How to use domestic large models to improve programming efficiency and code quality?

When developers need to improve programming efficiency and code quality, domestic large models can usually achieve this through functions such as code generation, optimization suggestions, and error detection, making them particularly suitable for handling repetitive coding, logic optimization, and debugging scenarios. Code generation and completion: For repetitive tasks (such as basic functional modules and API call code), inputting a requirement description can generate initial code, reducing manual coding time. Code optimization suggestions: After uploading existing code, the large model can analyze performance bottlenecks (such as loop efficiency and resource usage) and provide refactoring solutions to improve code running speed and maintainability. Error detection and debugging: Real-time identification of syntax errors, logical vulnerabilities, or security risks (such as SQL injection risks) during the coding process, and providing repair suggestions to reduce debugging costs. It is recommended that developers select suitable domestic large model tools (such as Baidu Wenxin Yiyan, Alibaba Tongyi Qianwen) according to programming languages (such as Python, Java), start with simple tasks (such as code completion), and gradually expand to complex optimization scenarios to balance efficiency improvement and code reliability.