Choosing an AI Coding Assistant in 2026: A Practical Guide Based on Building an E-Commerce Site from Scratch

1. Why Building an E-Commerce Site from Scratch Is a Good Test

By 2026, AI coding assistants have moved beyond the question of whether they can write code at all. The real question is whether they can collaborate reliably on realistic tasks. Instead of relying on rankings or demo videos, a better approach is to validate a tool against a business task with clear boundaries, common modules, and complete interactions.

A front-end shopping site is well suited for this purpose. It includes multi-page routing, form validation, state management, list rendering, and shopping flows. At the same time, it exposes weaknesses in code structure, responsive layout, visual consistency, and data organization.

The task described in this article assumes the following goals:

• Build a front-end shopping site with React.
• Use an AI coding assistant to generate the main pages and interactions.
• Avoid depending on a specific backend at first, and complete the flow with mock data.
• Leave room for later integration with real APIs, component libraries, and engineering standards.

On this basis, the focus is not only on whether pages can be generated, but also on four dimensions: runnability, structural soundness, maintainability, and adaptability to Chinese business scenarios.

2. Breaking Down the Task: Turning Vague Requirements into Actionable Prompts

The effectiveness of an AI coding assistant depends heavily on input quality. If the prompt is simply “write a shopping website for me,” the generated result usually suffers from missing pages, broken interactions, messy types, and poor visual control.

Therefore, before generating anything, the task should be broken down into a clear list of pages and modules.

2.1 Page Scope

The e-commerce site in this case should include at least the following pages:

• Product detail page
• Product checkout page
• Order success page
• User login page
• User registration page
• User account overview page
• User orders page
• User wallet and coupon page
• User settings page

These pages cover the most common user paths in e-commerce: browsing products, making a purchase decision, completing an order, viewing orders, and managing assets and personal information.

2.2 Functional Focus

For each page type, functional requirements should be specified rather than simply asking the assistant to draw the page. For example:

Product Detail Page

• Main product image and thumbnail area
• Name, price, and promotional information
• Variant selection
• Quantity selection
• Add to cart and buy now buttons

Product Checkout Page

• Shipping information display
• Product list
• Delivery method
• Coupon selection
• Total amount calculation

Order Success Page

• Success status indicator
• Order number
• Estimated shipping time
• Entry points for continuing shopping and viewing orders

Login and Registration Pages

• Phone number or email validation
• Verification code countdown
• Password strength hint
• Error state feedback

User Center

• Overview data cards
• Order status filtering
• Wallet balance and coupon list
• Profile and preference settings

2.3 Prompt Example

A reasonably complete prompt for the product detail page might look like this:

Use React and TypeScript to implement an e-commerce product detail page. The page should include a main product image, thumbnail switching, product name, price, promotional labels, variant selection, quantity selection, add-to-cart and buy-now buttons. Product data should be placed in a local mock file for now. Keep component boundaries clear, and do not connect to backend APIs yet. The interface should be responsive on both desktop and mobile.

This type of prompt works well because it defines the tech stack, page structure, interactions, and data boundary at the same time, reducing uncertainty in the generated result.

3. Observed Differences in Generation Styles Across Tools

Given the same task, different AI coding assistants show clear differences. This section does not evaluate specific brands, but describes common patterns from an engineering perspective.

3.1 Differences in Completeness

Some tools tend to generate the whole site skeleton at once. The number of pages is complete and the routing works. The downside is that initially the project seems to have everything, but on closer inspection:

• Some page interactions are missing.
• Form validation only handles the most basic required-field checks.
• Data is not shared across pages.
• Component granularity is coarse, making later changes more expensive.

Other tools generate more conservatively, often completing one or two pages first and then expanding gradually. The advantage is that the structure tends to be clearer. However, without clear engineering standards, this approach can also lead to inconsistent styles.

3.2 Differences in Visual Output and Interactions

The visual quality of generated code varies significantly. Some results use a reasonably unified theme color and card layout. Although simple, the result is usable. Other results may have button text with poor contrast against the background, overflowing mobile layouts, or misaligned form controls.

