义漂移，也难以稳定强化职业软件开发中通用的英文术语。为此作者提出 Vocabulary Bridge：一种面向双语代码解释的术语契约，目标是用开发者母语解释代码逻辑，同时显式保留标准英文技术术语，从而桥接“母语理解”与“专业英语能力”。作为早期可行性证据，作者实现了 Visual Studio Code 扩展 BhashaSE，支持泰卢固语、印地语、泰米尔语和卡纳达语，并将全部工具工件开源。该工作以形式化协议的方式引导 LLM 生成，为多语言 AI 辅助软件工程开辟了新方向；目前主要贡献在于协议设计与工具实现，尚未给出系统性用户评估结果。
India produces about 1.5 million Computer Science undergraduates each year, yet software engineering tooling remains predominantly in English. As the industry transitions to AI-assisted development, this English-centric assumption fails a significant portion of the global developer workforce. While AI and Large Language Models (LLMs) provide a multilingual foundation, their unconstrained application often suffers from semantic drift in low-resource Indian languages and may not consistently reinforce the English terminology commonly used in professional software development contexts. To address this gap, we propose Vocabulary Bridge: a terminology contract for bilingual code explanation. Unlike generic translation tools, this protocol aims at explaining code logic in the developer’s native language while explicitly retaining standard technical English terms. As early feasibility evidence, we present BhashaSE, a Visual Studio Code extension implementing this protocol for Telugu, Hindi, Tamil, and Kannada. By formalizing this approach, we aim to guide LLM generation to support the transition from native understanding to professional English proficiency, establishing a new direction for multilingual AI-assisted software engineering. All tool-related artifacts are open-sourced at https://github.com/rishalab/BhashaSE