UA In short: what vibe coding is
Vibe coding is an approach where most of the code is written by an AI (an LLM), while the human states requirements, checks the output, and steers the iterations. In practice it looks like a dialogue: you describe the functionality you want in plain language, and the model generates the code, tests, documentation, and even deployment instructions.
Why it matters right now
It matters because LLMs have reached a point where they can reliably produce correct boilerplate, CRUD modules, tests, and documentation, cutting “time to first prototype” several times over. The developer’s focus shifts from typing lines of code to product logic, architecture, security, and integrations.
How vibe coding works: the cycle, step by step
The essence of the cycle is that you set the direction (the “vibe”), the AI generates artifacts, and you validate and narrow things down to the result you actually need.
Step 1. Frame the task as a prompt
You start with a short, specific prompt: what’s needed, why, in which environment, and under what constraints.
Example prompt:
“Build a REST API for managing orders with Flask + SQLAlchemy. Fields: id, user_id, total, status. Add JWT auth, pagination, a Swagger spec, a Dockerfile. Explain the deployment steps on Ubuntu.”
Step 2. Code generation (LLM programming)
The model generates files, modules, tests, and a README. You immediately get a working “skeleton” of the solution.
Step 3. Validation and refinement
You run the project, write unit tests, and ask the AI: “optimize this,” “rewrite it for FastAPI,” “add a Redis cache,” “move the config into .env.”
Step 4. Integration, deployment, retro
The AI prepares a docker-compose file, CI/CD instructions, and baseline security policies; you approve them, measure metrics (performance, test coverage), and run a retro on the prompts you used.
LLM architecture: what a developer should know
To get consistent quality, a basic understanding of how the model “thinks” is enough.
Transformers and context
Modern LLMs are transformers that operate on a context window: everything you pass in the prompt (requirements, code snippets, errors) becomes the model’s “field of view.” Curate the context carefully and include only what’s relevant.
Code-oriented models and tooling
Models trained on code hold syntax and patterns more reliably. Combine them with tooling: static analysis, formatters (Black/Prettier), linters, API schema generators.
RAG and “context from the repo”
Retrieval-augmented generation (RAG) lets you feed the model your private documentation: style guides, architecture decisions, API contracts. This reduces hallucinations and improves fit with your stack.
Prompt engineering for vibe coding
A well-formed prompt saves hours of rework and review.
A prompt template for functional modules
- Role and goal: “You are a senior backend engineer. I need a billing module…”
- Environment: “Python 3.11, FastAPI, PostgreSQL 14, Docker”
- Features and constraints: “JWT, admin/user roles, 100 RPS, idempotent POST /invoices”
- Output artifacts: “code + tests + README + OpenAPI + Dockerfile”
- Quality bar: “PEP8, mypy, 90% coverage, no SQL injection vulnerabilities”
- Style: “Pure functions, layered structure, short module names”
Examples of follow-ups
- “Rewrite this using raw SQL and a connection pool instead of an ORM”
- “Add a rate limit and exponential backoff for outgoing requests”
- “Split the config into prod/stage/dev via .env”
Technical examples: backend, frontend, SQL, infra
Flask + JWT + Swagger (abridged example)
# app.py
from flask import Flask, request, jsonify
from flask_jwt_extended import JWTManager, jwt_required, create_access_token
from flasgger import Swagger
app = Flask(__name__)
app.config["JWT_SECRET_KEY"] = "change_me"
jwt = JWTManager(app)
swagger = Swagger(app)
USERS = {"admin@example.com": "secret"}
