Background requirements
When dealing with multilingual projects, developers often face problems such as inconsistent code styles and syntax errors.CortexON's code agent guarantees the quality of output through the following mechanisms.
Quality Enhancement Program
- Precision input specification::
- Use "Generate a Python crawler that requires asynchronous processing + error retries" instead of "Write a crawler".
- Specify the technology stack (e.g., "use Pandas to process data")
- Three-tier optimization mechanism::
- Click on the "Debug" button for static analysis after initial generation.
- Add annotations and type hints through the Optimize feature
- Linkage with file agents to save use case test set
- Language-specific strategies::
Emphasis on OOP design for Java
Focus on PEP8 specification for Python
Configuring ESLint rules for JavaScript
Practice Tips
1. Create a .code-template folder in the root directory of the project to store sample code.
2. Periodic adoptiondocker-compose down && docker-compose up --force-recreateUpdating model weights
This answer comes from the articleCortexON: Open source multi-agent AI tool to support automation of complex tasksThe
May not be reproduced without permission:AI productivity tools " How to achieve quality improvement in cross-language code generation?
