AI Code Review Policies: Where Humans Stay in the Loop

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-AI-code-review-policies-creators

1. Which type of code change requires mandatory human review in an AI-augmented review process?

   1. Fixing a typo in a comment
   2. Updating CSS styling for a button
   3. Adding a new dependency to the project
   4. Correcting a grammatical error in documentation

2. What does it mean to calibrate AI code review strictness to team standards?

   1. Requiring all senior engineers to approve every AI suggestion
   2. Adjusting AI warnings to match the team's established coding conventions rather than generic industry rules
   3. Setting the AI to reject all code with any warning
   4. Training the AI on the team's past code commits

3. A senior engineer overrides an AI suggestion and wants to proceed with their approach. According to best practices for AI-augmented review, what should happen next?

   1. The code should be rejected to maintain AI authority
   2. The AI should be retrained immediately on this specific case
   3. The engineer should simply proceed since they are senior
   4. The override reason should be documented so the team can learn

4. What aspect of code review can AI NOT replace, even with perfect flagging of issues?

   1. Detecting syntax errors
   2. Identifying performance bottlenecks
   3. Catching security vulnerabilities
   4. The team conversation and knowledge sharing that happens during review

5. What is the primary risk of treating code quality as 'a pure AI problem'?

   1. AI will start writing all the code itself
   2. The code will actually improve too quickly
   3. The AI might become too expensive to operate
   4. Developers may stop thinking critically about code quality and rely entirely on AI

6. In an AI-augmented code review policy, what is the purpose of setting review velocity targets?

   1. To force developers to approve code faster
   2. To track whether the AI-augmented process actually speeds up team workflow
   3. To measure how quickly humans can override AI suggestions
   4. To ensure AI reviews code instantly without delay

7. Which of the following is an appropriate task for AI to handle in a first-pass review?

   1. Evaluating whether a new microservices architecture fits the system
   2. Deciding if a proposed algorithm change is architecturally sound
   3. Assessing whether a security fix addresses the root cause of a vulnerability
   4. Checking code for consistent indentation and naming conventions

8. What is wrong with using industry default settings for AI code review without customization?

   1. The defaults may flag things your team allows or miss things your team cares about
   2. Defaults will cause the AI to malfunction
   3. Industry defaults are always wrong
   4. Defaults are too expensive to use

9. A developer receives an AI suggestion to rename a variable. The developer thinks the original name was clearer. What is the correct workflow?

   1. Immediately ask a senior developer to decide
   2. Override the suggestion and document why the original name was better
   3. Report the AI as broken
   4. Ignore the suggestion since the developer disagrees

10. Why should quality measurement be part of an AI code review policy?

    1. To verify that AI-augmented review catches the types of issues the team wants caught
    2. To make the AI feel accountable
    3. To compare teams against each other
    4. To fire developers who write bad code

11. Which scenario represents the inappropriate use of AI in code review?

    1. AI approves a security-sensitive change without human review
    2. AI flags inconsistent naming across a file
    3. AI suggests adding unit tests for a complex function
    4. AI highlights a potential null pointer exception

12. When should a team use AI for code review, according to best practices?

    1. Only after a human has reviewed everything
    2. For the first-pass review to catch routine issues
    3. Only for completely new projects
    4. Never—humans should review all code

13. What is the danger of allowing AI to substitute for senior engineer judgment on high-stakes changes?

    1. AI will refuse to make decisions
    2. Senior engineers will become unnecessary
    3. The code will be written faster
    4. The AI may not understand context, tradeoffs, and system-wide implications that experienced engineers consider

14. A team implements AI code review but notices velocity has not improved after three months. What should they evaluate?

    1. Reconsider whether the AI tool is actually helping or adding overhead
    2. Hire more junior developers
    3. Disable all human review
    4. Fire the entire engineering team

15. Which of these elements should be documented in an AI code review policy?

    1. Only the AI's error rate
    2. What AI reviews automatically, what requires human review, and how overrides are handled
    3. What the CEO thinks about code quality
    4. The salaries of developers