Coding output quality covers more than whether source files were generated. It includes functional behavior, architecture, readability, error handling, test coverage, accessibility, performance and fit with the requested experience.
A comparison may also assess creativity, visual detail and thoroughness. These qualities should be balanced with interaction polish and direct evidence that important user flows work.
ELI5
AI coding output quality describes whether AI-produced code is correct, secure, understandable, maintainable and suited to the requested job. Code that runs once can still be poor production code.
For example, an agent may create a feature that passes a basic demonstration but duplicates existing logic and misses error cases. Strong review checks requirements, tests, architecture, security and whether future developers can safely maintain the change.
What dimensions make up coding output quality?
Important dimensions include correctness, completeness, maintainability, testing, security, accessibility, performance and user experience.
Can visual polish compensate for broken behavior?
No. Visual quality is useful, but core interactions and requirements must still function correctly.
