AI Code Looks Right. That’s the Problem.

AI coding tools have become integral to the daily routines of software engineers, enabling faster code production but also introducing new challenges such as the proliferation of "AI slop"—code that appears correct but is subtly flawed or misaligned with project needs. This has led to concerns about the emergence of "write-only" code, where engineers struggle to understand the rapidly generated code. Traditional code review processes are ill-equipped to address these issues, as they lack the ability to capture and verify the original intent behind AI-generated code. Intent-driven development practices, which emphasize formalizing and reviewing specifications before implementation, are proposed as a solution. By clearly defining and reviewing the intended outcomes before allowing AI to generate code, teams can better manage AI output and mitigate the risks of misaligned code. This requires a shift from merely checking if the code appears correct to ensuring it fulfills the agreed-upon specifications, thus transforming code review into a process that can effectively catch AI slop and maintain code quality.

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