Reviewing and testing AI-generated code + Measuring and explaining AI productivity
Comes up in: AI-assisted coding, Hiring manager, Code review
This concept is being written. It gets the full five steps — explanation, predict, interview angle, practice with tests, and review cards. Here is exactly what it will cover, so you can study it elsewhere in the meantime.
What it covers
- review checklist (correctness, edge cases, security, licences, hallucinated APIs/packages — "slopsquatting")
- requiring tests
- static analysis
- keeping ownership and understanding
- team policies
- what studies say and do not say (cite only primary studies with dates
- note mixed results)
- measuring in your own team (cycle time, defect rate)
- talking about AI use honestly in interviews
Words you will know
What you will practise
- review 3 AI-generated PRs with planted bugs
- write a one-page team AI-usage guideline
Interview questions this answers
- How would you set an AI-usage policy for a team?
- Tell me about a time an AI tool was wrong and how you noticed.