AI Coding Tool Selection Guide: From Evaluation Criteria to E-commerce Site Implementation

I. Background: Why “Replacement” is a Real Topic

Over the past two years, AI coding tools have rapidly evolved from simple single-line code completion to autonomous multi-file project generation based on natural language requirements. These tools generally fall into three categories:

• IDE-Embedded: Delivered via editor plugins or standalone IDEs, focusing on integrating with existing development workflows to provide code completion, inline rewriting, and conversational modifications.
• Agent-Based: Operates at a “task” granularity, autonomously breaking down requirements, creating files, installing dependencies, and running validations. Developers act more like code reviewers.
• Cloud Collaborative: Emphasizes environment hosting and multi-user collaboration, managing runtime environments, dependencies, and code repositories uniformly in the cloud.

For developers, the first factor to consider when selecting a tool is not “who is smarter,” but usability prerequisites: stable access, network requirements, and whether the account and billing systems fit team procurement processes. This is why many teams use “usable without extra network configuration” as their initial filtering threshold.

Once usability prerequisites are met, the dimensions that truly determine the experience usually include the following. It is recommended to score each item during evaluation:

1. Task Closure Capability: Can it complete multi-file, multi-page requirements in one go, rather than just generating scattered snippets?
2. Engineering Structure Rationality: Is the directory division, style/logic separation, and data/view decoupling properly executed?
3. Consistency Control: Are color schemes, component reuse, and interaction specifications unified across multiple pages?
4. Repairability: When errors occur in the generated results, can the tool autonomously locate and fix them based on error messages?
5. Response Speed: Is the wait time for long tasks within an acceptable range, and are acceleration options available?
6. Maintainability: Is the generated code easy for humans to take over, without obvious coupling and redundancy?

The following sections will discuss these dimensions based on an actual site-building task.

II. Unified Evaluation Task Design

To make the outputs of different tools comparable, a task with clear boundaries, broad coverage, and the ability to expose engineering issues is required. An e-commerce site is an ideal choice because it naturally includes typical scenarios such as lists, details, forms, flow transitions, and state management.

2.1 Task Definition

Build a frontend shopping site from scratch using React, covering 10 pages:

Page Category	Pages
Product Browsing	Homepage (Product List), Product Detail
Transaction Flow	Purchase Page, Purchase Success Page
Account System	User Login, User Registration
User Center	Dashboard, Orders, Wallet (Coupons), Settings

2.2 Core Challenges

• Site Integrity: All 10 pages must be accessible and navigable, with no dead links or blank pages.
• Overall Consistency: A single design language must run through all pages, with consistent buttons, forms, cards, and spacing.
• Flow Closure: The path from browsing products to successful ordering must work, and the login state must be correctly passed during the flow.
• Responsiveness: There should be no obvious layout breakage at least under desktop and mobile viewports.

2.3 Prompt Suggestions

The quality of the prompt directly determines the output quality. It is recommended to explicitly state constraints in the requirement description rather than just giving a simple “build a shopping site”:

Build a frontend shopping site using React with the following requirements:
1. 10 pages in total: Homepage (Product List), Product Detail, Purchase, Purchase Success, Login, Registration, User Center-Dashboard, Orders, Wallet (Coupons), Settings.
2. Unified design language: Primary colors, border radius, spacing, and button styles must be globally consistent and extracted as common components.
3. Use React Router for routing, and the shopping flow must be fully functional.
4. Forms must include basic validation (required fields, format, length).
5. Separate styles and logic; extract mock data into independent files instead of hardcoding them in components.
6. The layout must be usable under both desktop and mobile viewports.

The key point is to write engineering requirements like “consistency,” “data extraction,” and “style separation” into the prompt. Otherwise, the tool tends to generate “runnable but hard to maintain” code.

III. Performance of Typical Tool Categories

The following describes actual test observations categorized by tool type, without pointing to any specific brand.

3.1 IDE-Embedded: User-Friendly, Higher Probability of One-Shot Success

These tools usually offer a dual mode of “conversation + auto-execution.” The auto mode (accepting a requirement and autonomously completing multi-step operations) is the most friendly to beginners because it hands over “task decomposition” to the model.

In tests, better-performing tools in this category exhibited the following characteristics:

• Interface Affinity: Clear interactive guidance, lower threshold than traditional IDEs, and more friendly to non-professional frontend developers.
• One-Shot Task Completion: In auto mode, it could generate 10 pages and run them in one go, with stable server responses and no obvious lag during long tasks.
• Usable Output: The generated interface was complete, the shopping flow worked, and basic form validation was included.

However, typical issues were also exposed:

• Mobile-First Tendency: The default layout leaned towards mobile, with only simple adaptation for desktop. Elements on the login/registration pages deformed on wide screens.
• Material Placeholders: Images were all placeholders, and the product detail page lacked information density, missing modules like specifications, reviews, and recommendations.
• Oversimplified Architecture: Although styles and JS were separated, the project lacked sufficient layering. Mock data was directly coupled in the component code, requiring significant refactoring to replace with real interfaces later.

The positioning of this type of output can be summarized as a “simple but usable” prototype-level delivery, suitable for rapid idea validation, but still a round of engineering refactoring away from production-ready.

3.2 Editor-Inherited: Low Migration Cost, but Stability Needs Improvement

Another type of tool continues the interaction paradigm of mainstream AI editors and provides compliance options like privacy mode, resulting in a low migration cost for developers with existing usage habits.

