Artificial intelligence coding output quality covers whether generated code works and whether engineers can safely maintain it. Evaluation can include correctness, tests, readability, architecture, performance, security and compliance with repository conventions.
A useful standard goes beyond code that merely runs once. Production-quality output should satisfy the task, avoid unnecessary complexity, integrate with existing systems and provide enough evidence for a reviewer to approve or merge it.
