Testing AI Coding Tools for E-commerce: Can AI IDEs Deliver Production-Ready Projects?

1. Background: From Code Completion to Project Delivery

AI coding tools are no longer just simple code completion plugins. Increasingly, developers are attempting to hand over the entire lifecycle—from requirements to a runnable project—to intelligent coding assistants. Particularly in fast-iterating frontend development where small teams have limited resources, using an AI IDE or smart development platform to generate a runnable prototype has become common practice.

However, in actual usage, several common issues frequently emerge:

• Fast generation speed, but chaotic project structure;
• Pages can run, but lack detailed interactions and responsive adaptation;
• Superficially completing 10 pages, but much of the logic is just an empty shell;
• Hardcoded data within components, requiring nearly a complete rewrite for future development;
• Unstable visual styles, with an obvious sense of fragmentation across different pages.

These issues are not unique to any single tool; rather, they are universal challenges currently faced by AI coding assistants transitioning from code completion to engineering delivery. Therefore, rather than debating whether a specific product is good or not, it is better to clarify: what kind of AI coding tool do we actually need? In what dimensions should it meet usability standards?

2. Task Definition: A 10-Page React E-commerce Site

To objectively evaluate the practical capabilities of different AI coding tools, we defined a relatively complete frontend task: building a React-based e-commerce site containing the following 10 pages:

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 Center - Dashboard
8. User Center - Orders
9. User Center - Wallet (Coupons)
10. User Center - Settings

On the surface, this is simply a quantitative stacking of pages. However, the core challenges of this task lie in:

• The completeness and overall consistency of the site;
• Routing relationships and navigation logic between pages;
• Simulation and persistence of user state;
• Organization of product data;
• Form validation, interaction feedback, and edge case handling;
• Responsive layout and basic visual standards.

Many tools can generate 10 isolated static pages. But to link these 10 pages into a “usable e-commerce site,” the AI tool must possess a certain degree of project planning capability, state management awareness, and component reusability logic.

3. Evaluation Dimensions: How to Judge Tool “Usability”

In this task, we evaluated the AI-generated results across the following dimensions:

1. Task Completion Rate

Can it generate all 10 pages at once? Does it require multiple supplementary prompts? Can it run directly after generation? Are there obvious errors?

Completion is the first threshold. If the generated project cannot even run, discussing code quality is pointless.

2. Page and Interaction Completeness

• Is the shopping flow a closed loop: Browse products -> View details -> Purchase -> Success page?
• Do login and registration have basic form validation?
• Do the User Center sub-pages have actual content?
• Are button states, empty states, and prompt messages reasonable?

There is a massive gap between “looks like a page exists” and “the page actually works” in many AI-generated projects.

3. Visual Consistency

• Are the primary color, fonts, and button styles unified?
• Is the layout structure coordinated across pages?
• Is there basic adaptation for mobile and desktop?

Visual consistency is a crucial reflection of a frontend project’s professionalism. If every page has a different style, the user experience will be highly fragmented.

4. Code Structure and Maintainability

• Are styles separated from components?
• Is there a reasonable directory structure?
• Is mock data managed independently?
• Are there extension points reserved for state management, Hooks, Context, etc.?
• Are there meaningless nested directories or duplicate files?

Code structure determines whether a project can be iterated continuously. If the generated result is a “one-time toy,” the subsequent development cost will be exceptionally high.

5. Generation Speed and Interactive Experience

• How long does it take from inputting a prompt to generating the complete project?
• Does it support multi-round fixes?
• Does the fixing process introduce new problems?
• Is the conversational interaction smooth?

Speed is important, but not at the expense of quality. A good AI coding assistant should strike a balance between “fast” and “stable.”

4. Typical Issues Found in Testing

During actual testing, different AI coding tools showed varying performances, but several common issues are worth noting and discussing.

Issue 1: Many Pages, but Broken Flows

Some tools generated all 10 pages completely, but there were obvious breakpoints in the routing navigation between pages. For example, users could enter the detail page from the product list, but the “Buy Now” button on the detail page did not correctly navigate to the purchase page; or the purchase success page lacked an entry point to “Return to Home.”

This indicates that during generation, the AI focuses more on the completeness of individual pages rather than the coherence of the overall flow. For users, this kind of site cannot complete real shopping behaviors.

