Technical Selection Guide for AI Coding Tools: Building an E-commerce Site from Scratch

1. Background and Problem Definition

As Large Language Model (LLM) driven AI coding tools rapidly evolve, developers increasingly expect to describe requirements in natural language and receive runnable code in return. However, in practical applications, the capability boundaries, generation quality, engineering standards, and interactive experiences of these tools vary significantly. In certain network environments, accessing specific overseas tools can be restricted or unstable, making a reliably accessible AI coding tool a primary requirement for many development teams.

This article does not endorse or review specific commercial products. Instead, using the task of “building an e-commerce site from scratch” as a typical multi-page, high-consistency scenario, it abstractly discusses the common performance of AI coding tools across five dimensions: requirement understanding, project structure generation, code quality, UI fidelity, and maintainability. It also provides reusable evaluation methods and engineering practice recommendations.

2. Task Definition: A Measurable E-commerce Site

To facilitate horizontal comparison, we define a target task with clear boundaries:

Prompt: Build a frontend shopping site using React, comprising 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 Dashboard
8. User Orders Page
9. User Wallet (Coupons)
10. User Settings Page

Core Challenge: The completeness and overall consistency of the site. Ten pages imply shared navigation, unified visual standards, route organization, state management, and proper separation of mock data. A missing link in any of these aspects will result in a project that “runs but is unusable.”

The value of this task lies in its realism: it is neither a single-file script nor an entirely open-ended prompt, but a minimal complete closed loop close to real-world business scenarios. A tool’s performance on such tasks fully exposes its true capabilities in architectural awareness and engineering constraints.

3. Evaluation Dimensions

3.1 Interactive Experience and Task Orchestration

Observation shows that AI coding tools generally exhibit three task orchestration modes:

• One-shot Generation Mode: The tool completes a large scope of generation in a single turn based on user requirements. The advantage is high efficiency and contextual coherence; the risk is concentrated rework costs if the output deviates from expectations.
• Step-by-step Construction Mode: The tool breaks the task into multiple steps, generating progressively and allowing intermediate intervention. The advantage is controllability; the disadvantage is the potential loss of global consistency between steps, resulting in “locally correct, globally fragmented” outputs.
• Multi-Agent Mode: Multiple selectable Agents are provided, targeting scenarios like rapid generation or complex task planning. The advantage is clear division of labor; the disadvantage is an increased selection cost, and output quality can vary significantly across modes.

From an engineering perspective, no mode is inherently superior; the key is whether the tool explicitly enforces “multi-page consistency.” For example, whether it unifies the routing table, reuses the same layout components, and abstracts mock data into independent modules. These details matter more than “generation speed” in determining whether the project can continue to evolve.

3.2 Project Structure and Code Quality

In multi-page tasks, common structural issues include:

• Styles and logic are not separated, or are separated but not placed into independent files;
• Mock data is hardcoded inside components, leading to high coupling;
• Directories like hooks and context are reserved but empty, creating a “looks standard but practically unusable” pseudo-structure;
• Chaotic directory hierarchies or nested folders with identical names, causing build failures.

A maintainable React project should at least meet the following baseline:

src/
  components/      # Reusable UI components
  layouts/         # Page skeleton and navigation
  pages/           # Route-level pages
  hooks/           # Custom logic
  context/         # Global state
  services/        # Data access layer (including mock data)
  routes/          # Centralized routing configuration
  styles/          # Global styles and variables

If the tool-generated project lacks critical layers in the above structure, the maintenance cost will increase non-linearly for every subsequent page added. Therefore, during evaluation, priority should be given to checking whether the data layer is independent, whether layouts are reused, and whether routing is centralized, rather than merely assessing if the first screen “looks good.”

3.3 UI Fidelity and Responsiveness

E-commerce sites are inherently multi-device. Common UI issues in generated results include:

• The design is heavily skewed towards mobile, with only simple stretching for desktop;
• Form pages like login/registration suffer layout deformation on narrow screens;
• Insufficient color contrast for buttons, resulting in invisible white text on a white background;
• Images uniformly use placeholders, resulting in low information density on detail pages.

