Handoff From Claude Design To Codex Or Claude Code

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-claude-design-to-code-handoff-creators

1. What is the key distinction between a prototype created by an AI design tool and a production implementation?

   1. Prototypes are faster to write but harder to debug
   2. Prototypes use more colors than production code
   3. A prototype is a demonstration of intent, while production code must be observable, reversible, and safe for end users
   4. Production implementations cannot be created by AI agents

2. Before instructing an AI agent to implement a feature, what should be clearly defined first?

   1. The team's coding standards document
   2. The programming language to use
   3. The job or task to be accomplished
   4. The specific tool name like Codex or Claude Code

3. When handing off a design to an AI implementation agent, what does the lesson recommend regarding scope?

   1. Always include every edge case in the initial handoff
   2. Write the smallest useful scope the agent can finish in one attempt
   3. Scope should be determined by the agent, not the human
   4. Create the largest possible feature set to impress stakeholders

4. After an AI agent produces code from a design, what is the recommended way to evaluate the result?

   1. Run the result as a user would, testing actual functionality
   2. Run the result as a fan of the tool, celebrating its capabilities
   3. Only check if the code compiles without errors
   4. Compare the output to other AI-generated code for quality

5. Which of the following should be included in a proper design-to-code handoff?

   1. A verbal description of what you want
   2. Just the final code files
   3. Only the visual mockups
   4. Tokens, components, states, data, constraints, and acceptance checks

6. What are the three phases the lesson identifies as the reliable path for design-to-code work?

   1. Design intent first, implementation agent second, verification third
   2. Planning, building, fixing
   3. Prototyping, testing, shipping
   4. Design, coding, deployment

7. What question should guide the acceptance criteria for a feature implementation?

   1. What should the user be able to do when this is finished?
   2. Which AI model generated this code?
   3. How many lines of code were written?
   4. What color scheme should be used?

8. When considering data security in a design handoff, what question should be asked?

   1. What is the data retention policy?
   2. Who should have admin access?
   3. What is the database schema?
   4. What data should the app or agent never expose?

9. Why is a rollback path important when implementing AI-generated code?

   1. It makes the code run faster
   2. Rollback paths are not mentioned as important in the lesson
   3. To revert changes if the output is wrong or causes issues
   4. It is required by most programming languages

10. What does the lesson identify as the 'real skill' in working with AI-generated prototypes?

    1. Turning a demo into something observable, reversible, and safe for users
    2. Creating the most complex designs
    3. Writing the most lines of code
    4. Using the newest AI tools available

11. What does the lesson say about solo builders' excitement regarding design-to-code handoff?

    1. Solo builders are more skilled than teams
    2. AI has made design-to-code completely automatic
    3. The reliable path is still structured: design intent first, implementation agent second, verification third
    4. They should avoid using AI tools entirely

12. Which key term from the lesson describes the conditions that must be met for a feature to be considered complete?

    1. States
    2. Tokens
    3. Components
    4. Acceptance criteria

13. What should be examined in the 'diff' when reviewing AI-generated code before sharing?

    1. The number of comments added
    2. The color palette differences
    3. Only the file names that changed
    4. The specific changes made to the codebase

14. When the lesson mentions 'failure path,' what aspect of the code is being addressed?

    1. How the application handles errors and edge cases
    2. The happy path where everything works
    3. The deployment pipeline
    4. The path users take through the UI

15. What warning does the lesson give about tool names, prices, and features?

    1. Tool comparisons are not important
    2. These never change and can be memorized
    3. Verify current docs before relying on exact UI or plan limits
    4. These details are always included in AI outputs