How I Use AI to Automate My Development Workflow

AI didn't replace my workflow — it amplified it. Here's exactly how I use AI tools every day as a full-stack developer.

My AI Stack

• ChatGPT — Boilerplate generation, API documentation, regex patterns
• Gemini — Code review suggestions, explaining legacy code
• Custom Prompts — Test case generation, commit messages, PR descriptions

The Biggest Win: Automated Testing

The biggest productivity gain came from automating test generation. I wrote a prompt template that takes a function signature and generates test cases:

Given this function signature: [function]
Generate Jest test cases covering:
- Happy path
- Edge cases
- Error handling
- Boundary conditions

This saves me roughly 2 hours per sprint. But here's the catch — you still need to review every test. AI generates reasonable tests, but it misses business logic edge cases that only a human who understands the domain would catch.

Code Review Automation

• Unused imports
• Inconsistent naming
• Potential null pointer issues
• Missing error handling

This means my team reviews focus on architecture and logic, not formatting.

Documentation Generation

Writing docs is my least favorite task. Now I use AI to generate first drafts: 1. Write the code 2. Feed it to ChatGPT with "Write developer documentation for this function" 3. Edit the output for accuracy and tone

What used to take 30 minutes now takes 5.

The Rules I Follow

1. Never commit AI-generated code without reviewing it. It will look right and be subtly wrong.
2. Use AI for first drafts, not final products. The 80/20 rule applies — AI gets you 80% there, you finish the last 20%.
3. Treat AI like a junior developer. It's fast and eager, but it needs supervision.

The Future

I'm experimenting with building custom AI agents that integrate into our CI/CD pipeline — automatically suggesting fixes for failed tests, generating deployment notes, and even preparing rollback plans. The potential is enormous.