AI doesn’t write my code for me; it helps me think better and move faster. I treat it like a teammate that proposes ideas, spots mistakes, and suggests alternatives—but always with human validation. Over the past year, I’ve integrated AI into my coding workflow, and it’s become an invaluable partner. However, maintaining control over the final output is crucial.
When I use it
- Brainstorming approaches and architecture: When tackling a new feature, I describe the problem to AI and ask for multiple architectural patterns. This helps me consider options I might not have thought of.
- Generating test cases and edge scenarios: AI excels at suggesting comprehensive test suites, including boundary conditions and error paths that ensure robust code.
- Finding inconsistencies during refactors: During code refactoring, AI can scan for potential issues like naming inconsistencies or logic flaws.
- Writing documentation and technical summaries: AI helps draft clear, concise documentation that explains complex code to other developers.
Benefits and risks
Benefits: speed, broader analysis, better communication. Speed comes from rapid ideation; broader analysis from AI’s ability to process vast information; better communication through well-structured explanations.
Risks: generic responses, hallucinations, loss of context. Generic responses can lead to boilerplate code; hallucinations might introduce bugs; loss of context occurs if prompts aren’t specific enough.
My golden rule
First I understand the problem, then I use AI to expand perspectives, and finally I validate with tests, official documentation, and my own metrics. AI is an accelerator, not a replacement for professional judgment. This approach ensures that AI enhances my skills rather than replacing them. By validating everything, I build confidence in the solutions.