How Do I Enforce Quality Checks on AI-Generated Code in CI/CD?

AI-generated code, while produced quickly, is not inherently production-ready and must undergo rigorous quality checks similar to human-written code within CI/CD pipelines. The key is to enforce strong CI fundamentals, treating AI-generated code just as any other code, by implementing linting, static analysis, security scanning, automated testing, and review rules. This approach ensures that potential issues such as subtle bugs, insecure patterns, or lack of error handling are identified and addressed before the code is integrated into production. An example illustrates how AI-generated code might appear functional but contain risks, like SQL injection, that should be caught by a robust CI pipeline. Steps such as enforcing linting, adding static analysis, running security scans, requiring test execution, and protecting the main branch are crucial to maintaining code quality. While AI changes the code writing process, it should not alter validation standards, emphasizing that a strong CI pipeline negates the need for AI-specific rules by ensuring code quality is upheld universally.