Practical Benchmarking of AI Coding Tools for Building a Full E-Commerce Frontend

Introduction: When AI Coding Tools Enter the “Usability” Era

Over the past two years, AI coding tools have evolved rapidly from simple code completion to task-level generation. Among the common technical approaches in the industry, one category consists of lightweight IDE plugins that provide conversational assistance and autocomplete. Another category is standalone IDEs that can generate an entire project from a single sentence. These latter tools—by reading the full project context, creating files automatically, and executing commands—have become important assistants for developers building prototypes and validating ideas.

A practical concern has emerged for many teams: while overseas tools may be powerful, some are inconvenient to use in certain network environments, and their support for Chinese-language contexts and domestic technology stacks can be limited. As a result, AI IDEs that can be used directly without extra network configuration have drawn significant attention. This article does not promote any specific commercial product. Instead, it focuses on a more general technical question: When a complex task such as “building a complete e-commerce site from zero to one” is assigned to different forms of AI coding tools, where do their actual performance differences lie, and how should we choose the appropriate working mode based on task characteristics?

To ensure comparability, we define a unified task and a unified prompt, observing only the quality and engineering usability of the output under identical input conditions.

Evaluation Design: Consistent Task, Consistent Prompt

Task Definition

Build an e-commerce site from scratch. The frontend should use a component-based framework (React as the representative). It must cover 10 pages:

1. Homepage (product list)
2. Product detail page
3. Product purchase page
4. Purchase success page
5. User login page
6. User registration page
7. User dashboard
8. Order history page
9. Wallet / coupon page
10. User settings page

Core Difficulties

• Site coherence and consistency: The 10 pages are not isolated; they share navigation, state, and a data model as an integrated whole.
• Reasonable project structure: Directory organization, separation of styles and logic, and decoupling of mock data from business code.
• Interaction details: Form validation, route transitions, responsive adaptation, and consistency of buttons and color schemes.

Observation Dimensions

We recorded performance across five dimensions: task completion rate in one pass, code quality (structure/style separation/data coupling), interface completeness and responsiveness, runnability and repairability, and development efficiency.

Performance of Four Tool Forms in Practice

Form A: User-Friendly Standalone IDE (Automated Execution Mode)

This type of tool features a consumer-oriented interface design that is visually less harsh, lowering the barrier for first-time users. Its “automated execution” mode—understanding the task in one shot, then continuously creating files and executing commands—stood out in our test: a single prompt completed the entire site, with smooth service responses and no noticeable lag.

Characteristics of the generated output:

• A unified visual style carried by a single primary color; layout is generally mobile-first with basic responsive adjustments.
• The shopping flow is a closed loop, including basic form validation.
• Code separates styles from scripts, and quality is acceptable.
• Shortcomings: image assets are placeholders; product detail pages have low information density; login/registration pages show layout distortion at certain widths; the architecture is overly simple, with mock data coupled directly into business code, making later extension require refactoring.

Technical insight: Automated execution mode is suitable for “quickly obtaining a runnable prototype.” However, for scenarios requiring strong engineering discipline, an additional round of “structural refactoring” prompts is needed—for example, requesting “extract all mock data into a separate data layer and split directories by feature.”

Form B: Standalone IDE with Classic Interaction (Builder Mode)

This tool form inherits the interaction paradigm of established AI IDEs and offers options such as privacy mode. Its builder mode also supports task-level generation, but in this test, the first output was runnable yet threw errors, and it took multiple repair rounds to stabilize.

Characteristics of the generated output:

• Form validation is relatively complete, and the product detail page carries more information than Form A.
• However, the overall visual quality is rough, mobile responsiveness is missing, and some buttons suffer from “white text on white background,” rendering text invisible.
• Color choices lack unified design constraints, and images remain placeholders.
• Code quality is acceptable; styles are separated but not organized into standalone files. The directory structure reserves places for state management and context, but these are left empty—“holes reserved but not filled.”
• Development speed is slower; the free version results in noticeable waiting during long tasks.

Technical insight: Reserved directory structures are useful, but empty implementations mislead future maintainers. When using such tools, explicitly require in the prompt that “all reserved directories must contain a minimal working implementation and must not be left empty.”

Form C: Multi-Agent or Multi-Mode Tools

These tools offer several agents or modes to choose from, which can create “choice fatigue” for first-time users. In practice:

• Fast generation mode: Produces quickly, but the project structure is chaotic, with nested directories of the same name (a folder containing an identically named folder), ultimately causing the project to fail startup.
• Architecture planning mode: Claims to handle complex tasks but responds more slowly; the generated project runs, but with numerous errors. We abandoned it after multiple repair rounds.

