AI assists with architecture, implementation, testing, code review, CI/CD, deployment, and day-to-day development support by acting like an additional colleague who can help move tasks forward faster while reducing unnecessary context switching between planning, coding, review, and documentation work.
The developer must provide context, orchestrate the workflow, judge the quality of the output, review the code, and decide whether the result is trustworthy and suitable for the project.
AI drafts functions, components, and boilerplate while the developer iterates on the implementation inside the IDE.
GitHub Copilot / CursorAI proposes test cases and starter test code for new or refactored functionality so engineers can cover behavior faster.
GitHub Copilot / JetBrains AI AssistantAI scans diffs for bugs, style issues, and risky changes before or during human review to speed up feedback cycles.
CodeRabbit / GitHub CopilotI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
AI didn't make me a 10x developer
It removed hours of boring work.
Now I use it to:
• Design APIs
• Generate boilerplate
• Review code
• Find edge cases
• Write tests
• Create documentation
The biggest productivity gain isn't coding.
It's reducing context switching.
How are you using AI in development?