ORDERS = []
@app.post("/login")
def login():
data = request.get_json()
if USERS.get(data.get("email")) == data.get("password"):
return {"access_token": create_access_token(identity=data["email"])}
return {"error": "invalid creds"}, 401
@app.post("/orders")
@jwt_required()
def create_order():
order = request.get_json()
order["id"] = len(ORDERS) + 1
ORDERS.append(order)
return order, 201
@app.get("/orders")
@jwt_required()
def list_orders():
page = int(request.args.get("page", 1))
size = int(request.args.get("size", 10))
start = (page - 1) * size
return jsonify(ORDERS[start:start+size])
if __name__ == "__main__":
app.run()
Unit test with PyTest
import json
from app import app
def test_auth_and_create_order():
client = app.test_client()
r = client.post("/login", json={"email":"admin@example.com","password":"secret"})
token = r.json["access_token"]
headers = {"Authorization": f"Bearer {token}"}
r2 = client.post("/orders", headers=headers, json={"total": 1200, "status": "new"})
assert r2.status_code == 201
React/Next.js: a controlled form component with validation
import { useState } from "react";
export default function CheckoutForm() {
const [data, setData] = useState({ email: "", total: 0 });
const [errors, setErrors] = useState({});
const onChange = e => setData({ ...data, [e.target.name]: e.target.value });
const validate = () => {
const e = {};
if (!/^[^s@]+@[^s@]+.[^s@]+$/.test(data.email)) e.email = "Invalid email";
if (Number(data.total) <= 0) e.total = "Amount must be > 0";
setErrors(e);
return !Object.keys(e).length;
};
const onSubmit = async e => {
e.preventDefault();
if (!validate()) return;
await fetch("/api/orders", { method: "POST", body: JSON.stringify(data) });
};
return (
<form onSubmit={onSubmit}>
<input name="email" value={data.email} onChange={onChange} placeholder="Email" />
{errors.email && <small>{errors.email}</small>}
<input name="total" type="number" value={data.total} onChange={onChange} placeholder="Amount" />
{errors.total && <small>{errors.total}</small>}
<button type="submit">Pay</button>
</form>
);
}
SQL: top customers by spend, with indexes
CREATE INDEX IF NOT EXISTS idx_orders_customer_date ON orders (customer_id, created_at DESC);
SELECT customer_id, SUM(amount) AS total_spent
FROM orders
WHERE created_at >= NOW() - INTERVAL '90 days'
GROUP BY customer_id
ORDER BY total_spent DESC
LIMIT 10;
IaC: a basic Terraform setup for a VPS deploy (excerpt)
provider "aws" { region = "eu-central-1" }
resource "aws_instance" "app" {
ami = "ami-xxxxxxxx"
instance_type = "t3.micro"
tags = { Name = "vibe-app" }
user_data = file("bootstrap.sh")
}
WordPress/WooCommerce: an LLM-generated plugin skeleton
<?php
/*
Plugin Name: WDS Vibe Example
*/
add_action('init', function () {
register_post_type('wds_doc', [
'label' => 'Docs',
'public' => true,
'supports' => ['title','editor','custom-fields']
]);
});
add_action('woocommerce_before_calculate_totals', function ($cart) {
foreach ($cart->get_cart() as $item) {
$product = $item['data'];
$mult = (float) get_post_meta($product->get_id(), '_wds_multiplier', true);
if ($mult > 0) $product->set_price($product->get_price() * $mult);
}
});
Quality checks and security in vibe coding
Quality doesn’t come from generation itself — it comes from your control perimeter around it.
Mandatory checks
- Static analysis: ESLint/Prettier, Flake8/Black, mypy, PHPStan
- Tests: unit + integration, ≥ 80–90% coverage on critical modules
- SAST/DAST: finding vulnerabilities in code and at runtime
- Dependency audit: checking CVEs and license policies
- Secret scanning: making sure no keys end up in the repo
OWASP guardrails
Require the AI to handle: input validation, parameterized SQL queries, correct CORS/CSRF, secret encryption, and least-privilege access.
Organizing a team around AI
A team process removes chaos and adds repeatability.
