AI Coding Tools in 2026: A Hands-On Evaluation of Building an E-Commerce Site from Scratch

As AI coding tools evolve from simple code completion to task-level generation, more developers and teams are exploring whether natural language can drive the creation of an entire project. In China, where network accessibility and compliance are critical factors, finding a tool that works reliably without additional configuration has become a key selection criterion.

This article is not a promotion of any commercial product. Instead, it draws on a relatively complete hands-on test—generating a ten-page React front-end shopping site from scratch—to examine the performance, common issues, and improvement opportunities of AI coding tools currently available in China. Tool names are used only as test subjects and do not imply endorsement.

Why an e-commerce site?

An e-commerce site was chosen because it is both familiar and comprehensive.

Most developers understand its core business logic: product listings, product details, checkout flows, login and registration, user dashboards, orders, wallets, and settings. At the same time, it places hidden but important demands on generation quality:

• Cross-page consistency: Ten pages need to share a unified layout, color scheme, navigation, and interaction patterns. They should not feel like ten independent pages stitched together.
• End-to-end flow: From browsing to purchase and order tracking, the entire user journey must form a working business loop.
• Forms and state management: Login, registration, and checkout involve form validation, data flow, and state persistence.
• Styling and responsiveness: Even without complex adaptation, the site should render properly across common screen widths.
• Project structure: Code layering, style separation, and mock data isolation directly affect long-term maintainability.

These requirements make it possible to assess whether a tool can handle task-level generation, rather than merely producing usable code snippets.

Test task and prompt design

To keep the test as fair as possible, all tools received the same prompt:

Use React to build a front-end shopping site. The site should include 10 pages: home page (product listing), product detail page, product purchase page, purchase success page, user login page, user registration page, user dashboard, order page, wallet (coupon/card pack) page, and settings page.

The prompt intentionally omitted UI framework, routing solution, state management tool, and project directory structure. This mirrors a common real-world scenario: many developers do not provide full technical constraints on the first attempt. They want the tool to generate a runnable version first, then refine it gradually.

The open-ended prompt revealed clear differences in default decision-making among the tools. Some prioritized delivering a simple runnable project quickly. Others attempted a more engineering-oriented structure. Some made substantial design assumptions within a single task.

Tools included in the test

The test covered four AI coding tools that are accessible and usable in China. To avoid unnecessary brand comparison, they are referred to as Tools A, B, C, and D.

Tool A

Tool A offered a friendly interface and clear interaction guidance. In task mode, users could request a full project using natural language. After receiving the prompt, Tool A generated the complete project in a single session and automatically entered the build and preview phase.

Generated results:

• All ten pages were present and accessible.
• The main shopping flow worked, and basic forms had some validation.
• The overall design leaned toward mobile, with simple responsive handling.
• Login and registration pages had element misalignment at certain screen widths.
• Product images were all placeholders, and detail pages contained limited information.
• The color scheme was restrained and consistent, though not particularly distinctive.

Code quality:

• Styles and scripts were mostly separated.
• The project structure was simple, without complex layering.
• Mock data and business logic were coupled and embedded in component files.
• Readability was acceptable and beginner-friendly.

Summary: Tool A suited scenarios where quickly seeing a working result mattered most. It did not attempt to generate a heavy project. Its priority was to make the project runnable and the core flow complete. The trade-off was limited engineering depth, which may require restructuring and data refactoring later.

Tool B

Tool B’s interface resembled a well-known AI coding editor. It provided multiple modes, including a standard chat mode and a task generation mode. The test used task generation mode.

Generated results:

• The first generated project could start but produced several runtime errors.
• After multiple rounds of fixes, the core pages rendered correctly.
• Product detail pages were richer than those from Tool A.
• Form validation was relatively complete.
• Mobile adaptation was poor. Some buttons had foreground and background colors that made text nearly invisible.
• Images were placeholders.
• Color choices varied across pages, resulting in moderate visual inconsistency.

Code quality:

• Overall code quality was acceptable.
• Styles and logic were partially separated, but some styles were not extracted into dedicated files.
• Placeholder structures such as hooks and context directories existed, but their contents were empty.
• The project ran, but it still felt incomplete.

Summary: Tool B showed potential but required significant human intervention. It attempted a better structure, yet the level of detail was insufficient. Developers need some experience to repair the generated code efficiently.

Tool C

Tool C included multiple built-in agents, each with a different task strategy. One agent emphasized generation speed, while another claimed to handle more complex architecture tasks.

Fast mode:

• Generation speed was relatively quick.
• The project directory structure had obvious issues, such as duplicated folders with the same name nested inside each other.
• The project ultimately failed to start.

Complex task mode:

• Response speed was slower.
• The final project started but produced many runtime errors.
• After multiple repair attempts, it still did not reach a usable state.

Summary: Tool C showed low completion quality in this test. The multi-agent design was forward-looking in concept, but task boundaries and engineering standards were not yet stable. For generating a complete project from scratch, reliability still needs significant improvement.

