AI Coding Interview Rubric: What to Measure
An AI coding interview rubric should measure the candidate's engineering judgment, not just the amount of code produced. Speed matters less than whether the candidate can understand requirements, validate output, and improve the solution.
The five scoring areas
A simple rubric keeps the interviewer focused. Each category should connect to behavior visible during the session or in the final file changes.
- check_circleRequirement understanding: did the candidate clarify the spec?
- check_circleAI judgment: did they guide and verify AI output?
- check_circleDebugging: did they find and fix issues methodically?
- check_circleImplementation quality: is the solution clear and maintainable?
- check_circleCommunication: can they explain decisions and tradeoffs?
What not to overvalue
Do not overvalue a polished first draft. AI can make weak candidates look fast for a few minutes. The stronger signal appears when the candidate has to inspect, adapt, test, and defend the implementation.
How EvalSpec helps
EvalSpec keeps the spec, files, and interview context together so review can focus on concrete work. That makes rubric-based evaluation easier than judging a conversation from memory.
FAQ
Should AI prompts be part of the score?
They can be, but only as evidence of judgment. A good prompt is useful because it helps the candidate reason, validate, and improve the implementation.
What is the biggest red flag?
The biggest red flag is accepting generated code without understanding or testing it.
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