Roles
- Collector (BA/PM): gathers requirements and use cases
- Prompter (Dev/Lead): writes prompts and drives the iterations
- Reviewer (Senior): handles code review and threat modeling
- Ops: sets up CI/CD, monitoring, and secrets policy
Git workflow
Feature branches, mandatory PRs, automated linting, tests, security scans. An “AI PR” goes through the same gates as a human one.
Where vibe coding saves the most
The biggest ROI is on boilerplate and routine work.
- CRUD modules, standard DB migrations, the REST/GraphQL layer
- Tests and fixtures, mock servers
- Documentation, examples, Postman collections
- Standard frontend markup, forms, dashboards
- Integrations with popular services (Stripe, SendGrid, S3)
Where you should stay careful or go manual
Critical security modules, cryptography, complex financial algorithms, and high-load parts with non-trivial optimizations all need human design and thorough review.
Advanced techniques and patterns
- Chain-of-thought / plan-then-code: ask the LLM to draft a plan first, then code against that plan
- Critic loop: in a separate prompt, ask the model to critique its own code and suggest improvements
- Spec-first: hand it a contract (OpenAPI/JSON Schema) and require strict compliance
- Diff-based edits: instead of “rewrite the file,” ask for “changes as a unified diff” — much easier to review
In practice: zero to production in one day
- Morning: a short spec + prompt → a backend and frontend skeleton
- Midday: refinements, tests, documentation, containerization
- Evening: the lead engineer runs a security checklist, a performance smoke test, and ships to staging
- Overnight: automated benchmarks and regression checks, release to production in the morning
Metrics for vibe coding success
- Time-to-prototype (TTP): hours instead of days
- Coverage: ≥ 80% on business-critical parts
- Defect rate: defects per 1,000 lines after review
- Lead time for change: time from spec to merge
- MTTR: average time to fix — should drop thanks to fast AI iterations
WordPress/WooCommerce integration (for practitioners)
- Generating plugin skeletons and hook templates
- Bulk migration of metadata / ACF field schemas
- Auto-generating structured data (JSON-LD) from your data layer
- WP-CLI tasks: import/export, health checks, cron jobs
Example WP-CLI command (LLM-generated skeleton):
if (defined('WP_CLI') && WP_CLI) {
WP_CLI::add_command('wds:fix-prices', function($args, $assoc_args) {
$q = new WP_Query(['post_type' => 'product','posts_per_page'=>-1]);
while($q->have_posts()){ $q->the_post();
$id = get_the_ID();
$mult = (float) get_post_meta($id, '_wds_multiplier', true);
if ($mult>0) {
$price = (float) get_post_meta($id, '_price', true);
update_post_meta($id, '_price', $price*$mult);
}
}
wp_reset_postdata();
WP_CLI::success('Prices updated');
});
}
Risks and compliance
- Confidentiality: don’t feed secrets, personal data, or NDA-covered code into public models
- Licensing: check the license terms on generated snippets
- Fact-checking: treat source links the model provides as a starting point only, and verify them manually
Vibe coding adoption checklist
- Pick a task from the “quick wins” list (CRUD/tests/docs)
- Lock in prompt templates and acceptance criteria
- Add linters, tests, SAST/DAST to CI
- Agree on a secrets and compliance policy
- Train the team on “plan-then-code” and diff-based edits
- Track TTP, coverage, defect rate, and MTTR every sprint
FAQ: quick answers
Will vibe coding replace developers? No; it changes the role: from typing code to design, review, security, and integrations.
Can you rely on AI without review? No; always keep a human in the loop.
What about security? Put in place a secrets policy, SAST/DAST, dependency audits, and mandatory tests.
Where do you start? Take a small service, write a spec, apply the prompt template, and wire up CI checks.
Closing thoughts
Vibe coding is a new level of abstraction in development: the AI writes the code, and the engineer sets the direction and guarantees quality. With a well-organized process, you cut the time to prototype, raise test coverage, and free up the team’s time for architecture, security, and product work. For a business, that means faster releases and lower TCO; for developers, it’s a shift from “coder” to architect and technical lead.
