What to look for when reviewing AI-generated code
AI-generated code fails in recognisable ways. These are the patterns worth checking deliberately, and why ordinary review habits miss most of them.
AI-built software
Insights on taking AI-assisted prototypes and generated code into production safely, covering review, testing, security and deployment discipline.
Teams now reach working software far faster than they reach software they can trust. A prototype assembled with AI assistance demonstrates the idea convincingly, and the pressure to put it in front of customers arrives long before anyone has looked at how it handles failure, load, data or access.
These insights cover the distance between working and dependable: the review that finds what generated code quietly assumed, the tests and observability nobody wrote, the secrets and permissions nobody set, and a deployment path the business can rely on. Productionisation closes that distance without throwing away the speed that created the software in the first place.
Fast to build and safe to run are separate problems, and the second one is still engineering work.