The developer describes a shift in their workflow when using the Codex AI model for backend tasks. Instead of writing the initial implementation manually, the developer now generates the first version of the code using the AI, then reviews the diff, tests the system behavior, identifies missing context, and refines the task for the next iteration.
In a recent integration task involving multiple services, RabbitMQ, and Redis, Codex successfully generated a functional publisher and consumer. However, the implementation was incomplete because it failed to ensure data reached the final consumer in the chain. The developer had to provide additional context to the AI to complete the end-to-end flow.
The author emphasizes that generating code is only the first step. The engineering challenge lies in verifying the entire system flow rather than just the isolated code snippet provided by the AI. The developer notes that relying solely on a diff review is insufficient for ensuring system-wide correctness.
While the developer acknowledges that AI can assist with writing tests and analyzing call chains, the responsibility for system integrity remains with the human engineer. The process of verifying results against the actual system behavior is described as a core engineering task, regardless of whether AI is used in the development cycle.
