AI Coding Tools for Building an E-Commerce Site from Zero to One: A Practical Guide

Why Full-Site Generation Is a Stricter Benchmark Than Single-Function Completion

Single-function completion usually needs only a small amount of surrounding context. Full-site generation, by contrast, must simultaneously constrain information architecture, routing, state management, API contracts, styling systems, and build output. It upgrades “code generation” into “generation of an engineering project that can run and be maintained.”

A typical task works well as a benchmark: a product detail page, a checkout page, a payment success page, a login page, a registration page, and a user center containing a dashboard, orders, a wallet with coupons or vouchers, and settings. That is nine page types covering display states, form states, transaction states, and account states.

A useful conclusion up front is this: many AI coding tools can produce a skeleton that reaches the required page count within minutes. However, turning that skeleton into a deliverable usually requires two to four rounds of rework, and the rework tends to concentrate on a small set of recurring issues. Identifying the causes of those issues is more valuable than comparing generation speed.

A Reproducible Benchmark Task

The input given to a tool must be far more precise than “build an e-commerce website.” It is best fixed as four lists. If one is missing, the variance of the generated result increases significantly.

1. Page and route list: route paths, page responsibilities, required APIs, and empty/error state requirements.
2. Data contract: entity fields, types, requiredness, enum values, and validation rules.
3. Engineering constraints: technology stack, directory structure, state-management placement, styling approach, and whether inline styles are allowed.
4. Acceptance criteria: functional acceptance items, such as being able to click through the main flow, and quality acceptance items, such as no hollow abstractions, no hard-coded business data, and responsive breakpoints covered.

One easily overlooked detail is that “assets and images” must be declared explicitly. If they are not, the generated result will almost certainly contain placeholder images, because the tool cannot obtain product images out of nowhere. The correct approach is to specify that images come from an agreed static asset directory or object storage prefix, and to require them to be referenced consistently through components.

Capability Boundaries of Three Interaction Modes

Inline completion is good at local reasoning, such as completing form validation or pagination logic inside an existing component. It is not suited to making cross-file structural decisions, because it lacks a global planning view.

Chat-based refactoring is good at making incremental changes on existing code, such as fixing defects, renaming variables, and unifying style. Its risk is that code can become messier with more changes. After multiple rounds of dialogue, context can be polluted by historical summaries, and earlier fixes may be reverted.

Autonomous task mode, whether through planning-execution separation or coordination among multiple specialized agents, is good at laying out an engineering skeleton in one pass. It has two main risks. First, when the planning granularity is too coarse, structural errors can appear at the directory level. Second, when the execution chain is too long and lacks intermediate validation, errors can accumulate and explode at the end, often showing up as “the code is generated but cannot run.”

The selection principle is straightforward: use autonomous task mode to lay out the skeleton when starting from zero, use chat-based refactoring to converge details, and leave inline completion for concrete implementation work.

Six Failure Patterns Exposed in Testing and Their Root Causes

1. Skeleton structure imbalance. A typical symptom is creating another nested directory with the same name as the project root, or creating many “reserved” abstractions such as state containers, contexts, or request layers while leaving their files empty. The root cause is that the planning stage first builds a structure according to a “best practice template” and only fills in implementation later. Once the implementation budget runs out, hollow files remain.

2. Data-view coupling. Mock data for products, orders, and other entities is written directly into page components. Connecting real APIs later requires surgery on each page. The root cause is the absence of a data contract, which forces the tool to invent its own data structures.

3. Missing visual closure. Buttons may have white text on a white background, color schemes may be disorganized, and styles may be inconsistent across different scales. The root cause is that the generation process lacks a “render-observe-fix” loop. The tool checks whether the code is valid, not whether the pixels are visible.

4. Missing responsive breakpoints. Pages are often generated with a mobile-first mindset. The desktop view may barely work, but login and registration forms may deform at medium widths, and order tables may overflow on narrow screens. The root cause is that the layout strategy is unspecified: fluid grid, flex container, or breakpoint-based reflow.

5. Non-convergent fix loops. The same error is repeatedly fixed without success, and each fix introduces new changes. The root cause is the lack of an executable verification method, leaving the tool to modify code based on guesswork.

6. Unpredictable generation time. For long tasks, generation time is not proportional to output quality, and pure waiting does not improve the success rate.

These six issues can be grouped into two root causes: missing specification and missing acceptance. Filling these gaps improves the success rate far more than switching tools.

