A feature can pass its happy-path test and still fail in everyday use.
When testing a Python function or API, try more than the expected input. Include empty values, malformed requests, missing records, permission failures, and dependency outages. These cases often reveal problems that a quick manual check will miss.
Automated tests make these checks repeatable. Unit tests help isolate individual behaviours, while integration tests show whether components such as APIs and databases work together. For AI-enabled features, add evaluation criteria for relevance, correctness, and safe handling of unexpected responses.
A useful project is to build a small Python API, write tests for its normal and failure paths, and then add one AI feature without weakening the existing checks.
The Full-Stack Python For AI Developers campaign focuses on practical Python, full-stack, and AI development.
What's one edge case you always test before calling a feature ready?