2026 AI Coding Tool Selection Guide: Building an E-Commerce Site from Scratch

As AI-powered coding tools become increasingly embedded in development workflows, many teams face a practical question: which intelligent programming environments are both stable and fully capable when overseas tools are unavailable? This article does not promote any specific commercial product. Instead, it provides an engineering-oriented evaluation of several categories of AI coding tools currently accessible in China, based on a concrete development task.

Evaluation Task and Constraints

To make the comparison meaningful, a unified task was defined: build a React-based e-commerce frontend from scratch, covering 10 core pages—homepage with product listing, product details, purchase flow, login/registration, user center, orders, wallet, and settings.

Although the task appears conventional, it introduces multiple challenges for AI coding tools. First, the number of pages places significant pressure on context management; the tool must maintain consistency in routing, component relationships, state transitions, and visual style throughout the entire generation process. Second, frontend engineering details—form validation, responsive layout, and interactive states—require results that are not only runnable but also practically usable. Third, code maintainability matters: whether components are reasonably split, styles are decoupled from logic, and mock data is independently managed directly affects downstream development efficiency.

A shared prompt was used across all evaluated tools. The prompt explicitly required a React-based shopping site and listed all 10 pages. The evaluation focused on four dimensions: interface interaction experience, one-shot task completion rate, generated code structure, and runtime reliability with visual consistency.

Interface Design and Task Execution Models

Notable differences exist in interface design philosophies among the tools. Some favor a lightweight, approachable interaction style, using visual elements to reduce the cognitive burden of complex tasks. This design tends to be friendlier for developers new to AI coding tools, but interface warmth does not compensate for task execution effectiveness.

In terms of execution modes, most tools now offer multiple options. Some adopt an “automatic execution” approach, attempting to handle everything from planning to code generation in one pass. Others retain more manual confirmation checkpoints, splitting the generation process into controllable steps. In practice, automatic mode significantly improves efficiency when the task goal is clear and page structures are standard. However, when design decisions or multiple implementation paths are involved, auto-generated solutions tend to be conservative and may omit critical interactions.

Some tools further segment execution modes into levels, such as fast generation versus deep planning. Fast mode suits prototype validation, while deep mode claims to handle more complex tasks. In this e-commerce evaluation, deep mode produced a more complete project skeleton but did not demonstrate stability proportional to its positioning in terms of detail implementation and error correction.

Page Fidelity Analysis of Generated Results

All evaluated tools met the basic requirement of generating 10 pages, but quality varied significantly.

The homepage product listing is the primary entry point for users. Its information density and visual hierarchy directly influence user engagement. Better-performing tools generated layouts that included product cards, category filters, and basic navigation, though placeholder images were commonly used, lowering visual completeness. Product detail pages showed even wider variance. Some tools generated only basic title, image, and price information, while more complete implementations included specification parameters, user review sections, and related product recommendations—substantially increasing information richness.

The purchase flow requires multi-step transitions across selection, confirmation, payment, and success states, demanding robust state management and routing design. Most tools completed basic flow chaining, but edge-case handling was often missing—stock insufficiency warnings, duplicate submission prevention, and payment failure fallbacks were rarely covered. Login and registration pages highlighted another differentiator: full frontend validation should include email format, password strength, confirmation password matching, and phone number checks. Some generated results only presented the visual form, leaving validation logic at framework defaults.

User center pages exposed broader engineering shortcomings. Order pages typically need to interact with product data, user state, and payment results, but many implementations hard-coded order data inside components, resulting in no actual data flow between order lists and the purchase workflow. Wallet and coupon pages suffered from the same data isolation issue, with most implementations remaining static.

Responsive design was a common weak point. Mobile-first design is particularly important in e-commerce scenarios, but some generated pages exhibited layout compression, button text overflow, and form control misalignment on narrow screens. One tool even produced buttons where background and text colors lacked sufficient contrast, affecting basic usability.

Code Quality and Maintainability Assessment

The value of AI coding tools lies not only in generating code, but in whether developers can efficiently take over the result. This evaluation assessed code quality from four angles: directory structure, style management, data layering, and extension readiness.

In directory structure, better tools separated components, pages, and style files, enabling developers to extend functionality on the existing foundation. Other tools generated redundant nesting—for example, duplicate same-name folders inside page directories—causing path resolution ambiguity and, in some cases, preventing the project from starting.

Regarding style management, none of the tools adopted mature styling solutions, but separation levels differed. Keeping styles in separate files is more conducive to team collaboration and theme switching, while inline styles are more efficient during generation but significantly increase maintenance costs.

Data layer design was the most common weakness. Mock data was tightly coupled with business logic in nearly all outputs. Product lists, user information, and order records were written directly into components or page files rather than being isolated in a data service layer. As a result, integrating a real backend requires repetitive modifications across multiple files, increasing regression risk.

On extension readiness, some tools created placeholder directories for state management and custom hooks, but the modules were empty and lacked usable abstractions. While this skeleton approach reflects awareness of best practices, it still falls short of a genuinely usable engineering foundation.

Execution Efficiency and Stability Observations

Execution efficiency includes two aspects: generation speed and error correction capability. Generation speed depends on cloud inference resources. During this evaluation, most tools responded reasonably smoothly without noticeable lag. However, one tool showed significantly longer generation times as task complexity increased, with some deep-mode runs approaching the time required to manually write the equivalent code—diminishing its efficiency advantage.

Stability proved more critical. Some tools generated projects with complete structures at first glance, but runtime errors were frequent, requiring multiple rounds of conversational fixes before reaching a demonstrable state. The repair process itself is an important experience factor. Tools that could quickly locate errors and apply fixes based on prompts performed significantly better than those that failed to converge after repeated attempts. A representative failure case emerged in this evaluation: a generated project started successfully, but multiple core pages contained blocking errors, and after several repair rounds the issues remained unresolved, leading to a final classification of unusable.

Selection Recommendations and Engineering Considerations

Based on these observations, China-based AI coding tools in 2026 have achieved basic capability for medium and small-scale frontend tasks, but there remains a clear gap from “generate and deliver.” Different types of developers should adopt differentiated usage strategies.

Teams needing to quickly validate product prototypes can prioritize tools with friendly interfaces and higher one-shot completion rates, focusing on workflow coverage and visual presentation. For medium-to-large projects with established engineering conventions, attention should shift to the generated code’s directory structure and data layering capability, treating the output as a “first draft” rather than a “final product,” and performing structural refactoring before integration.

Regardless of the chosen tool, several engineering practices are broadly applicable. First, specify code organization requirements in the prompt—such as component granularity, style management, and data service layer design—rather than only describing page functionality. Second, establish manual code review checkpoints, focusing on routing configuration, state transitions, and data dependencies. Third, perform build verification and basic test coverage on generated code to avoid bringing runtime errors into subsequent development stages. Fourth, pay attention to the separation between mock data and business logic; complete the data layer abstraction before integrating a real backend.

The evolution of AI coding tools is shifting from “code completion assistance” toward “task-level engineering collaboration.” At the current stage, human judgment and engineering constraints remain irreplaceable for ensuring project quality. Using these tools rationally, while preserving necessary human intervention, is the sustainable path to balancing efficiency and quality.