How to Choose an AI Coding Tool: An E-Commerce Frontend Benchmark

Don’t Judge by Specs—Use a Real Task as the Benchmark

AI coding tools today fall into three broad categories: conversational assistants embedded in editors, deeply modified AI-native IDEs, and cloud-based agent services that can execute tasks autonomously. Marketing claims like “understands your intent” and “generates a complete project in one click” are hard to compare across vendors. Demo tasks are carefully curated, and the low complexity of those demos does not reflect the realities of production-grade projects.

The only reliable evaluation method is to design a single, representative task with a unified set of acceptance criteria, then have each candidate tool complete it independently. The task should meet three conditions: broad coverage (exercising routing, state management, forms, lists, and end-to-end flows), moderate workload (completable within a single session), and quantifiable results (does it run, how many errors, how many fix iterations, how much time). Only with these three conditions in place can the results be meaningfully compared.

Benchmark Design: Building an E-Commerce Frontend from Scratch

An e-commerce frontend is a natural proving ground because it covers nearly every common scenario in frontend engineering:

• Product list and detail pages: tests data rendering and information organization.
• Checkout and order confirmation pages: tests flow completion and state transitions (place order → payment → success feedback).
• Login and registration pages: tests form validation, error messaging, and submission feedback.
• User center pages (dashboard, orders, wallet/coupons, settings): tests multi-page architecture and layout reuse under authenticated state.

It is equally important to define uniform technical constraints: use a component-based mainstream frontend stack; adopt a mobile-first approach with basic responsiveness; separate mock data from business logic; include basic form validation. The more explicit the constraints, the better you can evaluate how well a tool follows instructions—many failures actually stem from poorly specified requirements.

For consistent evaluation records, capture these fields: passed on first run (yes/no), error count, fix iterations, total time, whether styles and logic are separated, whether data coupling exists. Standardized fields lead to objective conclusions.

Choosing an Interaction Mode: Step-by-Step Confirmation, Fully Automatic, or Multi-Agent

Most current tools offer three interaction patterns, each with clear boundaries:

1. Step-by-step confirmation: the tool first outputs an implementation plan, then generates code incrementally, with each step available for review or modification. The advantage is control and reduced context drift; the downside is that a human must stay in the loop. For complex multi-page tasks, this mode tends to achieve the highest first-pass success rate.
2. Fully automatic: the tool generates the entire project in one go. It seems effortless, but errors accumulate as generation proceeds—once the tool goes off track midway, all subsequent code is built on a faulty foundation, and repair costs escalate quickly. This mode is better suited to single-page work or small-scope changes.
3. Multi-agent / high-speed mode: parallel generation delivers the fastest results, ideal for rapid prototyping. But the risks are equally clear: structure can easily spiral out of control, with symptoms including messy directory hierarchies, same-name nested folders, inconsistent module conventions, and in severe cases a project that simply won’t start.

An interesting industry pattern: early on, “fully automatic one-click delivery” was seen as the future. Today, many products are actually moving back toward step-by-step confirmation—for a simple reason: delivery quality matters more than delivery speed. A practical recommendation is to treat “automation level” as a tunable parameter: use high-speed mode for prototype validation, automatic mode for small single-page changes, and step-by-step mode for production projects, with mandatory human confirmation at three critical checkpoints: scaffolding, routing design, and data layer design. Additionally, when a tool offers too many modes, “choosing” itself becomes a cost: whether the documentation is clear and whether the defaults are sensible should also be part of your evaluation.

Six Common Problems in AI-Generated Code and How to Avoid Them

Regardless of which tool you use, the following problems occur frequently. The root causes and countermeasures matter more than the symptoms themselves.

1. Broken visual details. Symptoms: images are all placeholders, buttons have white text on white backgrounds and are nearly invisible, login and registration pages break on small screens. The root cause is that models lack a “rendering feedback loop”—they can only infer visual results from code. Countermeasure: explicitly specify design requirements—theme color palette, three button state colors (default/hover/disabled), breakpoint list (e.g., 375px/768px/1200px)—and require a “visual smoke test” pass after generation.

2. Superficial form validation. Symptoms: only non-empty checks are implemented; format and consistency validation are completely absent. Countermeasure: turn validation rules into an itemized acceptance checklist—field format (email, phone number), length boundaries, password confirmation matching, error message copy, and display position.