Tool D

Tool D leaned toward enterprise scenarios, with a more formal interface than Tool A. The test also used task generation mode.

Tool D did not produce a stable runnable result in this test, so it is not discussed in detail. A single test cannot fully represent a tool’s true capabilities, especially when results are heavily influenced by model version, prompt details, and environment configuration.

Common patterns and issues

Despite differences in performance, the test revealed several shared weaknesses in project-level generation.

1. Heavy reliance on placeholder images

Almost all tools used placeholders or solid color blocks for product images and avatars. This is understandable: models cannot generate real product photos, and calling external image libraries involves copyright and stability concerns. However, from a user’s perspective, an e-commerce site filled with placeholders leaves a poor impression.

This suggests that AI coding tools are better suited to generating structure and logic. Visual assets still require manual sourcing or integration with object storage and other infrastructure.

2. Mock data coupled with business logic

Several tools wrote mock data directly inside components or mixed it with business logic. For a demo, this is acceptable. For a real project that requires continuous iteration, this coupling significantly increases maintenance costs.

A better approach is to place mock data in a dedicated mock directory or data layer, then inject it through interfaces or context. This reduces the scope of changes when replacing mock data with real APIs.

3. Inconsistent styling and weak responsiveness

Cross-page consistency was one of the biggest challenges in this test. Some tools generated pages with inconsistent color schemes, button styles, spacing, and component designs. The same button could look different across pages. Responsiveness issues were even more common: some pages worked on desktop but broke on mobile, or vice versa.

This reflects a current weakness in global design decision-making. Models are better at local generation than at maintaining a design system across multiple pages.

4. Project structures that look complete but are not

Some tools generated directory structures containing hooks, context, and services folders. At first glance, the project appeared highly structured. Upon inspection, these directories were empty or contained only placeholder comments. This structural illusion is particularly unhelpful for beginners, creating a false sense of completeness while significant work remains.

5. Repair ability weaker than generation ability

The test repeatedly showed a pattern where generation worked, but repairs made things worse. Models could quickly produce large amounts of code, but their success rate dropped noticeably when understanding errors, locating issues, and making precise fixes. This remains a key challenge for AI coding tools: generation moves from 0 to 1, while repair moves from 1 to 1.1, requiring stronger context understanding and state tracking.

How to use AI coding tools more effectively

Based on the test experience, here are some practical suggestions.

1. Avoid overly open prompts

For more controlled results, specify the following in your prompt:

• Tech stack version, such as React version and whether to use TypeScript.
• Routing solution, such as React Router or an alternative.
• Styling approach, such as CSS Modules, Tailwind CSS, or plain CSS.
• Data placement, such as putting all mock data in a dedicated mock directory.
• Layout preference, such as desktop-first or mobile-first.

For example:

Use React 18 + TypeScript + React Router 6 + CSS Modules to build a desktop-first shopping site. Place all mock data in the src/mock directory. Components should not use inline mock data. Include the following ten pages…

This can significantly reduce randomness in the output.

2. Treat “runnable” as the first milestone

Do not expect a production-ready project from the start. A more pragmatic approach is to generate a minimal runnable version first, then manually optimize structure, decouple data, and unify styles. Think of AI tools as draft generators rather than complete delivery systems.

3. Know when to intervene manually

Manual involvement is recommended for:

• Directory structure design.
• Data layer abstraction.
• Global styles and design variables.
• Routing and access control.
• Maintainability of critical business logic.

These areas often require team conventions and long-term planning. They are not ideal for leaving entirely to the model.

4. Take code review seriously

Generated code can look reasonable without being truly correct. Conduct regular reviews, focusing on:

• Unused dependencies or empty directories.
• Duplicate code.
• Potential memory leaks or state management issues.
• Unnecessary complexity.

What to expect next

Based on this test, AI coding tools in China have reached basic usability for task-level project generation, but they are still iterating quickly. Key areas to watch include:

• Stronger global consistency control: Ten pages should share the same design variables and layout conventions rather than being generated independently.
• Better handling of visual assets: Integration with object storage and image services could reduce the placeholder problem.
• More reliable repair workflows: Moving from generation plus patching to a closed loop of generation, self-checking, fixing, and regression verification.
• Clearer engineering boundaries: Defining which directories and files are generated by the model and which are maintained by developers.
• Enterprise integration capabilities: Deeper integration with code repositories, pipelines, permission systems, and cloud development environments.

Final thoughts

The competition among AI coding tools is no longer just about who completes code faster. It is about who can reliably deliver usable results in more complex scenarios. For a ten-page e-commerce site, generating a runnable project with a working flow and readable code in one pass is already a meaningful threshold. Achieving visual consistency, sound structure, and extensibility still requires close collaboration between tools and developers.

For developers, choosing a tool is not only about a single generation result. It is also about how well the tool fits into your workflow, how well it respects team conventions, and how stable it remains across iterations. Test results are only a reference. The real answer must be validated through repeated use in actual projects.