As developers, we’re shaping how AI fits into our workflows. This carries a lot of responsibility. When working with tools like GitHub Copilot, Cursor, or AI models, it’s important to think beyond what the AI can do, and focus on how we should use it.
There are three key areas to keep in mind: code quality, copyright and licensing, and collaboration.
💻 Code Quality
Even when AI-generated code looks great at first glance, it’s still your responsibility to make sure it’s solid. Think of AI as your pair programmer, not your replacement.
Check, check, check your work. Review every line before merging, you’re the final gatekeeper. Regardless of whether GitHub Copilot or other is the author of your PR, you are still responsible for shipping the code.
Test your code. Does it run as expected? Are edge cases handled?
Keep your code maintainable. Make sure it’s written in your team’s style, easy to read, and simple to debug.
AI can speed things up, but good engineering practices still apply.
⚖️ Copyright and Licensing
AI models like GitHub Copilot were trained on public code. This means ownership and attribution can get tricky. Stay ethical and compliant by being mindful of where code comes from.
Provide context. The more project-specific context you give, the more likely your AI will generate original, relevant code.
Double-check the source. Before using generated code in a closed-source or commercial project, make sure it doesn’t infringe on others’ work.
Respect open source licenses. Check repo licenses and follow their terms.
Document responsibly. Comment when and how AI-generated code was used as this helps with transparency.
🤝 Collaboration and Transparency
Your AI might be an amazing coding buddy, but your best collaborators are still your human teammates. Building trust and clarity around AI use helps everyone work smarter.
Be open about AI use. Let teammates know when you’ve used AI assistance in your work. Ensure you're using AI within your organisation's policies and practices. Remember, tools like GitHub Copilot have business tiers to ensure you're effectively working within your teams' security and compliance.
Review with care. Treat AI-generated code the same way you’d review a teammate’s PR, that is, thoughtfully and critically. Check what model your AI is using. How has that model been trained? What kind of bias might be in the training data?
Encourage inclusivity. Diverse perspectives make for better code and fairer AI outputs. Try prompting your AI to consider different approaches or viewpoints.
Keep learning together. Share insights, report odd outputs, and help shape how your team or community uses AI responsibly.
Remember to be responsible
Responsible AI use isn’t about limiting creativity. Rather, it’s about enhancing it. By combining human judgment with AI’s capabilities, we can write cleaner, fairer, and more innovative code, together.
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