Code Review in the AI Era: Python Best Practices When Half Your Code Is Generated

AI coding assistants have changed the nature of code review. When a developer accepts an AI suggestion, the code looks correct — it compiles, it passes lint, it follows the project’s patterns. But looking correct and being correct are different things. AI-generated code has specific failure modes that traditional code review practices weren’t designed to catch.

The Plausibility Trap

AI models generate code that looks like code they’ve seen before. The variable names are conventional. The error handling follows common patterns. The API calls use the standard library correctly. This makes AI-generated code harder to review than human-written code, because human-written code that looks this polished usually is correct.

The trap: AI models don’t understand your specific business logic, your database schema, or your edge cases. They generate code that handles the common case correctly and silently fails on the edge case you forgot to mention in the prompt.

New Review Patterns

Review AI-generated code as if reviewing code from a junior developer who writes beautifully but doesn’t fully understand the system. Ask: does this handle the null case? Does it handle the empty list? What happens when the external API returns an error? What happens when the input is twice the expected size?

AI models are particularly bad at error handling. They tend to wrap everything in a try-except that catches Exception and logs the error — which is technically correct but gives you zero information during an incident. Review error handling specifically and require specific exception types.

The Review Workflow Shift

The traditional code review workflow assumes the author understands every line they’re submitting. With AI-generated code, the author might have accepted a suggestion without fully understanding the implementation. This shifts the reviewer’s responsibility from “did the author implement this correctly” to “is this implementation correct, regardless of what the author intended.”

This is a heavier burden. Some teams are responding by requiring AI-generated code to be explicitly marked in pull requests. The reviewer can then apply different scrutiny to marked sections. Other teams are using AI to review AI-generated code, running the same model (or a different one) as a second pass reviewer to catch hallucinations and edge case failures.

Testing as the Safety Net

The most effective defense against AI-generated bugs isn’t better code review — it’s better testing. AI models are good at generating test cases when given clear specifications. Before accepting AI-generated implementation code, generate tests for the expected behavior. Run the tests. If they pass, the implementation is at least correct for the specified cases.

The Bottom Line

AI-generated code is here to stay. The productivity gains are real, and the quality, on average, is acceptable. The challenge isn’t banning AI-generated code — it’s adapting your review and testing practices to the specific failure modes of statistical code generation. Review for edge cases, require specific error handling, and invest in test coverage. These practices were always important. AI just made them non-negotiable.