ai-coding-discipline

To prevent silent fallbacks in AI-generated code, apply coding-discipline rules that enforce fail-fast behavior and prohibit catch-all try/catch blocks in business logic, ensuring missing data is not masked.

The best way to enforce error propagation is to prohibit catch-all try/catch blocks within business logic and mandate boundary-level error handling, ensuring failures surface immediately rather than being swallowed.

To write meaningful tests that fail when core logic breaks, apply test-driven development principles and ensure your testing strategy focuses on failure-focused assertions rather than superficial coverage.

Debug logs should be retained during the bug fixing workflow until the fix is verified, because disciplined debugging workflows require continuous validation to ensure the error propagation has been genuinely resolved.

Yes, coding discipline discourages hardcoded lookup tables in favor of real logic, preventing common AI-generated code anti-patterns and ensuring that business rules are executed through dynamic implementation.

Yes, you can apply coding discipline rules to code reviewing and editing tasks, as the rules provide a clear, repeatable checklist for code changes across writing, reviewing, and debugging workflows.