The Systematic AI Code Review Workflow: Plan, Generate, Validate
SMRTR summary
A developer spent an afternoon crafting what seemed like perfect code with AI assistance—a user registration API that compiled flawlessly and sailed through staging. Hours after deployment, the security team delivered crushing news: the endpoint was riddled with SQL injection vulnerabilities that could have been caught instantly by proper review. This scenario captures the growing crisis of AI-generated code, where artificial intelligence optimizes for functionality over correctness, creating what one developer calls "technical debt at AI speed."
The solution lies in a systematic three-phase workflow that separates code generation from validation. Rather than the typical pattern of generate-deploy-hope, developers need structured planning before prompting AI, focused generation with clear specifications, and specialized review using tools designed to catch security flaws and performance issues that coding AI misses.
The key insight: different AI tools serve different purposes. Generation AI like Cursor optimizes for speed, while review AI like CodeRabbit hunts for vulnerabilities and maintains quality standards, with developers orchestrating both phases to maintain quality at unprecedented coding speeds.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
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