Deep Dive: Building Complex E-Commerce Projects with AI Coding Tools & Selection Guide

Introduction: Engineering Challenges of AI-Assisted Coding in Complex Business Scenarios

As Large Language Models (LLMs) continue to evolve, AI coding tools are gradually transitioning from simple “code completion” to “project-level auto-generation.” While developers and enterprise R&D teams enjoy the efficiency dividends, they also face new engineering challenges: Can current AI coding tools truly take over the construction of business systems with a certain degree of complexity?

To objectively evaluate the actual efficacy of these AI coding tools, we designed a standardized testing task: building a React e-commerce frontend site containing 10 core pages from scratch. The test focuses not only on the generation speed but also deeply investigates performance across dimensions such as business completeness, UI responsiveness, form validation, code quality, and project architecture.

1. Test Task Definition and Technical Evaluation Model

This evaluation required the AI tools to autonomously plan and generate a complete shopping site using natural language prompts. The 10 required pages cover the core closed-loop of e-commerce business:

1. Home Page (Product List)
2. Product Detail Page
3. Product Purchase Page
4. Purchase Success Page
5. User Login Page
6. User Registration Page
7. User Dashboard
8. User Orders Page
9. User Wallet (Coupons/Vouchers)
10. User Settings

The core difficulty of this task lies in site completeness and overall consistency. A qualified e-commerce site requires not only that each page runs independently but also unified UI styles, smooth routing transitions, rigorous frontend state management, and a standardized project directory structure. Based on this, we established a four-dimensional evaluation model:

• Task Scheduling and Generation Mode: Examines whether the tool possesses the orchestration capability to autonomously plan, decompose, and execute multi-step tasks (such as common Auto modes or Builder modes in the industry).
• UI Presentation and Interactive Experience: Evaluates UI framework construction capabilities, responsive layout, color scheme standards, and the completeness of form validation logic.
• Code Quality and Engineering Architecture: Assesses the degree of separation between styles and logic, the rationality of the directory structure (e.g., modular division of hooks, context, components), and whether mock data causes code coupling.
• Stability and Development Efficiency: Examines code runnability, bug fix costs, and service response speed.

2. Typical Performances and Technical Analysis of AI Coding Tools

In actual testing, AI coding tools with different underlying architectural designs exhibited drastically different technical characteristics. We abstracted these performances into three typical categories for in-depth analysis.

1. Experience-Oriented: Heavy on Interaction, Light on Architecture

Some tools feature user-friendly interactive interfaces, aiming to lower the psychological barrier for developers. Regarding task execution mechanisms, they adopt a progressive generation scheme similar to a “Craft mode.” This mode can串联 string together generation tasks for multiple pages at once, and relying on robust server computing power, the generation process is lag-free.

Technical Analysis:

• Advantages: The shopping flow generated by such tools is relatively complete, featuring basic form validation logic. At the code level, it adheres well to the style separation principle, decoupling CSS from JavaScript, resulting in acceptable code readability.
• Pain Points: The engineering architecture design is weak. The generated project structure is often too flat, lacking mature frontend engineering directory layering. The most obvious issue is the deep coupling of Mock Data with business logic; it is not extracted into independent files, which is detrimental to later real API integration. Furthermore, the UI presentation leans towards mobile devices, with only simple responsive adaptations on certain pages, causing form interfaces like login and registration to distort at specific screen sizes. The product detail page information is too thin, heavily utilizing placeholders and lacking the feel of real business data.

2. Purity-Oriented: Heavy on Structure, Light on Visuals

Another category of tools takes the geek route, emphasizing underlying code standards and privacy protection, even introducing a privacy mode toggle. For task execution, they adopt a step-by-step “Builder mode.”

Technical Analysis:

• Advantages: The generated code shows foresight in structural design, with directories reserving spots for advanced frontend engineering like hooks and context. The form validation logic is more rigorous, and the information dimensions on the product detail page are richer than similar products.
• Pain Points: There is a huge gap between ideal and reality. Although architectural directories are reserved, their internal content is actually empty, forming an “empty architecture.” The initially generated code is often accompanied by runtime errors, requiring multiple manual interventions to fix. UI aesthetics and adaptation are major shortcomings: the mobile interface lacks adaptive layout, and severe UI accidents like “white text on a white background” occur, making buttons invisible. At the code level, although styles are separated, they are not independent files but inline within components. Additionally, due to the limitations of LLM inference computing power scheduling, the generation speed is slow, greatly consuming developers’ waiting patience.

