ai-coding-discipline

To prevent silent failures in AI coding, apply a rule-based framework that enforces no silent fallbacks, ensuring data absence is never masked by default values and errors propagate explicitly to system boundaries.

To prevent silent failures in AI coding, apply a rule-based framework that enforces no silent fallbacks, ensuring data absence is never masked by default values and errors propagate explicitly to system boundaries.

To ensure AI-written tests actually fail when code breaks, enforce strict test-driven development rules that require meaningful tests catching real regressions, avoiding hardcoded lookup-table implementations that mask logic failures.

Yes, this coding discipline framework works for debugging and code review by applying a consistent six-rule framework across all code creation, modification, and testing workflows to ensure robust development standards.

The best way to prevent hardcoded lookup-table implementations in AI code is to enforce mandatory coding discipline rules that require generalizable logic over static data matching, ensuring consistent and safe code generation.