Exploring Domestic AI Coding Tools: Practical Test on Building a Complex React E-commerce Frontend

1. Background and Engineering Challenges

With the growing need for technological autonomy, local developers and enterprises are increasingly demanding Intelligent Integrated Development Environments (IDEs) that operate without network barriers. Setting aside brand-specific marketing, this article returns to technical fundamentals. We explore how to use existing domestic AI coding tools to build a moderately complex engineering project from scratch, deeply analyzing their technical performance in practical scenarios.

To objectively evaluate the true capabilities of current AI coding tools, we designed a highly representative business scenario: building a complete e-commerce frontend site from 0 to 1. The tech stack utilizes the mainstream React framework, requiring the tool to generate 10 core pages in a single pass. These pages include the Home Page (product list), Product Detail, Purchase Page, Purchase Success Page, User Login, User Registration, User Dashboard, Orders, Wallet (coupons), and Settings.

The core challenge of this task lies in “site completeness and overall consistency,” which manifests in three dimensions:

1. Cross-page State Management: The shopping flow involves maintaining user authentication states, synchronizing shopping cart data, and managing order status transitions. This requires the generated code to possess global state management capabilities (e.g., Context API or a state management library).
2. UI and Interaction Consistency: All 10 pages must adhere to a unified design language, including color schemes, component library usage standards, and responsive layout adaptation.
3. Architectural Standardization: The generated code should not be a flat collection of scripts. It requires a rational directory structure, separating styles and decoupling the data layer from the view layer.

2. Impact of AI Interaction Modes on Generation Efficiency

Current AI coding tools generally offer multiple generation modes. One leans towards a “conversational guidance” approach, requiring developers to confirm step-by-step. The other favors a “fully automated execution” approach, where the tool independently plans and completes the task after receiving the requirements. In practical testing, while the fully automated mode is intuitive, the lack of manual intervention nodes often causes initial directional deviations, triggering a cascade of errors.

Conversely, tools utilizing a “Craft” mode or a multi-agent collaborative architecture perform more robustly when executing complex tasks. This approach allows developers to review code and correct directions at critical nodes, effectively avoiding the “one wrong step, everything goes wrong” generation dilemma.

3. Analysis of Technical Approaches

3.1 UI-Friendly Approach: Fast but Architecturally Weak

Some tools leverage powerful cloud computing to excel in code generation speed, offering highly user-friendly interface interactions. When executing the aforementioned e-commerce task, this category of tools output runnable code in one pass without lag.

• Frontend Performance: The generated pages lean towards mobile adaptation with basic responsive handling. However, adaptive deformation issues occur on form-heavy pages like login and registration. The overall color scheme is unified, the shopping flow loop is complete, and basic form validation is included. Yet, the product detail page information is overly simplistic, and all images rely on placeholders, lacking the immersion of real business data.
• Code Quality: The code quality is acceptable, achieving the separation of style and logic files. However, the project architecture is somewhat thin. The biggest issue is that mock data is directly hardcoded into business components without an independent data layer, leaving significant technical debt for later API integration.

3.2 Experience-Inherited Approach: Rich Details but Poor Stability

Another type of tool inherits the fluid feel of traditional mature IDEs and adds developer-friendly features like a privacy mode.

• Frontend Performance: The strength of these tools lies in detail processing, featuring complete form validation logic and richer information dimensions on the product detail page. However, the flaws are equally obvious: the overall project is relatively crude, and the mobile interface lacks adaptive capabilities. A severe styling bug even occurred with white text on a white background, making content invisible. The color scheme lacks aesthetic consistency, and placeholder images are heavily used.
• Code Quality: The code structure shows some foresight, reserving directories for hooks and context. However, in the actual generated code, these modules are empty and fail to truly decouple state management. Furthermore, although style separation is implemented, it is not entirely independent into files. More critically, the first generated code contained runtime errors requiring multiple manual interventions to fix, and the generation speed was slow, easily triggering developer efficiency anxiety.

3.3 Multi-Agent Collaborative Approach: Severely Polarized Capabilities

Some tools introduce a multi-agent architecture, attempting to handle complex tasks through role division (e.g., architect, developer).

• Fast Mode: The generation speed is extremely fast, but the project structure is chaotic. It even made a novice error of nesting folders with the same name, ultimately causing project dependency resolution to fail and the application to not start.
• Architecture Mode: This mode focuses on complex task processing but is slightly sluggish in service response. Testing showed that the generated project could barely run, but the console reported frequent errors. Faced with multiple errors, the tool’s self-repair capability fell into an infinite loop. After several unsuccessful repair attempts, the task had to be abandoned. This exposes that under complex context dependencies, the tool’s error-correction ability remains insufficient.

4. Pitfalls and Best Practices

Based on the test results, developers should follow these best practices when selecting and using domestic AI coding tools:

1. Avoid Over-reliance on “One-Click Generation”: For complex projects with 10 or more pages, decompose the requirements. Use the tool to generate the basic framework and routing first, then generate page by page or module by module.
2. Focus on Data Layer Isolation: Explicitly require the tool in the prompt to “extract Mock data into independent api or mock directories, and hardcoding inside components is prohibited.” This drastically reduces later integration costs.
3. Strengthen Architectural Constraints: Specify project structure standards in the instructions, such as “Use Context API for global state management and create corresponding files under src/context,” to prevent the tool from generating empty shell directories.
4. Seamless Integration with Cloud Services: In modern frontend engineering, static resource management is crucial. In the later stages, developers should integrate common object storage services to host media resources like images rather than relying on local placeholders. Additionally, utilize message queues and cloud monitoring alerts to ensure site stability under high-concurrency e-commerce scenarios. Combining AI-IDE-generated frontend logic with mature cloud-native infrastructure is the only way to build a truly commercial-ready e-commerce system.

In conclusion, while 2026’s domestic AI coding tools have made significant strides in code generation efficiency, they still exhibit clear shortcomings in complex engineering architecture design, cross-page state management, and detailed style tuning. Developers must enhance their “prompt engineering” skills, treating AI as an auxiliary executor rather than a full-stack architect, to maximize its value.