It should be emphasized that placeholders themselves are not defects—using placeholder images during the prototyping phase is standard practice. What truly affects usability is whether form validation is complete, whether interaction states are comprehensive (loading, empty state, error prompts), and whether visual standards are unified. A prototype with restrained color usage and clear structure is far more valuable than a visually flashy product with non-clickable buttons.

3.4 Code Correctness and Rework Cost

“Can run” and “runs correctly” are two different tiers. In practice, a common situation is that the initial generation can start but contains runtime errors, requiring multiple rounds of fixes before seeing the complete result. Conversely, some tools generate a complete project structure that fails to build, ultimately leading to abandonment after unsuccessful fixes.

This suggests that a minimum validation checklist should be established during the evaluation phase:

1. Can the project install dependencies and start in one go?
2. Are all ten routes accessible without white screens?
3. Do form submissions include basic validation and feedback?
4. Does page navigation maintain state consistency?
5. Are there any blocking errors in the console?

Only after passing all five items should the evaluation proceed to the “UI and code quality” phase. Otherwise, any additional feature descriptions are meaningless.

4. Practice Recommendations: How to Use AI Coding Tools Effectively

4.1 Explicitly Write “Consistency” into the Prompt

Tools do not automatically understand that “ten pages should look like a single product.” It is recommended to explicitly enforce this in the prompt:

• Unify the use of the same layout components and navigation structure;
• Unify color variables and spacing standards;
• Centralize mock data under services/mock, with pages acting only as consumers;
• Centralize route registration and organize page components by directory.

Example constraint snippet:

Requirements:
1. All pages share a Layout component with a consistent navigation bar;
2. Colors and border radius are uniformly defined using CSS variables;
3. Mock data for products, orders, and coupons are centrally placed in the services/mock directory;
4. Routes are centrally configured in routes/index.jsx;
5. Login and registration pages must include mandatory validation and error prompts.

4.2 Phased Validation Instead of One-time Acceptance

It is recommended to split the task into three phases and validate each respectively:

• Phase 1: Skeleton — Routing, layout, and navigation are functional; pages contain placeholder content;
• Phase 2: Data and Interaction — List, details, form validation, and state transitions are usable;
• Phase 3: Visuals and Responsiveness — Unified color scheme, responsive breakpoints, empty states, and loading states.

Proceeding to the next phase only after the previous one passes minimizes the scope of rework.

4.3 Conduct a “Structure Audit” on Generated Code

After generation, it is recommended to immediately perform a structure audit, focusing on:

• Whether there are build hazards like nested directories with identical names;
• Whether directories like hooks and context are empty shells;
• Whether mock data is scattered within components;
• Whether styles are truly isolated into separate files.

Issues found during the audit should be prioritized for fixing, as they will compound as the number of pages increases.

4.4 Establish an Internal Tool Evaluation Baseline

For enterprise users, it is recommended to develop a lightweight evaluation rubric, scoring across five dimensions: task completion rate, structural standardization, number of fix rounds, UI consistency, and maintainability. Regular re-evaluations should be conducted. Given the rapid iteration speed of AI tools, static conclusions quickly become outdated; a systematic evaluation mechanism is more valuable than a one-time conclusion.

5. Conclusion

AI coding tools are already capable of handling the task of “generating a multi-page prototype from scratch.” However, regarding engineering consistency, structural standards, and code maintainability, developers still need to rely on explicit constraints and phased validation as a safety net. When selecting a tool, rather than focusing on “generation speed” or “visual appeal,” it is better to return to three fundamental questions: Is the generated project structure sustainable for evolution? Is the code correct and runnable? Is the rework cost controllable? Once these three aspects are clearly measured, the tool selection becomes obvious.

For developers operating in network environments where reliable accessibility is the primary concern, locally accessible AI coding tools are already sufficient to cover scenarios like prototype validation, teaching demonstrations, and the initiation of small to medium-sized projects. The true competitiveness remains rooted in the developer’s ability to decompose requirements and their awareness of engineering quality control.