Getting code to work is one milestone. Making it easy to test, extend, and maintain is another.
As a Python project grows, a few practices become increasingly valuable:
Separate business logic from API routes.
Keep configuration and secrets out of source code.
Validate incoming data instead of trusting every request.
Add automated tests for important behavior.
Use logging to investigate unexpected failures.
These habits matter even more when an application integrates external APIs or AI services, where network errors, unexpected responses, and changing dependencies can affect reliability.
A useful exercise is to take a small Python script and turn it into a properly structured application with tests and documentation.
For developers who want to explore Python fundamentals alongside web development and AI projects, Full-Stack Python For AI Developers is a related learning resource.
What's one code-quality practice that has made your Python projects easier to maintain?
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