2026-05-15 · ["vibe coding", "ai development best practices", "workflow", "cursor", "claude code"]

How to Do Vibe Coding Well: 10 Rules of Professional AI Development

The ten rules that separate vibe coding that compounds from vibe coding that collapses. A practical workflow from prompt to ship, plus the work where you should put the keyboard down and type instead.

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How to Do Vibe Coding Well: 10 Rules of Professional AI Development

Most public discussion of vibe coding lives at two extremes: "AI will write all software, type nothing" or "AI is unreliable garbage, type everything." Both are wrong and both are loud. The actually-useful middle ground — how to vibe code professionally, in a way that compounds rather than collapses — is rarely written down.

This article is that middle ground. Ten rules learned the hard way, applicable across Cursor, Claude Code, Aider, and any other agentic coding tool. They separate the engineers who ship faster and better in 2026 from the ones who ship faster and worse.

Rule 1: Commit small, commit often, commit before every agentic action

If you take only one rule from this article, take this one. Every meaningful vibe coding workflow starts with: git add -A && git commit -m "checkpoint before X". Agentic tools can take destructive actions across many files in seconds. The safety net is not the tool's "are you sure?" prompt — those get clicked through. The safety net is git.

When the agent finishes a coherent piece of work, commit that immediately with a real message ("Add login page with Supabase Auth, redirect on success, error state on failure"), not "wip" or "stuff." The discipline of writing the message also forces you to read what was actually changed.

Rule 2: Frame the task in writing before letting the model start

The single biggest determinant of output quality is prompt clarity. Vague prompts produce vague code.

Weak: "add user profile editing."

Strong: "Add a /settings/profile page that lets a logged-in user edit their display name, avatar URL, and bio. Use the existing form components in src/components/forms. Validate display name 1-40 chars and avatar URL is a valid URL. Persist via the existing useUpdateUser hook. Show a success toast on save and inline error on failure. Match the visual style of src/app/settings/account/page.tsx."

The strong prompt takes 90 seconds longer to write and saves 30 minutes of regeneration. Mental model: you are writing a Linear ticket for a competent contractor who has never seen your codebase. The more ambiguity you leave, the more it will be filled with whatever the model thinks is plausible.

Rule 3: Scope every task to fit in a single review

There is a temptation to ask the model to do everything at once. The result is always a diff so large no human can meaningfully review it, which means the bugs you missed live in production. Scope discipline: one coherent feature per task, one reviewable diff per session. If a task would produce more than a few hundred lines, break it into phases — data model first, then API, then UI, then tests. Each phase commits separately. The instinct to "just have the model do it all" produces codebases nobody can refactor. Resist it.

Rule 4: Read every diff. Yes, every diff.

This is the rule most often broken and the one most reliably correlated with collapse. If you cannot read the diff, you cannot debug the code. Reading the diff is not the same as squinting at it. Read for: files touched that should not have been, new dependencies you do not recognize, deletions you were not expecting, embedded secrets, type signatures that affect callers, TODOs in production code, tests modified to pass instead of fixing the underlying code. If a diff is too large to read carefully, the task was scoped wrong — reset, narrow, retry.

Rule 5: Run it before you trust it

The model claiming the code works is not evidence. The build succeeding is not evidence. Tests passing is not evidence (unless the tests are good). The only evidence is running the code through the actual workflow it is supposed to support. For a UI change, click through it. For an API change, hit it with curl. For a migration, run it locally and inspect the schema. 90 seconds spent here saves 90 minutes of "why doesn't this work in production." Make this loop fast — hot reload, fast tests, easy local environment.

Rule 6: Maintain real test coverage on what matters

Tests are how you ship vibe-coded changes confidently three months from now, when neither you nor any model session remembers what the original code did. You do not need 100% coverage — you need coverage on the paths whose failure would matter: auth, payments, data export, API contracts customers integrate with, the two or three primary user journeys that drive your business.

Make tests part of every prompt: "implement X and write a Vitest test that covers the happy path and the main failure mode." Do not save tests for later — later does not come. Models write reasonable tests as they go; they are bad at retroactively testing code that has accumulated mysteries.

Rule 7: Curate context, do not flood it

A common beginner mistake is dumping the entire codebase into context. The result is usually worse — the model becomes confused by irrelevant code. Curate instead: include only files directly relevant to the change, an architecture document for high-level shape, the type definitions or schema files that govern the area being changed. Exclude generated files, lock files, large data fixtures. The model is sharper with less and better context than with everything and noise.

Rule 8: Pick the right model for the task

Not every task needs the strongest model. In 2026 the practical choices are something like:

Task	Suggested model class	Why
Quick refactor in a small file	Mid-tier model	Fast, cheap, more than capable
Complex multi-file feature	Frontier reasoning model	Worth the latency and cost
Generating a UI component	UI-specialized model or v0	Trained for the surface
Writing tests for existing code	Mid-tier model	Pattern matching, not invention
Architectural design discussion	Frontier reasoning model	Reasoning quality matters
Boilerplate, glue, configuration	Cheapest competent model	Money and latency add up

Spending the strongest model on every task wastes money and time. Spending the weakest model on every task wastes everything else. Match the model to the task. Most professional users have at least two configured and switch between them deliberately.

Rule 9: Know your escape hatches

Sometimes the model is wrong and will continue to be wrong. The professional knows when to type instead. Signals it is time: the model has produced three subtly different wrong answers; the problem requires knowledge of a subtle business rule you cannot easily encode in a prompt; the fix is two lines and you can already see them; you are debugging code the model wrote and the only way forward is line-by-line reading; you are in a domain where training data is thin. The fastest engineers in 2026 fluidly switch modes based on what serves the task. Religious commitment to any one mode is the mark of an amateur.

