The Vibe Coding Method: 7 Practical Tips and the Art of Writing Prompts

You did your first vibe coding attempt and the code ran. Congratulations. Now the real question: does the same code still work six months from now? Does adding a new feature break the old one? Can another developer read this code? In this article I share seven concrete methods to go from "works" to "keeps working." These are practices I have applied to my own projects and taught to my students — they work again and again.

AI-driven development is not "writing prompts". It is knowing how to make decisions, how to give small tasks, and how to audit. The seven methods below target exactly that.

1. Document the architecture decision, then hand it to the model

Where the AI stumbles the most is decisions that were never spelled out. "Where does the data go?", "how much do we trust the user?", "which value comes from the server?" — left to the model, each file gets a different answer. The code works, but it is inconsistent.

The fix: a decision document. A single file at the project root, ARCHITECTURE.md or DECISIONS.md. Inside:

• Trust boundary. Which values come from the client, which are computed on the server?
• Data ownership. Which table holds which field, which service holds which logic?
• Product rules. "A user can do X at most N times per day", "if payment fails, then Y..."
• Technology choices. "PHP + MySQL, because...", "Firebase only for auth and notifications."

This document can be 50-100 lines. You write it. Then every time you give the model a task, remind it to "write to the decision document". The consistency of the output goes up noticeably.

Concrete example

In the decision document for LevelUpStudy there is this single line:

No value that drives the game economy ever comes from the client. Duration = min(server delta, planned duration, client-reported duration).

This single line made the model give the right answer for every duration-related prompt. Without it, every file could have produced a different formula.

2. Give small tasks, keep the big goal yours

A "build me the app" prompt gives the model a target, not a goal. The model will produce the shortest path to "it works" — which is not the path you want. The output of a "build the whole app" prompt is usually 500 lines, half of it disconnected, half of it duplicated.

Instead, give atomic tasks. Example:

❌ "Build me an e-commerce app"

✅ "Write the add-to-cart endpoint. Single transaction. If the same user adds the same product twice within 100ms, only one stock decrement happens. On error, return 422."

That one sentence contains five decisions: which endpoint, transaction rule, idempotency rule, behavior boundary, error response. The model writes code to those five decisions. You can test each one independently.

Atomic tasks also make debugging easier. "Adding to cart does not work" becomes "this endpoint returns 422 when it should return 500".

3. Add a checklist to the prompt

One of my favorite things about the model: if you tell it what to check, it actually checks. Instead of "write this function", try "write this function, then verify these 5 items."

Example:

"Write this function. Then verify:

1. Edge case: if the input is null, what does it return?
2. Given the same input twice, does it give the same output?
3. On error, does it throw, or silently swallow?
4. Does it have side effects (database, file, network)?
5. Is it testable (pure function, dependencies injectable)?"

The model appends the checklist to the output. You go through it one by one. If there is an issue, "fix that item" — second iteration. In two or three rounds you end up with very solid code.

4. Keep tests in sync, build a shield

Vibe coding's biggest safety net is tests. Move critical values (time, score, identity, permissions) into pure functions and protect them with tests. They stop the model from breaking the same spot in the future.

A pure function: no outside dependencies, same output for the same input, no side effects. calculateScore(time, level) -> number is pure. updateUserInDatabase(userId, score) is not, because it touches the database.

Rule: move business-rule logic into pure functions. Money, score, time, permissions, ranking — all can be pure. The tests you write for these functions stop the model from quietly changing the same spot later.

My practice:

1. When adding a new feature, I write the business rule as a pure function first.
2. I write a test for the pure function.
3. I tell the model "use this pure function to write the endpoint".
4. After the endpoint is written I ask "do the tests still pass?"

These four steps prevent most "works but calculates wrong" bugs.

5. Make audit questions routine

After every phase, ask the model three audit questions:

1. "Any dead code in this file?"
   The model lists unreachable functions, dead code, stale parameters. You delete them.
2. "Any duplicated functions?"
   The model lists functions doing similar work under different names. You decide whether to merge.
3. "Any magic strings?"
   The model suggests moving hard-coded strings ("Pen", "TR", "YKS") into constants.