Test performances showed obvious polarization:

Pros:

• Form validation was relatively complete, with more detailed rule coverage than the previous category.
• The product detail page had higher information density and richer fields.
• Code quality was acceptable, with style separation implemented (though not extracted into independent files), and directories reserving slots for layers like hooks and context.

Cons:

• First Output Not Directly Runnable: The first task could start but threw errors, requiring multiple rounds of fixes before seeing the complete result.
• Lack of Responsiveness: The layout did not adapt to mobile viewports, leading to fatal but basic issues like white text on a white background making buttons invisible.
• Unstable Aesthetics and Colors: The overall visual was simple, lacking unified color specifications.
• High Late-Stage Modification Cost: Due to the accumulation of the above issues, manual rework was significant.
• Speed Bottleneck: Wait times for long tasks were long, making developers willing to pay for speed; some versions only offered limited quotas.

Notably, although directories reserved slots for hooks and context, the file contents were empty. This indicates that the tool possesses “structural awareness” but did not actually implement the corresponding logic—a typical “scaffold hallucination.” During acceptance, each directory must be checked one by one.

3.3 Multi-Agent: Mode Selection Becomes a New Cognitive Burden

The third category of tools offers multiple Agent modes, targeting “rapid generation” and “complex task processing” respectively. While more modes expand capabilities, they also bring selection confusion.

Test observations:

• Fast Mode: Generated quickly, but the project structure was chaotic, with nesting errors like “creating a folder with the same name inside a folder,” ultimately causing the project to fail to start. The root cause is usually the tool failing to check for directory existence before creation.
• Complex Task Mode: Claimed to handle complex requirements but responded slowly. The generated project could run but had many errors, failing to converge even after multiple rounds of fixes, leading to abandonment.

This highlights an important conclusion: The number of Agent modes does not equal capability. Evaluation should focus on “whether the same task can stably converge in the optimal mode,” rather than the length of the mode list.

IV. Horizontal Comparison: Abstracting Observations into Reusable Conclusions

The above performances can be summarized into several key conclusions for reference during selection:

4.1 First-Time Success Rate ≠ Final Usability

Some tools can generate runnable projects in one go but with simple architecture; others require multiple rounds of fixes but end up with a more complete structure. Two metrics should be distinguished during selection:

• First-Time Runnable Rate: Affects initial experience and confidence.
• Post-Rework Usability Rate: Affects actual delivery costs.

For teams, the latter should carry more weight.

4.2 “Reserved Structures” Need Verification

The appearance of folders like hooks, context, and services in the directory does not mean the logic is implemented. The acceptance checklist should include:

[ ] Does every reserved directory contain actual implementation files?
[ ] Are there empty files or placeholder files with only comments?
[ ] Is mock data centralized in an independent module rather than scattered in components?
[ ] Are there structural anomalies like nested directories with the same name?

4.3 Responsiveness and Accessibility are the Weakest Links

Issues like “white text on a white background” indicate that generative tools lack systematic constraints on color contrast and responsive breakpoints. It is recommended to explicitly require in the prompt:

• Specify breakpoints (e.g., 768px, 1200px) and layout strategies for each;
• Require foreground/background color contrast for buttons and text to meet general accessibility standards;
• Require all interactive elements to have hover, focus, and disabled state styles.

4.4 Data Layer Decoupling is the Watershed for Productionization

The core difference between a prototype and a production project lies in the data source. It is recommended to require early in the task:

Extract all mock data to the src/mocks directory. Components must obtain data through a unified data access layer. Hardcoding array constants directly inside components is prohibited.

This constraint significantly reduces the refactoring cost of integrating real APIs later.

V. Practical Advice: How to Achieve Stable Outputs with AI Tools

5.1 Task Decomposition Strategy

Do not throw all 10 pages to the tool at once. A three-round approach is recommended:

1. Skeleton Round: Routing, layout, common components, and design tokens (colors/spacing/border radius).
2. Page Round: Generate in groups by “Browsing → Transaction → Account → User Center.” Proceed to the next group only after the previous one passes acceptance.
3. Hardening Round: Form validation, empty/error states, responsiveness, and accessibility.

5.2 Acceptance Checklist

After each round, accept based on the following dimensions to avoid issue accumulation:

• Are all routes reachable, and are there dead links?
• Is the visual consistency across pages maintained (compare primary colors, buttons, card styles)?
• Does form validation cover required fields, formats, and boundary lengths?
• Are there overflow, occlusion, or insufficient contrast under mobile viewports?
• Are there hardcoded data, empty directories, or duplicate nested directories?

5.3 Prompt Engineering Essentials

• Give Constraints, Not Just Requirements: Write engineering specifications into the prompt.
• Give Counter-Examples: Explicitly state “do not produce white buttons with white text” or “do not hardcode data in components.”
• Require Self-Checks: Ask the tool to list “unfinished items” and “known issues” after output, allowing humans to focus their review.

VI. Conclusion

AI coding tools can already cover the complete chain from “a single prompt to a runnable prototype,” but differences in engineering standards, consistency, and responsiveness are significant. When selecting, it is advisable to treat “usability prerequisites” as a hard threshold, use “post-rework usability rate” as the core metric, and conduct horizontal verification through unified tasks. For developers, true efficiency gains do not come from the tool’s “one-shot generation,” but from the combination of clear task decomposition, explicit engineering constraints, and strict acceptance checklists. Mastering these three aspects will stabilize output quality regardless of the tool category used.