Vibe Coding Quality: How to Build Reliable, Secure, Maintainable Software with AI

Improve it with a repeatable workflow: specify clearly, generate in small steps, then verify everything.

1. Write clear prompts. State the goal, inputs, outputs, and constraints.
2. Define technical requirements. Language, framework, style rules, performance and security needs.
3. Provide project context. Share relevant files, schemas, and conventions.
4. Generate code incrementally. One feature or function at a time.
5. Review AI-generated code. Read every line you plan to keep.
6. Run tests frequently. After each change, not at the end.
7. Validate APIs and libraries. Check official docs for every function and package.
8. Check security. Run scanners and review auth, input handling, and secrets.
9. Refactor generated code. Remove duplication and simplify.
10. Use version control. Commit small, so any bad change is easy to revert.
11. Monitor application behavior. Logs, metrics, and error tracking.
12. Perform human code review. Another person should look before merge.

Practical example: weak vs better prompting

Weak approach

“Make a user login system.”

This leaves everything open. The AI may pick weak password storage, skip rate limiting, and invent its own session handling.

Better approach

“Add email/password login to this Express + PostgreSQL app. Use bcrypt for hashing, parameterized queries, and HTTP-only session cookies. Rate-limit login attempts to 5 per minute per IP. Return generic error messages. Follow the existing folder structure in /routes and /services. Include unit tests for success, wrong password, and locked-out cases.”

The second prompt defines requirements, context, security expectations, and tests. That is the single biggest lever on vibe coding quality.

Practical example: reviewing and spotting a bug

Suppose the AI returns this:

app.get(“/user”, async (req, res) => {

  const result = await db.query(

    `SELECT * FROM users WHERE id = ${req.query.id}`

  );

  res.json(result.rows[0]);

});

A quick review finds several problems

• SQL injection: user input is concatenated into the query.
• Over-exposure: SELECT * may return password hashes.
• No authorization: any caller can read any user.
• No error handling: a database failure crashes or hangs the request.

A safer version

app.get(“/user”, requireAuth, async (req, res, next) => {

  try {

    const id = Number(req.query.id);

    if (!Number.isInteger(id)) return res.status(400).json({ error: “Invalid id” });

    if (id !== req.user.id && !req.user.isAdmin) return res.sendStatus(403);

    const { rows } = await db.query(

      “SELECT id, name, email FROM users WHERE id = $1”, [id]

    );

    if (!rows[0]) return res.sendStatus(404);

    res.json(rows[0]);

  } catch (err) {

    next(err);

  }

});

A test then locks in the improvement:

test(“rejects SQL injection attempt”, async () => {

  const res = await request(app).get(“/user?id=1 OR 1=1”).set(authHeader);

  expect(res.status).toBe(400);

});

Practical example: improving architecture

If the AI put routing, validation, database calls, and email sending in one 400-line file, ask it to refactor: “Split this into a route handler, a userService for business logic, and a userRepository for database access. Do not change behavior. Keep existing tests passing.” Then run the tests to confirm nothing broke.