Use A Second Model For Review

End-of-lesson check

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

1. What is the primary advantage of having one AI model write code while a second AI model reviews it?

   1. The second model guarantees the code will work in production
   2. The first model will write better code when it knows it's being watched
   3. The second model can catch security vulnerabilities the first model overlooked
   4. The first model learns faster from receiving feedback

2. When using an AI coding agent, what does 'name the job before naming the tool' recommend?

   1. Define what problem you need solved before selecting which AI model to use
   2. Choose the most expensive tool available for important tasks
   3. Always give your AI assistant a human name to improve communication
   4. List the file names you need modified before starting the task

3. Why does the lesson recommend writing the 'smallest useful scope' when assigning tasks to an AI coding agent?

   1. Smaller scopes reduce the chance of unintended changes and make review easier
   2. The smallest scope is the only scope that AI can handle correctly
   3. AI models perform better on small tasks because they have less context
   4. Large code changes are always rejected by version control systems

4. What does it mean to 'run the result as a user, not as a fan of the tool'?

   1. Trust that the AI tool produced correct output without questioning it
   2. Share the code with other developers immediately after generation
   3. Use the same AI tool to verify its own output
   4. Test the code by interacting with it the way an actual end user would

5. What is the second model primarily checking for during a code review with dual AI models?

   1. The exact same things the first model already checked
   2. Missed tests, security gaps, and accidental scope creep
   3. Network connectivity issues and server uptime
   4. Syntax errors, variable typos, and missing semicolons

6. What does a good rollback path ensure for AI-generated changes?

   1. Rollback is unnecessary when using AI coding assistants
   2. You can revert to the previous state if the output causes problems
   3. Changes can only be rolled back by the original developer
   4. The AI will automatically fix any bugs it introduces

7. The lesson states that AI can quickly produce a working demo. What distinguishes a production-ready feature from just a demo?

   1. Faster execution speed and lower memory usage
   2. Written in a compiled language rather than interpreted
   3. Observable behavior, reversibility, and safety for end users
   4. More complex algorithms and advanced features

8. Why does the lesson describe disagreement between the writing model and review model as 'where bugs hide'?

   1. The review model intentionally introduces bugs
   2. Bugs cannot exist when two models agree
   3. Disagreements often reveal assumptions or oversights in the code
   4. Bugs only exist when both models are wrong

9. What type of test proves that an AI-generated code change actually works?

   1. A test that checks every line of code was executed
   2. A test that compares the code to other similar projects
   3. A test that exercises the new functionality as a user would
   4. A test that verifies the AI's original prompt was accurate

10. What is 'accidental scope creep' in the context of AI code generation?

    1. When the AI adds unintended features beyond what was requested
    2. When the project timeline unexpectedly extends
    3. When multiple developers work on the same codebase
    4. When the code becomes too complex to understand

11. What makes code 'observable' in the context of AI-assisted development?

    1. You can see what the code is doing through logs, metrics, or user feedback
    2. The AI model can read its own output
    3. The code runs in a transparent container
    4. The code is open source and publicly available

12. The lesson mentions that tool names, prices, and features change fast. What does this imply about following tutorials?

    1. You should avoid learning new tools altogether
    2. You should never use AI coding tools
    3. You should only use the most expensive tools
    4. You should verify current documentation rather than relying on older guides

13. Why might running AI-generated code as a 'fan of the tool' lead to problems?

    1. It prevents the code from having security issues
    2. It ensures the AI will fix all bugs automatically
    3. It makes the code run faster due to positive thinking
    4. It causes you to overlook errors because you trust the AI incorrectly

14. What is the relationship between code review and verification in AI-assisted coding?

    1. Review helps verify that the AI's output meets requirements and is safe
    2. Review and verification are the same thing
    3. Verification should happen before review
    4. Review is unnecessary when using AI

15. What skill does the lesson identify as more important than generating a working demo with AI?

    1. Transforming the demo into something observable, reversible, and safe for users
    2. Memorizing the exact prompts that produced good results
    3. Writing more complex demos that impress other developers
    4. Convincing management that AI-generated code is perfect