After using Codex on real project work, I noticed that the best results did not come from asking AI to build a feature immediately.
In real projects, I find AI more useful as a technical reviewer before implementation. When I receive a task, I often ask AI to inspect whether the change is possible, which files or areas may be involved, and what side effects deserve attention.
After that, I write the intended flow myself: what should happen, where the logic should live, what data is needed, and which cases must be covered. Then I ask AI to review that flow before any code is changed.
This small step changes the workflow. AI becomes less of a code generator and more of a second reviewer that helps me think before touching the codebase.
A good AI-assisted development workflow still needs human ownership: inspect the task, understand the risks, write the implementation flow, review it, implement carefully, and verify the result with real project behavior. AI can make us faster, but the responsibility still stays with the developer.