3. Mock data coupled into business code. Symptoms: fake data is hardcoded into components, causing widespread rework when real APIs are integrated later. Countermeasure: have the tool build the data access layer first, then write pages. Recommended structure:

src/
├── components/     # Presentational components: data-agnostic, render-only
├── pages/          # Page-level components: login, register, detail, orders...
├── services/       # Data access layer: pages only fetch data from here
│   └── api.js
├── mocks/          # Centralized mock data, replaced wholesale during integration
└── styles/         # Global styles and design variables

// services/api.js — pages fetch data exclusively from this layer
import { productList } from '../mocks/product';
export const fetchProductDetail = (id) =>
  Promise.resolve(productList.find(item => item.id === id));

Later, you only need to point fetchProductDetail to the real API—not a single line of page code changes.

4. Overly simplistic architecture. Symptoms: all files are piled into a single directory level, styles and logic are mixed together. Countermeasure: before code generation, require the tool to output a directory design and a “page-route-component” mapping table, and get human sign-off before proceeding.

5. Structural low-level errors. Symptoms: same-name nested folders, broken path references—directly preventing the project from starting. This is especially common in high-speed mode. Countermeasure: require a post-generation self-check—list the directory tree, run a build, and treat “it actually starts” as the first acceptance gate. Do not proceed if it fails.

6. Fix loops. Symptoms: error → fix → new error introduced → fix again, growing increasingly chaotic. Countermeasure: set a fix iteration cap (e.g., three rounds). If exceeded, roll back to the most recent runnable state, split the task into smaller pieces, and retry. Don’t cling to sunk costs—the cost of regenerating is far lower than manually patching a mess.

Collaboration Techniques to Improve First-Pass Success

Rather than hoping tools get stronger, improve how you work with them. Three steps:

1. Requirements first: have the AI restate the requirements and output an implementation plan. Get human confirmation before generating code—this avoids “moving faster in the wrong direction.”
2. Stage-by-stage progression: scaffolding → global layout and styles → routing → page-by-page generation → detailed integration. Validate each stage before moving on, breaking the large task into rollback-friendly small steps.
3. Prompts with acceptance criteria. A useful template:

Goal: implement the user registration page
Constraints: reuse existing project components and style variables; mobile-first, no layout breakage at 375px
Fields: username, email, password, confirm password
Validation: email format; password ≥ 8 characters with both letters and numbers; two password entries match
Messaging: errors display below input fields in red text
Acceptance: no console errors; all above validations take effect

A prompt with acceptance criteria is like writing the definition of “done” into the contract in advance—no matter how confident the tool is, it cannot bypass your measuring stick.

Beyond Generation Quality: What Else to Consider

• Connectivity and compliance: access stability, response latency, and whether data leaves your jurisdiction. Enterprise users should pay particular attention. Services with direct regional access (such as AI coding and model service offerings from providers like Baidu AI Cloud) are often less troublesome in terms of connectivity, local language support, and data compliance.
• Privacy boundaries: whether a privacy mode that excludes your code from model training is available is becoming a hard requirement for enterprise procurement.
• Generation speed: latency compounds over long tasks. Record both “time to first token” and “total elapsed time.”
• Pricing model: differences between free tiers, subscriptions, and usage-based billing scale significantly with team size.
• User experience: interface friendliness does not affect code quality, but it does affect fatigue during extended use.

Finally, consolidate your observations into a scorecard (adjust weights based on your team’s needs):

Dimension	Weight	Evaluation Points
First-pass success rate	30%	Runs without fixes on first attempt
Code quality	25%	Layering, naming, structure, data decoupling
UI completeness	20%	Placeholder ratio, responsiveness, interactivity
Repair capability	15%	Error localization accuracy and fix convergence speed
Speed and experience	10%	Latency, interface, pricing reasonableness

Final Thoughts

Tools iterate on a weekly basis, but methodology remains useful over the long term: anchor on real tasks, use acceptance checklists as your guide, and treat automation level as a tunable parameter rather than an article of faith. For multi-page tasks, prefer step-by-step confirmation. Build the data access layer first. Constrain visual details explicitly. Roll back decisively when fix iterations exceed the cap. When you can measure every tool with the same yardstick, the answer to “which one should I choose” will reveal itself.