Asking AI to Suggest Ways to Improve Your Code

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-explorers-ai-coding-AI-and-asking-for-feedback-r7a5

1. When is a good time to ask AI for feedback on your code?

   1. Only when it's completely broken
   2. Only before you start writing it
   3. Once your code works — to find ways to make it even better
   4. Never — AI feedback makes code worse

2. What does 'working code isn't the finish line — clean code is' mean?

   1. Your code must use the color green
   2. Making code run is just step one — making it readable and good quality is the real goal
   3. Working code should be deleted and rewritten
   4. Code that runs is always perfect

3. Which of these is a SPECIFIC question you can ask AI for good code feedback?

   1. 'Is this code good?'
   2. 'What three specific things could I improve in this code?'
   3. 'Do you like my code?'
   4. 'Is this code the best code ever?'

4. AI is described as a 'friendly coach' for code feedback. What does that comparison mean?

   1. AI will give you a trophy for writing code
   2. AI spots things to improve without judging you — giving suggestions to make you better
   3. AI will yell at you if your code is bad
   4. AI only tells you what you want to hear

5. What does asking AI 'is anything risky here?' in your code help find?

   1. Whether the code is too long
   2. Potential security problems, bugs, or situations where the code could fail unexpectedly
   3. Whether you typed too fast
   4. Whether the code uses the right colors

6. If AI suggests a change to your code, should you always make the change?

   1. Yes — AI is always right
   2. No — read, understand the reasoning, and decide if it makes sense for YOUR code
   3. No — never use AI feedback at all
   4. Yes — making all changes will guarantee perfect code

7. What are the 'three magic feedback questions' to ask AI after code works?

   1. 'Is this fast? Is this short? Is this mine?'
   2. 'Is there a shorter way? What could break this? What would a pro coder change?'
   3. 'Did you like writing this? Is it perfect? Can I share it?'
   4. 'Is it green? Is it big? Is it fast?'

8. A student writes code that works and asks AI for feedback. AI says 'your variable names are confusing.' The student is surprised — the code works! Why is AI's feedback still valid?

   1. It isn't — if code works, naming doesn't matter
   2. Because confusing names make code hard to read and maintain, even if it runs correctly
   3. Because AI is wrong — variable names never matter
   4. Because the student should start over

9. How is asking AI 'what did I forget?' useful for code feedback?

   1. It helps you remember your password
   2. It prompts AI to check for edge cases, error handling, and features you may have overlooked
   3. It makes AI repeat your code back to you
   4. It finds deleted code

10. AI reviews your code and suggests making it 'shorter.' What should you do before accepting the suggestion?

    1. Accept it immediately — shorter is always better
    2. Refuse it — shorter code is always worse
    3. Understand HOW it would be shorter and verify that the shorter version does the same thing
    4. Ask AI to make it longer instead

11. Why do real professional coders have their code reviewed by teammates — and what does AI review replace or supplement?

    1. Teammates read code for fun; AI review is just for beginners
    2. Team code review catches mistakes the original author is too close to see; AI review gives solo coders the same benefit
    3. Teammates review code to slow down the process
    4. AI review replaces all human review permanently

12. What is the difference between 'code that works' and 'code that is good'?

    1. There is no difference — working code is always good code
    2. Good code is faster but doesn't have to work correctly
    3. Working code produces correct output; good code is also readable, maintainable, and handles unexpected inputs
    4. Good code is written by experts; working code is for beginners

13. AI gives you five improvement suggestions. You implement all five without thinking about any of them. What skill are you missing?

    1. Typing speed
    2. Critical evaluation — the ability to judge whether feedback applies to your specific situation
    3. Trust in AI
    4. Speed of accepting feedback

14. Why is getting code feedback a 'habit great coders build from the start' rather than something they do occasionally?

    1. Because great coders have lots of free time
    2. Because reviewing code regularly builds quality instincts — each review teaches something that improves the NEXT piece of code
    3. Because AI requires frequent use to stay active
    4. Because code must be reviewed daily to prevent bugs from appearing

15. A student's code works perfectly for normal inputs but crashes when someone types nothing. AI catches this in a review. What type of problem is this?

    1. A style problem — the code should be written differently
    2. An edge case — an unusual input the code wasn't designed to handle
    3. A speed problem — the code is too slow
    4. A naming problem — variables are named incorrectly