AI Coding Tool Selection Guide: Hands-On Benchmark with an E-Commerce Project Built from Scratch

Why Benchmark Tasks Matter More Than Vendor Claims

Marketing pages are always impressive: one prompt, one app; minutes, not hours. But real selection decisions hinge on how tools perform under genuine engineering constraints—whether requirements are understood in one pass, whether visual style stays consistent across pages, and whether the resulting codebase can be maintained over time. Feature comparisons and marketing copy cannot answer those questions. The most reliable approach is to design a “benchmark task”: run every candidate tool against the same requirements using the same prompt, then score them side by side against a unified rubric.

A good benchmark task must meet three criteria:

• Sufficient information density: multiple pages, multiple states, interactive loops that expose the tool’s task-planning ability.
• Clear functional boundaries: every requirement can be mechanically verified as “done / not done,” avoiding subjective scoring.
• Close to real business: findings should transfer to day-to-day development.

One practical but often overlooked constraint: the tool must be directly accessible from your network environment without extra proxy configuration. For many teams, this is a deal-breaker.

Designing the Benchmark: Building an E-Commerce Frontend from Scratch

The benchmark task used here is a complete e-commerce site frontend. It naturally covers five sub-domains—listing, detail, transaction, authentication, and user account—making the evaluation surface comprehensive. The page list (10 pages total) is:

1. Home (product list)
2. Product detail
3. Product purchase (checkout)
4. Purchase confirmation
5. User login
6. User registration
7. User account—overview
8. User account—orders
9. User account—wallet (coupon pack)
10. User account—settings

The difficulty is not in any single page but in two things:

• Completeness: is the “browse → detail → order → payment feedback” transaction loop truly closed? Does login state persist across all account pages, and does unauthenticated access correctly redirect to the login screen?
• Overall consistency: are color palette, border radius, and spacing uniform? Is routing well organized? Do similar lists and cards reuse the same component across pages?

Writing and Deconstructing the Prompt

The prompt is the “exam paper” for the benchmark. The more engineering-oriented it is, the more comparable the results. A reusable reference version:

Please use a mainstream component-based frontend framework to build a complete shopping site frontend from scratch, implementing the following 10 pages:
home (product list), product detail, product purchase, purchase confirmation,
login, registration, user account overview, user account orders,
user account wallet (coupon pack), user account settings.
Constraints:
1. Component-based development with unified routing and navigation across pages;
2. User account pages require login; unauthenticated access must auto-redirect to the login page;
3. Product, order, and other data must be read from a dedicated data layer—no hard-coding in components;
4. Separate styles from logic and share a common set of design tokens;
5. Forms must include basic validation (required fields, format checks, password confirmation);
6. Responsive design for both mobile and desktop.

The key is making “implicit engineering expectations” explicit. By default, models take the shortest path to a working demo: placeholder images, data baked into components, inline styles mixed with logic. Putting these constraints up front forces the tool to operate under real-world规范, revealing its true capability.

Five Core Evaluation Dimensions

1. Interaction Mode

Most AI coding tools offer two work modes: conversational (the developer steers, generates and confirms segment by segment) and autonomous agent (the tool plans and executes continuously until delivery). Autonomous mode has a higher ceiling but also a lower floor—when task understanding is correct it delivers in one shot; when it drifts, it compounds errors and fixing becomes expensive. Run both modes during evaluation: conversational mode tests interactive efficiency and context retention; autonomous mode tests planning and self-correction. When a tool bundles multiple agent variants, also assess whether their responsibility boundaries are clear. Blurry “multi-agent” menus often confuse users and can even prevent the task from running at all.

2. First-Pass Success Rate

Record three outcomes per tool: runs on first generation, runs after several fix rounds, or never runs. In practice, first-pass success is rare; most cases need human intervention. The number of fix rounds is a more honest metric than “can it generate”—it reflects both error probability and self-repair ability.

3. UI Completeness

Focus on four details:

• Visual consistency: do all pages share the same primary color and spacing tokens?
• Responsiveness: does the mobile layout adapt properly, or are there obvious defects like white-on-white buttons?
• Form validation: are required-field, format, and password-confirmation checks all present?
• Image strategy: is there a unified placeholder component with real image fields reserved, or are placeholders scattered randomly?