Asking these three at the end of every sprint stops "spaghetti that runs". The model does not audit its own work; if you do not ask, the bad code keeps coming.

6. Treat refactor as a separate phase

A common vibe coding mistake: letting the model sneak in "while you are at it, fix this part too" during a new feature. Result: one commit mixes new feature + refactor + bug fix, hard to roll back.

Instead:

• Feature phase: only new work.
• Refactor phase: only cleanup.
• Test phase: only tests.

After each phase, ask "what did we do, what did we learn, what's next?" The separation raises quality, and it also means you can revert a single commit when something breaks.

7. Keep a prompt log, extract recurring patterns

While doing vibe coding you end up writing the same prompts in different places. "Understand this file, then write Y function following rule X" kind of patterns. Note them down.

How I do it in practice:

• A note file (PROMPT_PATTERNS.md) where I keep the best-working prompt patterns.
• Next to each pattern, when it works and when it does not.
• When I open a new project, I pick the right pattern and adapt it.

This habit reduces the load of "writing prompts from scratch every time". It also trains your prompt-writing muscle.

Bonus: five things that break the prompt itself

Even with all seven methods in place, how you write the prompt can still ruin the result. The five I see most often:

1. Not writing the acceptance criterion.
   The gap between "write a login endpoint" and "write a login endpoint; 401 on a wrong password, 429 after five failed attempts" is exactly one round trip. Skip the criterion and you pay for that round trip later.
2. Describing the context instead of pasting it.
   Do not say "we have a User model" — paste the definition. When the model guesses field names, the code compiles and the data does not land.
3. Not asking for the reasoning.
   Say "write it and explain why you wrote it that way" rather than just "write it". You catch the wrong assumption before you start reading the code.
4. Making the model choose.
   "Which library should I use?" is a bad question; "which of these three libraries fits these constraints?" is a good one. You supply the options, the model supplies the argument.
5. Expecting a perfect prompt on the first try.
   Good prompts are iterative. Read the first answer, find where the model misread you, and narrow only that sentence. Rewriting from scratch is almost always slower.

Is vibe coding actually sustainable?

If you apply the seven methods above, yes. Otherwise: a codebase that runs but cannot be maintained, growing debt with every feature, the "whoever wrote this should delete it" point six months in.

What sustainability needs is rhythm:

1. Decide.
2. Give small tasks.
3. Keep tests in sync.
4. Audit.
5. Keep refactor separate.
6. Keep a prompt log.

This rhythm combines the model's productivity with the developer's discipline. This is how vibe coding works in practice: you gain speed, and you can still open the code months later and understand it.

Final thought

Vibe coding is a method, not a tool. Tools will change (tomorrow some other model will take the lead), but the method stays: decide, give small tasks, keep tests in sync, audit.

For a more compact starting point, see the Vibe Coding guide page. For a hands-on tool comparison, see Vibe Coding tools compared.

For the story of building a real app with this method, see Shipping an App Store App with Vibe Coding — the LevelUpStudy story.

Frequently Asked Questions

How do I write a vibe coding prompt?

A prompt has three parts: goal (what to do), constraint (which rules to follow), acceptance (when is it "done"). Example: "Write the add-to-cart endpoint. Single transaction, idempotent. On error return 422."

How do I keep AI-written code quality high?

Move critical business rules into pure functions protected by tests. At the end of every phase, audit for dead code, duplicated functions, and magic strings. Keep refactor in a separate phase from new features.

What is the most important vibe coding tip?

Document the decisions. Without an architectural decision document every prompt to the model is incomplete. This single habit noticeably improves output quality.

How do I debug with vibe coding?

Paste the error as-is, then prompt "find the code that produces this error, explain why, write the fixed version". Never commit without reading the model's explanation; sometimes a "fix" just hides the bug.

Where can I learn vibe coding in English?

Start with this article and the Vibe Coding guide page. The richest English resources are Anthropic's blog, OpenAI's blog, and Cursor's docs.