These issues are common in real business work, which indicates that current AI coding assistants still require manual visual confirmation and interaction review.

3.3 Differences in Data Organization

Many generated results couple mock data directly inside components, such as hardcoding an array in a product card component. This approach may be acceptable at the prototype stage, but it needs refactoring before real development.

A better approach is:

• Place mock data in a dedicated mock directory.
• Expose functions for fetching products, orders, and user information through a service layer.
• Allow components to receive data through props or state management.

4. Common Problems and Improvement Directions

4.1 Missing Responsive Layout

A shopping site must support mobile access. However, generated results often have the following problems:

• Broken grid layouts
• Misaligned buttons
• Overflowing tables
• Deformed login and registration pages on narrow screens

It is recommended to explicitly require media queries or a responsive grid system in the prompt, and to specify a minimum supported width, such as 360px.

4.2 Oversimplified Form Validation

Login, registration, and checkout flows all depend on forms. Generated code often only checks required fields, missing:

• Phone number format validation
• Email format validation
• Password strength validation
• Password confirmation matching
• Submit button disabled logic

These pieces of logic can be extracted into separate validation modules and reused across pages.

4.3 Placeholder Image Issues

Generated results commonly use placeholder images. This is acceptable for a prototype demo, but if the site is used for a presentation or acceptance review, product images, banners, and icons should be prepared in advance and stored in a unified location.

4.4 Overly Simple Architecture

Many generated projects contain only page files and a small number of components, lacking a structure such as:

src/
  components/
  pages/
  services/
  mock/
  hooks/
  utils/
  styles/

For future expansion, the directory structure matters more than the initial amount of code. Developers should take the initiative to reorganize the project after generation.

5. From Prototype to Maintainable Project: Refactoring Suggestions

AI coding assistants are better at generating a first runnable prototype than at directly producing production-grade projects. The key to real implementation lies in the subsequent refactoring.

5.1 Extract the Data Layer

Move mock data for products, orders, users, and coupons out of pages into a unified mock directory, and access it through service functions. This way, when real APIs are introduced later, only the service implementation needs to be replaced.

5.2 Build Base Components

Shopping sites contain many recurring elements, such as:

• Product cards
• Price displays
• Quantity selectors
• Empty states
• Loading states
• Bottom action bars

Extracting these elements into base components can significantly reduce duplicate code.

5.3 Unify Style Variables

Colors, border radius, spacing, and font sizes should be centralized into style variables. This avoids ending up with conflicting color schemes in the same project. Even without a complete design system, at least the primary color, secondary color, success color, and error color should be unified.

5.4 Add Route Guards

User center pages should check login state. If the user is not logged in, they should be redirected to the login page, and after successful login, they should return to the original page. This is often missing in generated prototypes.

6. Practical Constraints in Backend and Deployment

A front-end shopping site that stays at the mock data stage has limited value. To form a complete loop, the following aspects also need to be considered:

• Persistence of product data
• User authentication and session management
• Order creation and status transitions
• Payment flow
• File upload and static asset hosting

These capabilities usually depend on common cloud services. For example, object storage can be used for product images and user avatars, message queues for asynchronous order processing, and logging and monitoring services for troubleshooting checkout link failures.

When choosing an AI coding assistant, it is also important to check whether the tool can support integration with such cloud resources, rather than only generating pure front-end code.

7. Conclusion: Treat AI Coding Assistants as Collaborators, Not Replacements

This e-commerce site case shows that AI coding assistants already have strong page generation and task decomposition capabilities. However, human involvement is still needed in visual consistency, mobile adaptation, architecture design, data organization, and business boundary handling.

A more practical way to use these tools is:

1. Have a human complete the requirement breakdown and page design first.
2. Use an AI coding assistant to generate the first prototype.
3. Have developers conduct code review and engineering cleanup.
4. Continue iterating based on business feedback.

AI coding tools for Chinese-language development scenarios are maturing quickly. For developers, the most important thing is not chasing a single strongest tool, but establishing an evaluation and collaboration process that can adapt to different tools. Only by repeatedly testing tools in real business scenarios can developers find the workflow that suits them best.