Iterative AI code generation places model output inside a feedback cycle. The system generates or edits code, executes or renders it, observes tests and visible behavior, then uses that evidence to plan the next change.
Iteration can repair mistakes and extend a working prototype, but it can also accumulate fragile patches or drift from requirements. Reliable workflows preserve state, use explicit checks, limit side effects, and periodically reassess the overall architecture.
ELI5
Iterative AI code generation means improving generated code through several focused passes. The first result creates something to inspect, and each later request responds to a specific problem or missing requirement.
For example, an agent can first build a profile card, then fix mobile spacing after viewing it, add keyboard focus after an accessibility check, and finally refine animation without regenerating the entire application.
What feedback can guide iterative code generation?
Tests, compiler errors, runtime logs, screenshots, rendered scenes, user review, performance measurements, static analysis, and requirement checks can guide revisions.
What risks can repeated AI edits create?
They can accumulate duplicated logic, fragile patches, hidden regressions, architectural drift, unnecessary dependencies, and fixes that optimize one symptom while harming another area.
