example-driven-pattern-learning

Consistency in AI code generation improves by applying few-shot learning to extract coding patterns and principles from provided examples. Supplying good and bad examples allows the AI to systematically learn specific styles, error handling conventions, and test case structures.

Few-shot learning for code quality improvement is a technique that extracts principles and patterns from a small set of examples to guide AI output. It uses good and bad examples to enforce consistent coding styles, response formatting, and error handling conventions.

Generate test cases matching existing patterns by applying template-based extraction to your code. This Skill systematically extracts principles from current test case structures and applies them consistently to create new tests that adhere to your established conventions.

Yes, you can enforce error handling conventions by providing good and bad examples to the AI. This pattern extraction process creates clear contrasts between recommended and discouraged practices, ensuring new API endpoints maintain consistent error handling and response formatting.

Pattern extraction for code generation is needed when your output requires strict adherence to specific coding styles, error handling conventions, or test case structures. It ensures quality improvement by systematically applying learned principles across all generated code.