Designing a large code base is a complex but manageable task if you follow structured planning and coding best practices. By implementing modular design, using version control, maintaining coding standards, and automating testing and deployment, you will create a maintainable, scalable, and robust application.

Note Coding Best Practices:

Error Handling: Implement robust error handling and logging.

Coding Standards: Follow coding standards and use linters to enforce them (e.g., PEP8 for Python).

Documentation: Write docstrings, comments, and external documentation to ensure code readability.

DRY Principle: Follow the “Don’t Repeat Yourself” principle to avoid redundancy.

Testing: Implement unit tests, integration tests, and end-to-end tests to ensure code reliability.

Here’s an example project structure for a Python-based project:

my_project/
├── app/
│ ├── init.py
│ ├── main.py
│ ├── config.py
│ ├── models/
│ │ ├── init.py
│ │ ├── user.py
│ │ ├── product.py
│ ├── api/
│ │ ├── init.py
│ │ ├── user_api.py
│ │ ├── product_api.py
│ ├── services/
│ │ ├── init.py
│ │ ├── user_service.py
│ │ ├── product_service.py
│ └── utils/
│ ├── init.py
│ ├── helpers.py
│ └── validators.py
├── tests/
│ ├── init.py
│ ├── test_user.py
│ ├── test_product.py
├── docs/
├── requirements.txt
├── README.md
└── setup.py

now reference a real app as example FPE’s structure in Github as well as the dev docs: