Why AI-Generated Code Needs Intent-Level Tests, Not Just Green Checks

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Modern large language models can generate code that compiles cleanly and passes standard unit tests while still violating critical business rules, such as security constraints or financial precision requirements. Unlike human developers, LLMs optimize for statistical pattern matching rather than logical intent, making them prone to producing confidently broken code. A three-layer testing hierarchy is proposed — covering syntax, behavior, and domain invariants — with most AI-generated code passing only the first layer. Intent-level validation strategies such as property-based testing, state-machine verification, and anti-pattern suites are recommended to catch logical and business-rule failures. Integrating these checks into CI/CD pipelines is presented as a necessary step for teams relying on AI-assisted development.

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