- 具備Python、Java或JavaScript等程式語言的經驗
- 熟悉軟體架構和程式碼審查流程
- 對大型語言模型的運作方式有基本了解
目標受眾
- 後端工程師
- DevOps團隊
- 資深開發人員和技術主管
LLMs 用於代碼理解、重構和 Documentation 是一門技術課程，專注於應用大型語言模型（LLMs）來提升代碼質量，減少技術債務，並自動化軟件團隊中的文檔任務。
這門由講師主導的培訓（線上或線下）面向中高級軟件專業人士，他們希望使用 GPT 等 LLMs 更有效地分析、重構和記錄複雜或遺留的代碼庫。
在培訓結束時，參與者將能夠：
- 使用 LLMs 解釋不熟悉的代碼庫中的代碼、依賴關係和邏輯。
- 識別並重構反模式，提升代碼可讀性。
- 自動生成並維護內聯註釋、README 文件和 API 文檔。
- 將 LLM 驅動的洞察整合到現有的 CI/CD 和審查工作流程中。
課程形式
- 互動式講座和討論。
- 大量練習和實踐。
- 在實時實驗室環境中進行動手實踐。
課程定制選項
- 如需為本課程定制培訓，請聯繫我們進行安排。
使用LLMs理解代碼
- 代碼解釋和逐步分析的提示策略
- 處理不熟悉的代碼庫和項目
- 分析控制流程、依賴關係和架構
重構代碼以提高可維護性
- 識別代碼異味、無用代碼和反模式
- 重構函數和模塊以提高清晰度
- 使用LLMs建議命名規範和設計改進
提升性能和可靠性
- 在AI協助下檢測低效和安全風險
- 建議更高效的算法或庫
- 重構I/O操作、數據庫查詢和API調用
自動化代碼Documentation
- 生成函數/方法級別的註釋和摘要
- 從代碼庫編寫和更新README文件
- 在LLM支持下創建Swagger/OpenAPI文檔
與工具鏈的集成
- 使用VS Code擴展和Copilot Labs進行文檔編寫
- 將GPT或Claude集成到Git預提交掛鉤中
- 在CI管道中集成文檔和代碼檢查
處理遺留和多語言代碼庫
- 逆向工程舊的或無文檔的系統
- 跨語言重構（例如，從Python到TypeScript）
- 案例研究和配對AI編程演示
倫理、質量保證和審查
- 驗證AI生成的更改並避免幻覺
- 使用LLMs時的同行評審最佳實踐
- 確保可重現性並符合編碼標準
總結與下一步
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