The product detail page is especially revealing—the gap between a sparse “looks okay” page and a fully populated usable page reflects how deeply the tool understands the business.

4. Code Quality

Open the generated result and check three things: are styles separated from logic? Is the directory structure clean (pages, components, router, data layer each in their place)? Is mock data decoupled from components? A frequent failure point is product data hard-coded inside components, which would require a project-wide rewrite once real APIs are connected. An ideal structure:

src/
├── pages/        # Page components
├── components/   # Shared components: buttons, cards, form fields
├── router/       # Routing and auth guards
├── services/     # Data-access layer
├── mock/         # Mock data, referenced only by services
└── styles/       # Design tokens and global styles

The data layer exposes only functions; components never know the data source:

// services/product.js — the sole data entry point for components
import { products } from '../mock/products'
export function listProducts({ page = 1, size = 10 } = {}) {
  return products.slice((page - 1) * size, page * size)
}
export function getProductDetail(id) {
  return products.find(p => String(p.id) === String(id))
}

When mock data is later replaced with real HTTP calls, only the services layer changes—zero page modifications. This is the litmus test for whether generated code can evolve.

5. Speed and Stability

This covers service response time, whether long tasks stall or interrupt, and network stability during generation. The same task can take dramatically different durations under different loads. Slow generation breaks developer flow and directly lowers practical usability. Record total elapsed time from prompt submission to first runnable output, not per-message speed.

Recurring Issues and Root Causes

Grouping problems that appear repeatedly across multiple test rounds clarifies each tool’s capability boundaries:

1. Placeholder images everywhere: models default to treating “image” as a placeholder. Countermeasure: specify a unified placeholder component in the prompt and reserve image fields with size requirements.
2. No mobile adaptation: training corpora skew toward desktop examples. Countermeasure: explicitly state breakpoint requirements, or generate the mobile version first and extend to desktop.
3. Mock data coupled with components: models optimize for fastest working demo. Countermeasure: make “data must go through the data layer” a hard constraint and spot-check component source during review.
4. Login/registration page visual defects: visual detail stability drops when many pages are generated at once. Countermeasure: generate in batches and verify page by page.
5. Color drift across pages: each page drifts independently. Countermeasure: ask the tool to produce a design-token file first, then require every page to reference the same tokens.

Engineering Practices That Improve Output Quality

Rather than expecting perfection from the tool, make the process robust. Four recommendations:

First, define the design before generating. Supply design tokens for primary color, border radius, and spacing so every page shares variables:

:root {
  --color-primary: #2f6bff;
  --color-bg: #f5f7fa;
  --radius-card: 12px;
  --space-unit: 8px;
}

Second, generate in stages. Request the directory structure and routing skeleton first; confirm it, then generate pages one by one, and finally polish details. Generating all ten pages at once leads to inconsistency.

Third, review immediately after each round. Walk through the full shopping flow—browse → detail → order → confirmation—and verify redirect logic for unauthenticated account access. Fix issues like white-on-white buttons or missing form validation on the spot; do not batch them to the end.

Fourth, drive mock data with real data shapes. Predefine the field structures for products, orders, and coupons so mock data doubles as a future API contract, reducing rework during integration.

From Local Prototype to Production-Ready Engineering

The benchmark site still needs several steps before it is production-ready: real APIs and auth services, product image storage and distribution, deployment, logging, and monitoring. These can be addressed with standard cloud capabilities: object storage for product images, a content delivery network for static assets, and logging plus monitoring services for observability. Several cloud providers offer these building blocks; among them, platforms such as Baidu AI Cloud also provide AI-powered coding assistant products that can bridge the coding-assistance step with cloud-based development workflows—worth considering for teams that want end-to-end R&D integration during tool selection.

Conclusion

There is no silver bullet in tool selection. Running the same e-commerce benchmark across candidates reveals that real differences concentrate on three things: task-planning ability, awareness of engineering conventions, and error-correction efficiency after mistakes. Codify the evaluation dimensions into a checklist, archive prompts as templates, and document pitfalls into team standards. Tools will keep evolving; this methodology will stay relevant.