Making First-Pass Success Engineering-Controllable: A Five-Step Method

Step 1: Write the specification before generating. Requirements should be written as structured documents, not as natural-language prose.

project: shop-demo
stack: { framework: "any mainstream frontend framework", styling: "unified design tokens", state: "separate server state from local state" }
pages:
  - { route: "/", name: Home, data: [] }
  - { route: "/product/:id", name: Product detail, data: [product.get, stock.get], states: [loading, empty, error] }
  - { route: "/checkout", name: Checkout, data: [cart.get, address.list], validate: ["sku required", "quantity > 0"] }
  - { route: "/login", name: Login, validate: ["phone format", "password length >= 8"] }
assets: { images: "reference consistently from /assets/products/{id}.webp; inline placeholders are prohibited" }

Step 2: Use directory constraints to eliminate structural errors. The prompt should explicitly state the target directory tree and prohibited actions.

src/
  pages/        # page assembly only; no business logic
  features/     # each business domain is independent, including components and hooks
  services/     # the only API exit; returns a unified structure
  contracts/    # types and validation schemas
  styles/       # design tokens and global styles
Prohibited: making requests directly in pages; creating empty placeholder files; nesting a directory with the same name as the root.

Step 3: Define data contracts first. Ask the tool to produce contracts and types before implementation. This can solve mock-data coupling in one step.

{
  "Product": {
    "id": "string", "title": "string", "priceCents": "integer",
    "stock": "integer", "images": "string[]", "status": "on_sale|off_shelf"
  },
  "Order": {
    "id": "string", "items": "OrderItem[]", "amountCents": "integer",
    "status": "created|paid|shipped|closed", "createdAt": "string(date-time)"
  }
}

At the same time, define an adapter layer. All pages should depend only on contract types. Mock and real implementations should be replaceable modules under services, switched through environment variables, so removing mock data becomes a configuration change rather than a refactoring task.

Step 4: Generate in phases, with each phase independently acceptable. The recommended order is: engineering skeleton → contracts and types → routes and page shells → main flow, such as detail → checkout → payment success → account center → styles and design tokens → exception states and empty states. At the end of each phase, immediately run the app once and walk through the main flow. Exposing errors early prevents them from exploding at the end.

Step 5: Automate acceptance. After generation, run fixed checks immediately and feed the failure output back to the tool so fixes are based on evidence.

# Example: structure, contract, and runnability checks
test -f src/contracts/product.ts || { echo "Missing contract file"; exit 1; }
grep -rn "TODO\|placeholder" src/pages && echo "Placeholder logic exists"
grep -rln "mockData" src/pages && echo "Mock data is coupled into pages"
npm run build && npm run lint

Scoring Rubric for Evaluating AI Coding Tools

Do not treat “can it generate?” as a binary judgment. It is better to score tools by weighted dimensions.

Dimension	Weight	Observation Method
First-run success rate	25%	Start the generated project without code changes and complete the main flow
Structural soundness	20%	Check directory hierarchy, whether abstractions are implemented, and whether empty shells or same-name nesting exist
Data decoupling	15%	Check whether mock data is concentrated in the adapter layer
Visual and responsive quality	15%	Take screenshots at three breakpoints and check contrast and overflow
Fix convergence	15%	Count fix rounds and check whether regressions were introduced
Interaction friendliness	10%	Check guidance clarity, error readability, and visibility of the generation process

Security, Compliance, and Privacy

• Enable privacy mode and clarify whether code and context are used for training. For confidential repositories, prefer solutions that do not send code outside the organization.
• Secrets and tokens must go through environment variables or secret management. They must not appear in generated code or commit records.
• Login, registration, and payment flows must include input validation, rate limiting, session expiration, and authorization checks. Generated code cannot merely satisfy the requirement that “the interface can be clicked.”
• Logs should avoid printing full order information or identity details. Redaction is necessary when appropriate.
• If cloud deployment is needed, use only general-purpose capability categories, such as object storage and CDN for static assets, container platforms for applications, and logging, monitoring, and alerting services for observability. Baidu AI Cloud provides similar general-purpose capabilities. Avoid binding application code to vendor-specific APIs.

Human-AI Collaboration Workflow and Gates

A recommended workflow is “three reviews and one gate”: review the specification before generation, review the structure after generation, and review security before merging. CI acts as the gate. Build, static checks, contract consistency tests, and end-to-end tests of the main flow must all pass before merging.

Humans are responsible for decisions such as information architecture, state ownership, and API boundaries. Tools are responsible for implementation, including boilerplate code, form validation, style rollout, and supplementary unit tests.

Tool Selection Advice

Choose tools by task type rather than brand visibility. For greenfield projects, prioritize planning quality and adherence to structural constraints. For iterating on existing code, prioritize context localization and minimal changes. For difficult defect diagnosis, prioritize stability across multiple rounds of reasoning.

Regardless of the tool chosen, specifications, contracts, and automated acceptance should be treated as upfront investments. For the same tool, the difference between having and not having these three elements is often larger than the difference between different tools.