How to produce high-quality code with AI coding assistants

AI coding assistants are a double-edged sword. On the one hand, they have the potential to improve your productivity and automate repetitive tasks. But used incorrectly, they can slow you down and introduce quality issues that take time and effort to resolve. 

Here’s a quick and easy guide on how to mitigate these risks when using AI coding assistants like GitHub Copilot, Cursor, Cline, and others:

Common pitfalls in AI generated code

• Loss of context or tooling hallucinations

• Reduced maintainability 

• Increased technical debt

• New security and vulnerability risks

• Misalignment with team style or standards

Key strategies to reduce AI coding quality issues

1. Keep human oversight and code reviews

Your AI-generated code should undergo the same level of scrutiny as human-written code. Peer reviews help identify context-specific issues, logical errors, and adherence to project standards that AI tools might overlook. Additionally, enforce standard pull-request workflows. Avoid practices like vibe coding in favor of AI-assisted development.

2. Provide adequate context for prompts

AI assistants perform better when given clear and detailed prompts. You should specify the intended function, language, style guides, and system constraints to improve the relevance and quality of AI-generated code. 

3. Use encapsulation

Keep generated code isolated in well-defined modules or functions, making it easier to test, refactor, or replace as needed. This also reduces spread of potential issues throughout the codebase.

4. Align AI use with actual use cases

When you have AI as a hammer, everything looks like a nail, but it is not the solution for every problem. Define objectives and appropriate use cases. For example, AI excels at boilerplate code, test generation, and documentation, but less so at large architectural decisions.

5. Implement automated quality gates

Completing your human review, you can implement automated tools that act as quality control for your AI-generated code, checking for maintainability, code smells, and technical debt before they hit production.

6. Use secure coding practices

AI coding assistants can inadvertently introduce security vulnerabilities, such as improper input validation or insecure patterns. You should:

• Conduct security-specific checks on AI-generated code.

• Limit exposure of sensitive data during AI interactions.

• Regularly update dependencies and vet third-party code before integration.

7. Monitor and measure the impact of your AI-generated code

There are times when AI is slowing you down, not speeding you up, and this can be hard to identify yourself. You should have objective ways to measure the code quality, velocity, and error rates of your AI-powered workflows to make sure it’s producing better results. You also want to do this to detect any patterns of declining quality.

8. Maintain your knowledge and skill growth

You should avoid over-reliance on AI, as getting it to write too much of the code can result in you failing to develop essential skills. Intermittently solve problems without AI to deepen your understanding. Also, make sure you are always able to understand what the AI tool is actually producing.

Continue to educate yourself on the strengths and weaknesses of the AI tools you are using, and those currently available, as well as better workflows you can use to get the most out of them. It’s an ever evolving space, so you’ll never be short of something to catch up on!

Conclusion

AI coding assistants are tools that can enhance productivity and accelerate development, but without proper use, they can end up having the reverse effect. The strategies listed above can help you make the most of AI tools while mitigating potential quality and security issues.