Building an E-commerce Site with AI Coding Tools: Practices and Challenges

As large-model technologies evolve rapidly, AI coding tools are moving from single-purpose code completion toward full-stack code generation and agent-based collaboration. For developers, intelligent development platforms that require no complex network configuration and fit the local development environment are becoming increasingly important. To objectively evaluate the engineering capability of such tools in real business scenarios, we designed a comprehensive test task: building an e-commerce site from scratch. We then analyzed the quality of the generated code and the overall development experience.

1. Test Task Design and Requirement Breakdown

The best way to evaluate an AI coding tool is to place it in a realistic business scenario. This test task used a mainstream front-end framework to build a front-end shopping site from zero to one, with a complete end-to-end business flow.

The requirements covered ten core pages:

1. Home page (product list display)
2. Product detail page (product specifications and introduction)
3. Product purchase page (checkout and order confirmation)
4. Purchase success page (flow completion feedback)
5. User login page
6. User registration page
7. User center - Dashboard
8. User center - Orders
9. User center - Wallet (coupons and cards)
10. User center - Settings

The challenge was not generating a single page, but ensuring the completeness and overall consistency of the site. An e-commerce system involves complex routing, state sharing, and unified UI conventions, which place high demands on an AI tool’s context understanding and engineering orchestration capabilities.

2. Core Evaluation Dimensions for Intelligent Coding Tools

In actual use, different AI coding tools showed distinct technical characteristics. We can evaluate them professionally from the following three core dimensions.

1. Interaction Mode and Task Orchestration

Current intelligent coding tools mainly use two interaction modes. One is a highly automated full-auto mode that attempts to understand requirements and output a complete project in one pass. The other is a step-by-step construction mode that requires developers to confirm and guide key decisions. The full-auto mode can greatly lower the operating barrier, but it easily loses context in complex projects. The step-by-step mode is more robust and allows developers to correct generation logic incrementally. A good interaction design should balance machine automation with developer control.

2. Code Generation Quality and Engineering Completeness

In e-commerce scenarios, form validation is essential. The evaluation found that mature tools can accurately generate form logic with non-empty field validation and regular-expression format matching, and can provide a relatively rich page structure on product detail pages. However, some tools produce weak visual results. For example, login and registration interfaces distort at different screen sizes and lack proper responsive adaptation. Product images are often replaced by default placeholders, and the content depth of product detail pages is insufficient.

3. Runtime Stability and Debugging Cost

Whether generated code can run successfully in one pass is an important indicator of tool maturity. In complex project generation, some tools produce code with runtime errors on first output, such as incorrect component import paths or undefined state. This requires developers to have troubleshooting skills and repeatedly prompt the AI to fix issues before seeing a working result. Low first-run success rates and high debugging costs are common challenges facing automated code generation today.

3. Pain Point Analysis and Optimization Strategies for AI-Generated Code

To address the problems exposed in the evaluation, developers can improve final output quality by optimizing prompt engineering and introducing a cloud-native architecture.

1. Strengthen UI Specifications and Responsive Layout Constraints

To solve interface distortion and consistency issues, requirements sent to the AI should explicitly constrain the UI framework and layout specifications. For example, developers can require Flexbox or CSS Grid for responsive design and predefine theme colors and component libraries.

Example prompt specification:

Please use a mainstream UI component library to build the pages.
Requirements:
1. Use a mobile-first responsive design globally, with a breakpoint of 768px.
2. Center the login and registration pages vertically and horizontally, with a maximum form width of 400px.
3. Avoid placeholder images. Use local static assets or generate solid-color background blocks instead.

2. Improve Data Flow and State Management

For multi-page, highly interactive systems such as e-commerce sites, AI tools often generate independent logic for each page by default and lack global state management. Developers need to guide the AI to introduce a state management library and centrally store information such as shopping cart data and user login state to prevent data loss during page transitions.

3. Use Cloud-Native Architecture to Improve Robustness

Front-end page generation is only the first step. A complete e-commerce site needs a robust backend. In actual deployment, developers can combine Baidu AI Cloud’s cloud-native capabilities to improve the overall architecture. For example, they can use a Baidu AI Cloud cloud server to host backend API services, use BOS object storage to store real product image resources and replace AI-generated placeholders, and use log services to monitor front-end runtime errors. This combination of “AI-generated front end + cloud-native backend” can effectively address the limitations of AI tools in generating backend business logic and deployment capabilities, helping build a truly usable production-grade application.

4. Conclusion and Outlook

The practice of building an e-commerce site from scratch shows that the AI coding tools tested here, which can be used without complex network configuration, already provide practical value in basic code generation and form validation, and can significantly improve the efficiency of front-end page initialization. However, there is still room for improvement in context maintenance for complex projects, responsive UI detail handling, and first-run code success rates.

In the future, as large models deepen their understanding of engineering context, intelligent coding tools will gradually evolve from “code generators” into “full-stack agents.” While embracing these tools, developers should also strengthen their architecture design and troubleshooting skills, treat AI as a productivity assistant, and combine it with mature cloud computing infrastructure to maintain core competitiveness in the technology wave.