Issue 2: Widespread Inadequate Responsive Adaptation

In a mobile-first era, generated results often skew towards desktop layouts. Some tools generated pages where elements overlapped on narrow screens, forms stretched and deformed, and buttons misaligned. Even when basic adaptation was implemented, it was often “scaling” rather than “reflow.”

Responsive adaptation requires the AI to possess a certain level of CSS planning capability, including the rational use of Flex/Grid layouts, setting media query breakpoints, and interaction trade-offs across different devices. Currently, this remains a weak point in AI-generated frontend code.

Issue 3: Severe Image Placeholder and Data Coupling

Almost all generated results used placeholder images. This is understandable, as real product images require external resources. However, the problem is that placeholder images often fail to reflect the actual attributes of the products, causing the information on the detail page to be completely disconnected from the list page.

More seriously, product data is usually written directly inside the components rather than extracted into independent data files or mock services. This means that when connecting to real APIs later, developers have to modify the code page by page, resulting in a massive workload.

Issue 4: Unreasonable Directory Structure

One tool created same-name nested directories in the project, such as creating a components folder inside src/components. This structure provides no organizational value and instead increases the complexity of import paths.

Another tool created directories like hooks, context, and services, but they only contained empty files or placeholder comments. On the surface, it appeared “engineered,” but in reality, these design patterns were not actually utilized.

Issue 5: Limited Fixing Capabilities

When errors occurred in the generated project, some tools could fix them based on the error messages, but the process was often mechanical. Some fixes introduced new syntax errors, while others repeatedly modified the same block of code without fundamentally solving the logical issue.

This shows that current AI coding assistants still fall short in “understanding the project globally” and are better at handling local code generation rather than making global architectural adjustments.

5. What Makes an AI Tool Suited for “Project-Level Generation”

Considering the above issues, we believe that an AI coding tool suitable for “0-to-1 project generation” should possess the following capabilities:

1. Task Decomposition and Planning

Instead of generating all 10 pages at once, it should first break the task down into steps: “Routing structure -> Page skeleton -> Common components -> Data models -> Page content.” The resulting project structure will be clearer and easier to modify later.

2. Emphasis on Independent Mock Data Management

Good generation results should extract product data, user data, and order data into independent mock modules, providing them to components through a unified service layer. This way, even when replacing them with real APIs later, the component logic does not need to be altered.

3. Basic Design Standard Awareness

At a minimum, it should unify basic variables like primary color, fonts, button border radius, and spacing. Capable tools can also generate a simple theme file, allowing developers to quickly adjust the overall style.

4. Multi-round Fixing with Context Memory

Fixing capability is one of the core values of an AI coding tool. A good tool should be able to make incremental modifications based on previously generated content, rather than regenerating the entire project from scratch every time.

5. Transparent and Controllable Interaction

Developers should be able to see the AI’s generation plan, execution steps, and file change logs. This helps determine if the AI is “going off track” and allows for timely intervention when necessary.

6. Selection Criteria: From “Usable” to “Good to Use”

When selecting an AI coding tool, developers should focus on the following points:

1. Network Accessibility

Whether a tool can be used directly in a standard network environment is a fundamental threshold. Developers should not spend time dealing with network configurations.

2. Prompt Understanding

Prompt descriptions often contain colloquial expressions and business terminology. AI tools need to accurately understand these descriptions and translate them into reasonable page structures and interaction logic.

3. Maintainability of Results

If the generated code cannot be maintained, it is only useful for temporary demonstrations. For real business applications, maintainability is far more important than “looking good.”

4. Integration with Existing Workflows

Can the AI tool export a standard frontend project structure? Is it compatible with common code management workflows? These factors will impact its actual effectiveness within a team.

7. Conclusion

Available AI coding tools are maturing rapidly. Although they are still far from “completely replacing human labor,” they already significantly improve efficiency in scenarios like prototype building, page generation, and repetitive code writing.

The biggest takeaway from this e-commerce site test is: do not just look at how many pages the AI generated, but rather whether these pages can actually be linked into a usable business flow. A simple project with a clear structure, separated data, and a closed-loop process is far more valuable than a complex project with numerous pages but hollow logic.

When choosing an AI coding tool, developers should prioritize “engineering capabilities.” After all, the code generated by AI will ultimately be maintained and iterated by humans. Only those tools that can output maintainable code, support continuous fixing, and possess architectural awareness are truly worth integrating into the daily development workflow.