Deep Evaluation of AI Coding Tools: Building an E-commerce Frontend from Scratch

Deep Evaluation of AI Coding Tools: Building an E-commerce Frontend from Scratch

With the rapid advancement of large language models (LLMs), AI coding tools are transitioning from simple “code completion” to “autonomous task execution.” In the current tech ecosystem, various automated build tools are emerging, offering developers unprecedented potential for efficiency gains. However, when facing complex real-world business scenarios, different tools exhibit significant differences in underlying architecture comprehension, code quality control, and bug-fixing capabilities.

This article strips away specific product brands to focus purely on technical capabilities. Using a standard “building an e-commerce frontend from scratch” task as a baseline, it deeply analyzes the performance, pain points, and best practices of mainstream AI coding tools in practical applications.

1. Evaluation Task Design: Full-Chain E-commerce Frontend

To comprehensively test the context-understanding capabilities and engineering generation levels of AI coding tools, we defined a typical mid-to-large frontend business scenario: building a frontend shopping site using the React tech stack. This task covers a complete e-commerce business loop, with specific page requirements including:

1. Basic Flow Pages: Home (Product List), Product Detail, Checkout, and Purchase Success pages.
2. User Authentication Pages: Login and Registration pages.
3. Advanced Interactive Pages: User Dashboard, Orders, Wallet (Coupons), and Settings.

This comprehensive task effectively tests the tools’ overall capabilities in component reuse, state management, form validation, and responsive layout design.

2. Core Capability Dimensions Analysis

During actual testing, different AI coding tools adopted varying generation strategies, exposing highly representative issues. We conducted a neutral technical evaluation across the following four core dimensions:

2.1 Automation Modes and Task Completion

Current AI tools generally offer two mainstream interaction modes: the Craft/Guided mode (developers provide detailed context, and the tool generates step-by-step) and the Auto/Autonomous mode (the tool takes over the entire process, automatically breaking down and executing tasks).

• Risks of Autonomous Execution: In fully automatic mode, some tools exhibited aggressive generation strategies, attempting to build all pages at once. However, this often led to chaotic project structures. For example, in one generation, the tool nested folders with the same name in the directory structure, causing module resolution path errors and ultimately preventing the project from starting. The auto-repair mechanism frequently fell into infinite loops when facing such structural errors, failing after multiple attempts.
• Advantages of Guided Mode: Tools utilizing the guided mode performed more robustly. Although developers needed to input commands step-by-step, the clear context boundaries allowed the tools to generate a complete and runnable shopping process in one go. This indicates that at the current stage, fully autonomous “autopilot” development remains immature for complex engineering. Human-machine collaborative guided development is the optimal solution for balancing efficiency and quality.

2.2 Code Architecture and Engineering Standards

Code quality is a critical metric for determining whether AI coding tools can be applied in production environments.

• Separation of Styles and Logic: Better-performing tools consciously followed the principle of separation of concerns, storing JS logic and CSS styles in separate files. They even pre-reserved custom directory structures like hooks and context, reflecting good engineering thinking. However, some tools reserved architectural slots but left the internal implementations empty, creating a “pseudo-engineered” architecture. Others directly inlined styles, lacking independent file management.
• Data Coupling Issues: In most generated code, mock data was deeply coupled within business component code, failing to be extracted into an independent public data service layer. This anti-pattern significantly increases the refactoring costs for subsequently integrating real API interfaces and fails to comply with enterprise-level frontend development standards.

// Anti-pattern example: Strong coupling of data and views
const ProductList = () => {
  // Mock data written directly inside the component, hard to maintain
  const mockProducts = [{ id: 1, name: 'Product A', price: 100 }];
  return <div>{mockProducts.map(p => <div key={p.id}>{p.name}</div>)}</div>;
};
// Recommended pattern: Abstracted data service layer
import { fetchProducts } from '../services/api';
const ProductList = ({ products }) => {
  return <div>{products.map(p => <div key={p.id}>{p.name}</div>)}</div>;
};

2.3 UI Presentation and Responsive Adaptation

Frontend applications directly face end-users, making UI completeness crucial.

• Basic Visuals: Most tool-generated pages primarily targeted mobile views, with color schemes leaning towards minimalism (e.g., using brand blue as the primary color). The overall result was usable but slightly rudimentary.
• Forms and Interaction: Basic form validations (e.g., required fields, format checks) were generally implemented. Some tools could generate detailed information for product detail pages.
• Critical Blind Spots: Responsive adaptation was a major disaster area. Some generated login and registration pages suffered severe distortion on mobile devices. There were even extreme, basic errors like white text on a white background, rendering functions invisible. Furthermore, images almost exclusively relied on placeholders, lacking abstract designs for integrating real static resource services like object storage.

2.4 Response Speed and Execution Stability

Differences in the underlying LLM’s computing power and network scheduling directly impact the developer’s perceived waiting time. Some tools responded sluggishly when generating large projects, even tempting developers to pay for speed boosts. Others relied on robust compute scheduling, ensuring a smooth, lag-free generation process. Additionally, while Multi-Agent architectures can refine task division, complex mode selections (e.g., offering various modes like Zulu or Builder) ironically increased the cognitive load on developers.

3. Engineering Best Practices for AI Coding Tools

Based on the above testing pain points, teams looking to adopt AI coding tools are advised to follow these practical guidelines:

1. Reject the “One-Click Generation” Fantasy, Adopt Step-by-Step Prompt Engineering
   Do not attempt to complete an entire project with a single broad prompt. Break the business down into independent stages: infrastructure setup, route configuration, page component generation, and state management integration. Use refined prompts to guide the tool in generating modules with clear structures and single responsibilities.
2. Pre-define Architectural and Standard Constraints
   Explicitly declare project structure rules in your prompts. For example: “Abstract the API request layer into src/services, use CSS Modules for component styles stored in the same directory, and strictly prohibit hardcoding Mock data inside components.” Strong constraints prevent the tool’s code smells.
3. Leverage Cloud-Native Capabilities to Bridge Infrastructure Gaps
   AI tools mostly generate pure frontend code logic and are typically incapable of handling backend services, data storage, and resource distribution. In actual engineering, developers should combine the tool-generated code with standard cloud services. For instance, use standard object storage services to manage static resources like product images, utilize message queues to handle asynchronous order processes, rely on container platforms for automated deployment, and integrate logging services and monitoring alerts to ensure online stability. This model of “AI-generated business code + cloud services for underlying support” is the efficient paradigm for modern software development.

Conclusion

AI coding tools have made significant progress in code generation speed, but they still cannot fully replace the judgment of senior developers regarding engineering architecture for complex businesses, deep logic repair, and UI detail control. Developers should treat them as a “junior engineer with strong execution but lacking architectural vision.” By controlling core designs in human-machine collaboration and letting AI tools handle tedious coding tasks, teams can ultimately achieve a true leap in R&D efficiency.