Working with AI: A Developer's Guide
AI tools like Claude and GitHub Copilot are transforming how developers work. These aren’t replacements for developer skill—they’re amplifiers that help experienced developers work faster and explore solutions more efficiently.
How We Use AI in Development
Code Generation
AI excels at generating boilerplate code, common patterns, and initial implementations. Instead of typing out repetitive code, we describe what we need and let AI generate the first draft.
What works well:
- CRUD operations and API endpoints
- Unit test scaffolding
- Configuration files
- Documentation templates
Problem Solving
When facing a tricky bug or architectural question, AI serves as a brainstorming partner. We can describe the problem, explore potential solutions, and refine our approach before writing code.
Code Review Assistance
AI can help review code for common issues, suggest improvements, and explain complex logic. It’s not a replacement for human review, but it can catch issues before code reaches that stage.
Documentation
AI generates clear documentation from code, creates README files, and helps write technical specifications. This speeds up a task many developers neglect.
Best Practices
Review Everything
Never commit AI-generated code without reading and understanding it. AI can produce code that looks correct but has subtle bugs or security issues.
Provide Context
The more context you give AI, the better the output. Include relevant code, explain your architecture, and describe constraints. Vague prompts produce vague code.
Iterate
First drafts from AI are rarely perfect. Use AI output as a starting point, then refine through conversation or manual editing.
Protect Sensitive Information
Be cautious about what you share with AI tools. Don’t paste production credentials, sensitive customer data, or proprietary business logic. Use placeholder values and anonymized examples when possible.
Know the Limits
AI doesn’t understand your business context, security requirements, or performance constraints. It can’t make architectural decisions that account for factors it doesn’t know about.
The Future of AI-Assisted Development
AI tools are getting better rapidly. Developers who learn to use them effectively will have a significant productivity advantage. But the fundamentals still matter—understanding algorithms, system design, security, and good engineering practices.
AI is a powerful tool. Use it wisely.
