learn-ac-iterate

Iterative prompting refines AI-generated TypeScript code through a four-phase sequence of skeleton, implementation, testing, and refactoring. This staged feedback catches design issues early and reduces rework compared to single-prompt attempts.

The best way to structure AI code review checkpoints is by applying a four-phase sequence that reviews structure, signatures, persistence, tests, and error handling incrementally. This ensures quality control at each stage of CLI tool development.

Stepwise refinement produces higher quality software plans than one-shot prompting by applying incremental correction through staged feedback. You can compare an iterated TODO list CLI implementation against a one-shot prompt to see the quality difference directly.

You need git-based comparison and JSON file persistence validation to complete the refactoring phase of the AI code workflow. Git allows you to track incremental corrections and verify that persistence logic functions correctly across iterations.

You can use the iterative AI workflow for coding exercises, prompt iteration, and other small implementation workflows beyond CLI tools. Any project benefiting from checkpointed review across skeleton, implementation, testing, and refactoring phases is suitable.

Single-prompt AI code generation requires rework because it attempts to produce a perfect result immediately without checkpointed review. Staged feedback through stepwise refinement catches design issues earlier and makes AI collaboration more predictable.