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Reviewing AI-Generated Code: Check Boundaries Before Logic

DEV Community·Pablo Ifrán·30 days ago
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#signal#ai#python#fullscreen#order#order_id
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The code came back. It looks right. Do you ship it? Post 2 gave you structured prompts that produce better AI output. But "better" isn't "perfect." Even a well-constrained prompt will occasionally slip a database call into an application service, or sneak a business rule into a route handler. You still have to review the diff. Reviewing AI-generated code the same way you'd review a colleague's PR is slow, frustrating, and misses the real failures. Here's a workflow that's actually fast and checks the right things first. Why Reviewing AI Code Is Different When a colleague sends you a PR, you can ask them questions. You can understand their intent. With AI output, you can't. The AI made decisions about where code lives, what it imports, how it structures logic based on pattern-matching from its context window. It had no architectural intent. It was optimizing for "generates code that compiles and passes the tests I can see." With a colleague's code, you're asking: Does this solve the problem correctly?…

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