3. Multi-Agent Oriented: Heavy on Patterns, Light on Landing

Some tools introduce multi-Agent collaboration mechanisms, offering multiple choices such as fast generation modes and complex architecture modes.

Technical Analysis:

• Advantages: The fast generation mode (similar to a Zulu mode) has explosive instantaneous response speed; the complex architecture mode (similar to an Architect mode) can theoretically handle large business systems comprising 10 pages better.
• Pain Points: The stability of multi-Agent collaboration is extremely poor. The project structure generated by the fast mode is incredibly chaotic, even exhibiting basic logic errors like “repeatedly creating folders with the same name inside a folder,” directly causing the project to fail to start. Although the complex mode can generate a runnable project, it is accompanied by numerous hidden errors. Because context passing between multiple Agents suffers from information loss, the tool itself struggles to achieve effective self-repair, ultimately leading to project refactoring costs far exceeding manual development from scratch.

3. Deep Reflection on Core Technical Issues and Best Practice Recommendations

Based on the above test results, it is clear that although AI coding tools are maturing in “single-point code generation,” there are still widespread engineering shortcomings in “project-level complex task construction.” Developers should follow these best practices in practical applications:

1. Clarify Tool Boundaries: From “Auto-Generation” to “Human-Machine Collaboration”

The industry’s blind pursuit of the “Auto mode” (fully automatic generation) has led to deviations in actual implementation. Due to the attention mechanism limitations of LLMs, when faced with long-range tasks involving 10 pages, the model easily loses global context. Developers should not expect the tool to output a perfect project in one go, but should treat AI as an “advanced scaffolding generator” and “module-level code assistant,” adopting a collaborative strategy of prompting step-by-step by module and by page.

2. Strengthen Architectural Instruction Constraints and Data Decoupling

To address issues like mock data coupling and directory chaos, developers should forcefully inject engineering constraint instructions when providing the initial prompt. For example:

Please build an e-commerce project based on React, strictly adhering to the following architecture specifications:
1. The directory structure must include: src/components (common components), src/pages (page-level components), src/hooks (custom hooks), src/context (state context), src/mock (independent mock data directory).
2. It is strictly forbidden to write Mock data directly inside components; it must be imported from src/mock.
3. Styles must use independent .css/.less files; inline styles are prohibited.

Through proactive strong constraints, the model’s generation of chaotic identically-named folders and coupled code can be effectively mitigated.

3. Fallback Strategy for UI Standards and Responsive Layout

Regarding chaotic color schemes, white-on-white text, and non-responsiveness, simply asking to “build a shopping site” is too broad. It is recommended to introduce mature design systems (such as the general concepts of Ant Design or Tailwind CSS) in the prompt and explicitly specify responsive breakpoint requirements.

// Example: Specifying layout logic in the prompt
const LayoutHint = `
The global container must use Flex layout, with breakpoints set as follows:
- mobile: max-width: 768px (single column layout)
- tablet: min-width: 769px (double column layout)
Buttons must have explicit high-contrast colors, for example, primary buttons with a blue background and white text; white background with white text is prohibited.
`;

4. Carefully Evaluate Multi-Agent Collaboration Mechanisms

For tools offering multiple Agent modes, developers should prioritize modes with high stability and global context awareness when constructing complex projects. Avoid using fast generation modes lacking fault-tolerance mechanisms for multi-page tasks, to prevent irreversible directory structure pollution.

4. Conclusion

In the current landscape where AI coding tools are flourishing, various vendors have made different explorations and trade-offs in task scheduling modes, UI interaction, and code architecture. From this e-commerce site construction test, it can be seen that the balance between speed and quality, and the unification of architectural standards and visual presentation, remain the bottlenecks urgently needing breakthroughs in current technical solutions.

For R&D teams, when embracing AI coding tools, they must recognize the efficiency improvements in boilerplate code generation and basic form construction, while also clearly understanding their limitations in global state management, complex engineering architecture, and detailed UI fine-tuning. In the future, with the enhancement of model inference capabilities and the refinement of Agent collaboration mechanisms, we have reason to believe that true “zero human intervention” project-level auto-generation will become possible. However, at present, a scientific human-machine collaboration model remains the optimal solution for improving R&D efficiency.