The Iterative Vibecoding Workflow: Small Steps, Fast Feedback

Why this matters

The seductive thing about Cursor or Claude Code is the one-shot prompt. "Build me a Stripe checkout flow." Twenty minutes later you have something that looks done — until you try to use it. The iterative vibecoding workflow is the opposite move: small prompts, tight loops, fast feedback. Every loop ends in a working state you could ship.

If you've ever found yourself rage-deleting an entire AI-generated module because you can't tell where the bug is, you've felt the cost of skipping iteration. Short loops aren't slower. They're the only thing that makes vibecoding work at scale.

The setup

You need three things before you start:

• A clean working tree (git status is empty).
• A clear goal you can describe in one sentence.
• A way to run the thing after every change — npm run dev, a test, a curl command. Anything.

Step 1: Slice the task into 20-minute units

If the smallest unit you can describe is "build the auth flow," that's too big. Slice it: "add the sign-in page UI," then "wire it to the Supabase magic-link call," then "redirect on success." Each slice should fit in one prompt and one verification.

Good slice sizes:

• Adds or changes 1-3 files.
• Has a single observable outcome ("the form submits and shows a success toast").
• Could be reverted with one git revert without ripping out something else.

Step 2: Prompt → run → commit

The loop is three moves, every time:

1. Prompt. Describe the slice with the file paths it should touch and the constraint you care about. Don't dump unrelated context.
2. Run. The moment the AI says "done," you run the code. Not read the diff — run it. Hit the page. Call the endpoint.
3. Commit. If it works, commit before doing anything else. If it doesn't, decide: patch with one more prompt, or revert.

# After every working slice
git add -A
git commit -m "feat(auth): add magic-link sign-in form"

The commit history becomes your debug ladder. When something breaks two prompts later, you know exactly where it stopped working.

Step 3: Revert before you patch

The single biggest failure mode in vibecoding is the "third patch death spiral": you prompt a fix, it half-works, you prompt another fix, now there are two new bugs, you prompt again, the AI rewrites a function unrelated to the bug, and now nothing works.

The rule: if the second patch doesn't fix it, revert. Go back to the last green commit. Re-prompt with what you've learned. This feels wasteful. It is the cheapest path 90% of the time.

# When you're three patches deep and lost
git reset --hard HEAD~3
# Re-prompt with what you now know about the failure mode

If you've been debugging AI-generated code for an hour without progress, that's the signal. Revert.

Step 4: Pin context, don't dump it

As the codebase grows, the temptation is to give the AI more context — paste more files, expand the chat. Resist. The model is better with a focused window than a flooded one.

Use context management techniques: pin the 2-3 files the slice touches, reference others by path only, and let your .cursorrules or CLAUDE.md carry the long-lived rules. A pinned, lean context produces tighter diffs and lets you actually review what changed.

Common mistakes

• Prompting for whole features at once — A "build the dashboard" prompt that returns 14 new files cannot be reviewed, debugged, or partially reverted. Slice first.
• Skipping the run step — Reading a diff and saying "looks right" is how silent bugs ship. Run the code every loop.
• Patching past the third try — The AI is now confused by its own previous edits. Revert and restart with what you learned.
• Letting the chat get stale — A 200-message thread carries baggage. After a feature ships, start a new chat. Less context-window pollution, fewer hallucinated assumptions.
• Ignoring git — No commits between prompts means no rollback. You are coding without a parachute.

What's next

Once the loop is muscle memory, layer on a real spec discipline — see how to write a PRD an AI can execute — so each slice has clear acceptance criteria. Pair the iterative loop with a tighter prompting pattern playbook and your output rate jumps without the rework tax.