AI coding tools are most useful when they are part of a disciplined development workflow. The goal is not to let the model write unchecked code. The goal is to use AI to clarify requirements, explore implementation options, draft changes, write tests, and review risk before a pull request is merged.
Step 1: Turn the request into an engineering brief
Start by asking the AI to restate the task in plain language. Include the expected behavior, affected files, non-goals, edge cases, and test expectations. This prevents the model from rushing into code before the problem is understood.
A good brief answers four questions: what should change, what should stay the same, how users will notice the change, and how we will verify it. If any answer is vague, fix the brief before editing code.
Step 2: Ask for a small implementation plan
The best AI coding sessions use short plans. Ask for the smallest safe implementation path, likely files to inspect, and possible failure points. Do not accept a plan that proposes broad refactors unless the task truly requires them.
Step 3: Implement in narrow changes
Use AI to make focused edits, then inspect the diff yourself. Look for over-abstraction, changes outside the requested area, missing error handling, and assumptions about data shape. AI is helpful, but it can still produce confident code that does not match the system.
Step 4: Add tests before polishing
Ask the AI to identify the most important tests for the behavior. Good tests cover the normal path, at least one edge case, and one regression risk. If the code touches shared logic, add a broader test. If it touches UI, test the visible state and user flow.
Step 5: Use AI as a reviewer
Before opening a pull request, ask the model to review the diff as a skeptical senior engineer. The review prompt should ask for bugs, missing tests, security issues, performance risks, and unclear naming. Treat the answer as a checklist, not as approval.
Pull request checklist
- The task is restated clearly.
- The change is scoped to relevant files.
- Tests cover behavior and edge cases.
- The diff has been reviewed for hidden assumptions.
- The pull request explains user impact and verification.
This workflow turns AI from a code generator into a development partner. The developer remains responsible for judgment, but the model accelerates the boring and repetitive parts of the process.
Next steps: read the Writoria Start Here guide, explore related AI workflow playbooks, or browse reusable AI templates.