Reviewing AI Generated Work

Code review has always been one of the most important practices in engineering. It is where mistakes get caught, where knowledge transfers between team members, where standards get enforced, and where the collective understanding of a system gets built and maintained over time. None of that has changed with AI-assisted development. What has changed is the volume, the nature of the output being reviewed, and the specific failure modes that reviewers need to be watching for. A team that adopts LLM-assisted development without adapting its review practices is a team that is accruing risk faster than it realises. The code looks fine. It passes the tests. It does what was asked of it. And underneath that surface coherence there are patterns, assumptions, and subtle problems that only become visible when something goes wrong in production or when the next engineer tries to extend the code and finds it significantly harder than it should be. This article is about how to review AI-generated wo