AI-Driven Frontend Refactoring – Achieve Better Code Quality

Gracjan Prusik

07 Apr 2025
AI-Driven Frontend Refactoring – Achieve Better Code Quality

Introduction

AI-Driven Frontend Refactoring is one of those tasks that most frontend developers tend to put off ‘for later.’ And honestly, it’s hard to blame them—it often requires time, focus, and... a fair amount of patience. But what if I told you that today, a large part of that process can be handled by artificial intelligence?

AI doesn’t just help us write new code anymore—it increasingly supports us in maintaining and improving existing codebases. With tools like ChatGPT, DeepSeek, Claude or Cursor, frontend refactoring with AI is no longer a tedious chore but a fast, semi-automated process.

In this article, I’ll show you how AI-Driven Frontend Refactoring can support frontend developers in code refactoring, improving readability, updating styles, and fixing bugs—and which specific tools are worth knowing.

Why Is Refactoring So Important?

Before we dive into the details, it’s worth asking: why do we even bother with refactoring in the first place?

  • To improve code readability
  • To make maintenance and future development easier
  • To eliminate repetitive code blocks
  • To prevent bugs before they happen

Refactoring is like cleaning your apartment—no one really enjoys it, but everyone knows it has to be done from time to time.

Now imagine you have a robot that does the cleaning for you.
That’s exactly what AI brings to the table.

AI-Driven Refactoring of Frontend Functions – From Complex to Simple

Let’s take a simple example – a function that does multiple things at once:

function handleUser(user) {
  if (user.isLoggedIn && user.token) {
    const fullName = `${user.firstName} ${user.lastName}`;
    localStorage.setItem("userToken", user.token);
    alert(`Welcome, ${fullName}!`);
  } else {
    alert("Please log in.");
  }
}

Request to AI: "Refactor the code to make it more readable and in line with best practices."

AI responds:

function isAuthenticated(user) {
  return user.isLoggedIn && user.token;
}

function getFullName(user) {
  return `${user.firstName} ${user.lastName}`;
}

function greetUser(user) {
  localStorage.setItem("userToken", user.token);
  alert(`Welcome, ${getFullName(user)}!`);
}

function handleUser(user) {
  if (isAuthenticated(user)) {
    greetUser(user);
  } else {
    alert("Please log in.");
  }
}

This code is:

  • More modular – The logic is broken down into smaller, more manageable parts (filter() and map()), making it easier to modify and extend.
  • Easier to test – With pure functions and no side effects, each part of the function can be tested independently, ensuring better maintainability and reliability.
  • Ready for further development – The structure is clean, concise, and follows best practices, making it easy to add more features or handle additional requirements in the future.

AI-Driven Refactoring of Frontend Styles – From CSS to Tailwind / Styled Components

Another typical case: you want to migrate your code from traditional CSS to a newer styling approach, such as Tailwind CSS.

Original Code (CSS):

.card {
  background: white;
  padding: 20px;
  border-radius: 8px;
  box-shadow: 0px 4px 10px rgba(0,0,0,0.1);
}

Request: "Convert this style to a Tailwind class."

AI responds:

<div class="bg-white p-5 rounded-lg shadow-md"></div>

And that's it – no need to search through the documentation, no guessing which shadow fits.

AI-Driven Simplification of Frontend Component Code

You have a component that does too much, and you’d like to break it down?

function UserProfile({ user }) {
  return (
    <div>
      <h2>{user.name}</h2>
      <p>{user.email}</p>
      <p>{user.bio}</p>
      {user.isAdmin && <span>Admin</span>}
    </div>
  );
}

AI might suggest:

const UserHeader = ({ name }) => <h2>{name}</h2>;
const UserInfo = ({ email, bio }) => (
  <>
    <p>{email}</p>
    <p>{bio}</p>
  </>
);
const AdminBadge = () => <span>Admin</span>;

function UserProfile({ user }) {
  return (
    <div>
      <UserHeader name={user.name} />
      <UserInfo email={user.email} bio={user.bio} />
      {user.isAdmin && <AdminBadge />}
    </div>
  );
}

Więcej komponentów = większa kontrola, lepsza czytelność.

Removing Dead Code and Unused Dependencies in Frontend with AI

AI can analyze the file structure and indicate which functions or imports are not being used.

Request: "Point out unused variables and functions in this file."

Example of results:

// ❌ Unused import
import { calculateAge } from "../utils";

// ❌ Unused variable
const debugMode = true;

This is especially useful in large projects – it saves time and reduces clutter.

Automating Refactoring Tests with AI – From Manual to Automated

AI can also help rewrite tests, for example, from manual console.log statements to proper unit tests (e.g., in Jest).

Code before:

console.log(getUserGreeting("Anna")); // should return "Hello, Anna!"

Code after:

test("returns greeting for user", () => {
expect(getUserGreeting("Anna")).toBe("Hello, Anna!");
});

For large applications – a huge time saver.

Tools that support AI-driven refactoring

Here are some tools that are particularly worth testing:

  • ChatGPT / Claude / DeepSeek – general support, code generation, and refactoring.
  • Github Copilot – automatic refactoring suggestions in the editor.
  • Cursor – AI-first IDE that allows refactoring, code analysis, and working with code without leaving the editor.

Summary

Refactoring frontend code with the help of AI is not the future – it's the present. Thanks to AI-powered tools, we can improve code quality faster, safer, and without the frustration.

Whether you want to:

  • organize functions,
  • simplify styles,
  • detect unused imports,
  • or generate tests – AI can help with that.

In the next part of the series, we'll take a look at:

  • Code review with AI: Which tools help analyze code?
  • Optimizing frontend application performance with AI
  • Accelerated onboarding of new developers with AI

Frontend with AI is not just about faster development – it's also cleaner, better, and more satisfying code. If you want to learn how to create components.

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series