Technical insight: Multi-mode design is reasonable—fast mode for prototypes, planning mode for complex engineering. However, the output of different modes should maintain structural consistency; otherwise, the migration cost when switching between modes is high. For developers, in complex tasks, prioritize a “plan first, generate second” workflow and run build validation immediately after each step. This significantly reduces the probability of “generated but unable to run.”

Form D: Plugin-Based Smart Coding Assistant

This form provides conversation, completion, and task execution as an IDE plugin. Its strength is integrating into an existing workflow without changing development habits. In the “full-site generation” task, the plugin approach as a whole is better suited to “piecewise generation and sequential assembly”: the tool generates skeleton pages and components first, and the developer then connects routing and state. Its limitation is weaker support for “generating a complete project in one pass” compared to standalone IDEs, requiring more granular prompt decomposition.

Technical insight: The correct usage of a plugin is not “build a site with one sentence,” but “advance module by module”: first define directory conventions and data contracts, generate page by page, and finally integrate styles and routing in a unified pass.

Horizontal Comparison: The Engineering Logic Behind the Differences

We summarize the above observations in a comparison table (qualitative descriptions):

Dimension	Form A (Automated)	Form B (Builder)	Form C (Multi-Mode)	Form D (Plugin)
One-pass completion	High	Medium (multiple repairs needed)	Low–Medium	Depends on decomposition
Code structure	Simple but clean	Reserved but empty	Prone to chaos	Follows existing project
Interface consistency	Good	Weak	Unstable	Prompt-dependent
Responsive design	Basic but usable	Missing	Partially missing	Needs manual additions
Data decoupling	Weak (coupled)	Weak	Weak	Controllable
Efficiency	Fast	Slow	Medium	Medium

A notable commonality is that all four tool types are generally weak at decoupling mock data from business logic, handling responsive details, and replacing placeholder assets. This suggests that current AI coding tools excel at “structural generation” but still require human intervention for “engineering discipline and design fidelity.” This is not a flaw; it is a reasonable division of labor—the tool assembles the skeleton, and the developer controls the quality.

Reusable Practical Methods

Layered Prompt Design

Do not pack all constraints into one sentence. Use three layers instead:

[Role and Goal] You are a frontend engineer. Generate a runnable e-commerce frontend project.
[Structural Constraints] Organize directories by feature module. Place all mock data in a dedicated data layer. Separate styles from components.
[Acceptance Criteria] All 10 pages must be reachable via routes. Forms must include basic validation. Button colors and contrast must meet readability standards. No layout overflow at mobile widths.

The benefit of layering: structural constraints and acceptance criteria are “checkable,” so the tool is more likely to self-align during generation.

Immediate Build Validation

Regardless of tool form, the first thing after generation is to run the build and start commands, record the list of errors, and feed them back to the tool as “one-by-one fixes.” In practice, a project that runs but reports errors often stabilizes after two to three rounds of targeted repairs.

Post-Generation Data Layer Refactoring

If mock data is coupled into components, append a follow-up prompt: “Extract all mock data into a standalone module. Components must reference it via imports, with no inline hardcoded values.” This single step significantly improves maintainability.

Design Constraints Upfront

Button visibility and color issues essentially stem from missing design tokens. Prompt the tool to “define a set of color variables and button style classes, and ensure all pages reference them consistently.” This prevents “white-on-white” issues at the source.

Selection Recommendations: Match Tool Form to Task Characteristics

• Validating an idea or prototyping quickly: Prefer standalone IDEs with automated execution and one-pass generation. Optimize for speed, not perfection.
• Requiring engineering standards and long-term iteration: Choose plugin-based forms or modes that support stepwise planning. Define standards first, generate second, and conduct manual reviews.
• Complex multi-page systems: Adopt a “plan architecture first, generate module by module, integrate last” workflow to avoid directory chaos from large one-shot tasks.
• Team collaboration: Regardless of tool, route generated outputs through code review, focusing on data coupling, directory conventions, and responsive behavior.

Conclusion

AI coding tools that are directly usable in domestic environments have crossed the “can they work” threshold and entered the “how to use them well” stage. This evaluation shows: no single tool can deliver production-grade code in one pass for complex tasks, but a reasonable workflow and a disciplined prompting strategy can multiply the efficiency of producing a usable prototype. For developers, the real competitive advantage is not remembering which tool is stronger, but mastering a transferable methodology: task decomposition, structural constraints, build validation, and post-generation refactoring. Tools will iterate; the methodology remains portable. As for whether future tools will adopt more human-like interaction details—that is an evolution of product experience. For engineering practice, managing and using generated output well is the most worthwhile investment today.