Rule 10: Treat the model like a teammate, not an oracle

The model is a competent collaborator, not a replacement for thinking. Treat it like a smart, fast junior engineer with shallow knowledge of your specific business: be specific, set expectations, review the work, give feedback. When the model is wrong, say so explicitly ("That breaks X because Y. Try again with Z constraint") — vague pushback produces vague retries. Praise specifics — models adjust within a session. Do not pretend the model can read minds; if it matters, write it in the prompt. Do not pretend the model cannot remember; if you have explained the architecture three times in this session, it remembers.

A concrete workflow: prompt to ship in one feature

A professional vibe coding session for "add magic link login alongside the existing email/password flow," end to end:

Step 0 (30s). git status to confirm clean tree. git checkout -b feat/magic-link-login.

Step 1 (3 min). Write the prompt: "Add a magic link login option alongside the existing email/password flow at /login. Use the existing Supabase Auth client at src/lib/supabase.ts. Show two equally weighted options. The magic link form takes only an email and shows a confirmation message after sending. Add tests in src/app/login/__tests__/."

Step 2 (5 min). Send to the agent. Wait. Do not interrupt.

Step 3 (10 min). Read the diff. Verify each change matches intent. Notice the agent inlined state that should use the existing useAuth hook. Note for follow-up.

Step 4 (2 min). Run it in the browser. Confirm both forms appear, are styled correctly, and submit. Check Supabase dashboard.

Step 5 (3 min). Run tests. Inspect them — do they assert real behavior or just rendering? In this case, they assert the Supabase client is called correctly. Good enough.

Step 6-7 (7 min). Follow-up prompt: "Refactor the magic link form to use the existing useAuth hook." Read the new diff, run tests, verify.

Step 8 (1 min). Commit. "Add magic link login to /login, integrated with existing useAuth hook." Open PR, ship.

Total: 31 minutes, with every diff read, browser-verified, and tests included. The same feature manually would take 2-4 hours; vibe-coded carelessly, it would take 90 minutes and ship with at least one bug.

When NOT to vibe code

Vibe coding is the right default for most product engineering work in 2026. It is not the right default for everything. Type the code yourself, with AI assistance only at the edges, when:

• Performance-critical code. Inner loops, real-time systems, anything where every microsecond matters.
• Highly novel algorithms. If the problem is genuinely new, the model produces confident-looking implementations that may be wrong.
• Production-critical security primitives. Cryptography, key management, secure session handling. The model knows patterns; it does not know your threat model.
• Unknown domains. If you cannot evaluate the model's output, you cannot vibe code there.
• Large architectural decisions. "Should we use REST or GraphQL?" produces confident, balanced, useless answers. Architecture is a human decision.
• Foundational library or framework code. The bar for clarity and convention is higher than vibe coding usually achieves alone.

The list is not "anything important." It is "things where the human cost of getting it wrong outweighs the speed benefit of generating it."

The mindset shift

The deepest change vibe coding requires is psychological. Engineers were trained to think of typing code as the work. Vibe coding redefines the work as framing, reviewing, judging, integrating, testing — typing is a minority of the work. The craft has not been devalued; it has migrated. Knowing what to ask for, what to accept, what to reject, what tests matter, what shortcuts will hurt you in six months — that is the craft now, and it has never been more valuable.

If you are building a SaaS and need a marketing surface that compounds — a landing page that converts, looks premium, and ships in days — that is what we do at Launq. AI for the boring parts; senior humans for the parts that decide whether your page works. You can vibe code the back office. The front door deserves more.

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FAQ

What is the most important rule of vibe coding? Commit small and commit often, before every agentic action. Git is the safety net that makes every other mistake recoverable. Without it, a single bad agent run can destroy hours of work and hide the damage in a way that is impossible to untangle.

How specific should my prompts be? Specific enough that a competent contractor who had never seen your codebase could execute them without asking questions. Name the files, the existing patterns to follow, the validation rules, the success and failure states. Vague prompts produce vague code; the cost of writing 90 seconds more prompt is repaid 20 times over in fewer regenerations.

Should I read every diff the AI produces? Yes. If you cannot read the diff, you cannot debug the code, and you do not understand your own codebase. If a diff is too large to read, the task was scoped wrong — break it into smaller chunks and try again. This single discipline separates compounding vibe coding from collapsing vibe coding.

Which model should I use for vibe coding? Match the model to the task. Use a frontier reasoning model for complex multi-file features and architectural work. Use a faster mid-tier model for simple refactors, boilerplate, and tests. Use UI-specialized tools like v0 for component generation. Spending the strongest model on every task wastes money; spending the weakest on every task wastes correctness.

When should I stop vibe coding and just type? When the model has produced three subtly wrong answers, when the fix is two lines you can already see, when you are debugging code the model wrote and the only way forward is reading it line by line, or when you are in a domain (custom DSLs, niche frameworks, embedded environments) where the model's training data is thin.

Do tests matter when vibe coding? More than when manually coding. The model has no memory of code it wrote three months ago, so when bugs appear, you (or the model) must read the code cold. Tests are the documentation of intent. Make tests part of every prompt; do not save them for "later."

Can I vibe code an entire production SaaS? Yes — many founders in 2026 do exactly this, with the discipline described above. The risks are real but manageable: small commits, real test coverage on critical paths, security review of authenticated endpoints, and a willingness to type when the model is wrong. The risks become unmanageable only when those disciplines are skipped.

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— Romain at Launq