Refactor In Small Slices

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coder-small-refactor-slices-creators

1. When using an AI coding agent for refactoring, what is the recommended first step before naming the tool you will use?

   1. Check the agent's pricing model and rate limits
   2. Search documentation for the newest agent features
   3. Define the specific job or task that needs to be accomplished
   4. Review the existing test suite coverage

2. After an AI agent completes a refactor, how should the developer evaluate the result?

   1. Read through the code and compliment the elegant solution
   2. Run only the unit tests to confirm syntax is correct
   3. Show the diff to other developers for admiration
   4. Run the application as an actual user would, testing real workflows

3. Before sharing an AI-generated refactor with a team, which three things should be inspected?

   1. The AI's confidence score, token usage, and API latency
   2. The file size, number of functions, and dependency versions
   3. The diff (code changes), data access patterns, and failure paths
   4. The color scheme, variable naming, and comment density

4. What makes large, vague refactors particularly risky when working with AI coding agents?

   1. AI agents cannot parse files larger than 1MB
   2. Large refactors consume too many API tokens
   3. Agents tend to drift from the original intent and humans lose track of what changed
   4. Vague requirements trigger copyright infringement

5. When planning an AI-assisted refactor, which question helps define what data the system must protect?

   1. Which team member should review the code?
   2. How many files need to be changed?
   3. What programming language should be used?
   4. What data should the app or agent never expose?

6. What is the purpose of defining a rollback path before starting an AI refactor?

   1. To show stakeholders that the project has version control
   2. To allow the agent to undo its own changes automatically
   3. To comply with legal audit requirements
   4. To be able to revert to a working state if the agent's output introduces bugs

7. According to the principles in this material, what separates a beginner from an expert when using AI for coding?

   1. The expert can write better prompts than beginners
   2. The expert uses AI more frequently throughout the day
   3. The expert knows which AI model has the most parameters
   4. The expert can turn a working demo into something observable, reversible, and safe for others to use

8. What does the term 'module boundary' refer to in the context of AI-assisted refactoring?

   1. The time limit for how long an agent can run before timing out
   2. The interface between different code components where data and behavior are exchanged
   3. The physical location of files in a project directory
   4. The limit on how many lines of code an AI can generate in one response

9. When the lesson mentions keeping behavior 'stable' during refactoring, what does it primarily mean?

   1. The code should maintain the same variable names and comments
   2. The refactored code should have the same performance characteristics
   3. The project should keep the same folder structure
   4. The external functionality users depend on should continue to work correctly

10. What test proves that an AI refactor actually works as intended?

    1. A test that counts the number of files modified
    2. A test that verifies the specific change achieves its intended outcome
    3. A test that checks all existing functionality still passes
    4. A test that measures the AI's response time

11. Why do experienced developers on forums like Reddit recommend breaking AI refactoring into small tasks?

    1. Small, focused tasks give agents clear targets and make it easier to verify correct behavior
    2. Small tasks allow the AI to learn from its mistakes
    3. Small tasks use fewer API tokens overall
    4. Small tasks prevent the AI from becoming confused about context

12. What question should be answered to determine if a refactoring task is complete?

    1. How many lines of code were changed?
    2. What should the user be able to do when this is finished?
    3. When was the task started?
    4. Which AI model generated the solution?

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

    1. The project has comprehensive documentation
    2. The code runs without any errors
    3. The system produces clear outputs and logs that allow developers to understand what is happening
    4. The code is written in a widely-used programming language

14. When evaluating an AI refactor, why is examining the 'diff' important?

    1. It shows exactly what changed, allowing review of each modification
    2. It proves the code compiles without syntax errors
    3. It displays the AI's confidence in its changes
    4. It reveals how many tests will need updating

15. What quality makes code 'safe enough for another person to use' after an AI refactor?

    1. The code has proper error handling, clear boundaries, and can be rolled back if needed
    2. The code passes all automated tests
    3. The code has detailed comments explaining every line
    4. The code uses popular frameworks and libraries