6. Frequently Asked Questions

Q1: Can I use AI-generated code directly?

A: No, not directly. It needs review and testing.

AI-generated code may have the following problems:

• logical errors or poor handling of edge cases
• failure to match the project's coding conventions
• security risks
• insufficient performance optimization

You need to:

• carefully read the generated code
• understand its logic
• test different scenarios
• confirm that it follows the project conventions

Q2: What if AI misunderstands what I mean?

A: Correct it in time and describe the requirement again.

That's not what I meant. What I mean is...
This understanding is incorrect. It should be...
Let me describe the requirement again...

If it is still wrong after several corrections, you can:

• provide more context
• give specific code examples
• split the task into smaller pieces

Q3: What if I run into something AI cannot solve?

A: AI is not all-powerful. Some problems still need you to solve them yourself.

Problems AI may not be able to solve:

• very new technologies (AI knowledge has a cutoff date)
• business logic unique to your team
• problems that require access to external systems
• complex performance optimization issues

At that point, you need to:

• read the official documentation
• search for related solutions
• ask experienced teammates
• ask in the community

Q4: How do I judge whether AI's suggestion is reasonable?

A: Use your own experience and knowledge to judge it.

Evaluation criteria:

• whether it follows best practices
• whether it considers edge cases
• whether there are potential security risks
• whether it fits the project's tech stack
• whether performance is acceptable

If you are not sure, you can:

• ask AI to explain why it suggests that approach
• ask for alternative solutions
• consult team members

Q5: How should a team use AI in collaboration?

A: Establish shared conventions and a shared knowledge base.

Recommendations for team collaboration:

• share the project's CLAUDE.md configuration
• unify code conventions and style
• record solutions to common problems
• regularly share useful prompts
• check AI-generated code during code review

Q6: How do I avoid becoming overly dependent on AI?

A: Keep learning and thinking. AI is an assistant, not a replacement.

Recommendations:

• understand AI-generated code instead of copying it blindly
• actively learn concepts you do not understand
• regularly review foundational knowledge
• try solving problems yourself first, then use AI to verify
• participate in code review to learn from others' experience

7. Summary

Through this chapter, you have now mastered:

1. AI's capability boundaries: understand what AI is good at and not good at, and build the right collaboration model
2. Project-type strategies: different development strategies for brand-new projects, mature projects, rapid prototypes, and maintenance projects
3. Common task workflows: complete workflows for new feature development, bug fixing, code refactoring, and code review
4. Project knowledge base: learn how to build project documentation so AI can understand your project better
5. Collaboration techniques: practical ways to improve AI collaboration efficiency

Key takeaways:

• Clear division of roles: you make decisions and ensure quality, AI handles execution and assistance
• Clear communication: be specific and do one thing at a time
• Verify promptly: do not trust blindly, test and verify
• Keep learning: understand AI's capability boundaries and continuously improve the collaboration model

Remember: AI is a tool, not a replacement. It can make you more efficient, but the final code quality still depends on your judgment. Start with simple tasks and gradually build trust. You will find that AI can save you a lot of time and let you focus on more valuable work.

💡 Next step

In the next chapter, we will learn how to use AI for code review and quality assurance to ensure code maintainability and security.