- 具备Python、Java或JavaScript等程式语言的经验
- 熟悉软体架构和程式码审查流程
- 对大型语言模型的运作方式有基本了解
目标受众
- 后端工程师
- DevOps团队
- 资深开发人员和技术主管
LLMs 用于代码理解、重构和 Documentation 是一门技术课程，专注于应用大型语言模型（LLMs）来提升代码质量，减少技术债务，并自动化软件团队中的文档任务。
这门由讲师主导的培训（线上或线下）面向中高级软件专业人士，他们希望使用 GPT 等 LLMs 更有效地分析、重构和记录复杂或遗留的代码库。
在培训结束时，参与者将能够：
- 使用 LLMs 解释不熟悉的代码库中的代码、依赖关系和逻辑。
- 识别并重构反模式，提升代码可读性。
- 自动生成并维护内联注释、README 文件和 API 文档。
- 将 LLM 驱动的洞察整合到现有的 CI/CD 和审查工作流程中。
课程形式
- 互动式讲座和讨论。
- 大量练习和实践。
- 在实时实验室环境中进行动手实践。
课程定制选项
- 如需为本课程定制培训，请联系我们进行安排。
使用LLMs理解代码
- 代码解释和逐步分析的提示策略
- 处理不熟悉的代码库和项目
- 分析控制流程、依赖关系和架构
重构代码以提高可维护性
- 识别代码异味、无用代码和反模式
- 重构函数和模块以提高清晰度
- 使用LLMs建议命名规范和设计改进
提升性能和可靠性
- 在AI协助下检测低效和安全风险
- 建议更高效的算法或库
- 重构I/O操作、数据库查询和API调用
自动化代码Documentation
- 生成函数/方法级别的注释和摘要
- 从代码库编写和更新README文件
- 在LLM支持下创建Swagger/OpenAPI文档
与工具链的集成
- 使用VS Code扩展和Copilot Labs进行文档编写
- 将GPT或Claude集成到Git预提交挂钩中
- 在CI管道中集成文档和代码检查
处理遗留和多语言代码库
- 逆向工程旧的或无文档的系统
- 跨语言重构（例如，从Python到TypeScript）
- 案例研究和配对AI编程演示
伦理、质量保证和审查
- 验证AI生成的更改并避免幻觉
- 使用LLMs时的同行评审最佳实践
- 确保可重现性并符合编码标准
总结与下一步
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