Selecting AI Coding Tools for Complex Engineering Projects

As software development enters a more intelligent stage, developers’ expectations for AI coding tools have moved beyond “completing a few lines of code” or “generating a single function.” Tools with real business value need to understand complete engineering requirements and generate code that is well-structured, runnable, maintainable, and verifiable across multiple files, modules, and pages. This is especially important when building a complete shopping site, which is a typical complex task. In such a scenario, an AI coding tool must not only generate UI code, but also handle routing, state management, form validation, user login and registration, order flows, user account areas, mock data, responsive layouts, and exception handling as a complete engineering workflow.

Therefore, when discussing AI coding tools available in China, especially domestic options, developers should not stop at listing available tools. They should ask deeper questions: Does the tool have task-level understanding? Can it generate a complete project skeleton? Can it maintain consistency across pages and files? Can it repair errors effectively after failures? Does it meet the security, compliance, and auditability requirements needed for enterprise development? Only by establishing such an evaluation framework can teams determine whether a tool is suitable for replacing traditional single-function code completion tools and becoming team-level development productivity infrastructure.

1. Why Complex Tasks Reveal the Real Limits of AI Coding Tools

Take building an e-commerce shopping site from scratch as an example. On the surface, it may appear to be just generating several pages. In practice, it involves multiple business domains and engineering layers working together.

From a business perspective, the task usually includes pages such as:

• Home page with a product list
• Product detail page
• Purchase flow
• Purchase success page
• User login page
• User registration page
• User center dashboard
• Order management page
• Wallet or coupon package page
• System settings page

These pages do not exist in isolation. They revolve around core objects such as users, products, orders, and accounts.

From an engineering perspective, the task requires at least the following capabilities:

1. Page routing design: How pages navigate between each other, how login state is validated, and how unauthenticated users are handled when they try to access order pages or the user center.
2. Global state management: User login information, shopping cart data, current order status, and wallet balance need to be shared across multiple pages.
3. Data service abstraction: Product lists, product details, user information, and order records should be retrieved through a unified service layer rather than being directly coupled with mock data in pages.
4. Form validation: Forms for login, registration, delivery information, and payment confirmation need basic validation and error feedback.
5. Separation of styles and logic: Page structure, interaction logic, and style definitions should have clear boundaries to avoid generating large amounts of hard-to-maintain inline code.
6. Responsive adaptation: Desktop and mobile layouts should switch reasonably to avoid content overflow, invisible buttons, or misaligned forms on small screens.
7. Exception handling: Login failures, insufficient inventory, failed order submission, and network errors need to be explicitly handled.
8. Placeholder resource management: When images, icons, or fonts are missing, placeholder solutions should be used instead of causing broken images or build failures.

Complex tasks expose differences between tools because they require AI not only to write code, but to write correct, runnable, and maintainable code. Generating a single page may rely mainly on language capability. Generating a complete project relies on task planning, context understanding, adherence to engineering conventions, and automated repair capability.

2. Selection Should Be Based on an Engineering Evaluation Framework, Not Just UI Friendliness

When choosing AI coding tools, many developers first focus on whether the interface is attractive, whether prompts are convenient to enter, and whether responses are fast. These factors matter, but for complex projects they are only basic experience factors. What truly determines the ceiling of a tool is its performance across the full engineering lifecycle.

A relatively complete evaluation framework can include the following dimensions.

2.1 Task Understanding and Requirement Decomposition

A strong tool should be able to turn a high-level requirement such as “build a shopping site using component-based frontend technology” into executable engineering tasks. For example:

1. Define the project structure.
2. Define page routing.
3. Generate shared components.
4. Build business pages.
5. Integrate state management and mock data.

If a tool simply generates a large amount of code without a clear task path, it may produce pages that appear complete but have broken logic, or entry points that exist but cannot complete real workflows.

2.2 Completeness of Generated Results

Completeness does not only mean having all required pages. Each page also needs usable content. For example, a login page should not contain only an input field; it should also include validation, error messages, and navigation logic. An order page should not contain only a title; it should show order status, amounts, product summaries, and action buttons. Whether a tool can reliably generate these key details is an important indicator of maturity.

2.3 Engineering Structure and Code Consistency

In complex projects, inconsistent naming, duplicated directories, incorrect file hierarchy, and inconsistent state management locations quickly increase maintenance costs. High-quality tools should follow basic engineering conventions, such as:

• Clear responsibilities for pages, components, services, state, and utility functions
• Consistent naming for the same type of data
• Avoiding complex data requests directly inside page components
• Separating styles from logic
• Consolidating common capabilities into reusable modules

2.4 Runnability and Error Repair Capability

Generating code is only the beginning. The ability to start the project and keep it running is more important. A tool should support a basic closed loop: generate, run, discover issues, fix issues, and verify again. If the project cannot run directly after initial generation, the tool should be able to correct errors based on error messages instead of repeatedly introducing new problems.

2.5 Maintainability and Extensibility

After a project is completed, it often needs to be extended with features such as coupons, favorites, payment channels, logistics status, or admin functions. If the generated code is only superficially complete and lacks clear layering, every new feature may require changes across many files. Therefore, selection should focus on whether the generated code has good extensibility.

2.6 Security, Compliance, and Enterprise Governance

In enterprise environments, code generation tools must not only be usable but also controllable. Capabilities such as code access permissions, model invocation auditing, sensitive information filtering, log retention, and keeping data within required boundaries all affect whether a tool can be used in real production development processes. This is especially important for systems involving user accounts, orders, and payment information.

3. A General Technical Approach: From Model Capability to Engineering Closed Loop

To understand how AI coding tools support complex projects, it is useful to look at a general technical approach. A mature AI development system is usually not the result of a single model capability, but the result of multiple layers working together.

3.1 Model Service Layer

The model service layer is responsible for understanding natural-language requirements, generating code, explaining code, generating tests, locating errors, and providing refactoring suggestions. For complex tasks, the model must understand not only syntax but also engineering structure. For example, when a user requests that “after successful login, the user should be redirected to the user center and the login state should be preserved,” the model needs to understand routing, state persistence, and permission validation.

3.2 Context Management Layer

Code generation quality depends heavily on context. Context includes not only the current prompt, but also the project structure, existing files, interface definitions, design conventions, dependencies, and historical error messages. Context management determines whether a tool can continue development on top of an existing project instead of generating an isolated example from scratch every time.

3.3 Task Orchestration Layer

A complex project is not suitable for putting all requirements into a single prompt at once. A more reliable approach is to split the task into stages:

1. Generate the basic project structure.
2. Define page routing.
3. Generate shared components.
4. Generate business pages.
5. Integrate state management.
6. Add mock data.
7. Run build checks.
8. Fix issues based on errors.

This staged approach reduces the chance of generation going out of control and improves consistency in the final result.

3.4 Engineering Execution Layer

AI coding tools usually need to perform code generation, file writing, dependency installation, project startup, and log viewing in a real development environment. The more stable the execution layer, the fewer problems such as “generation appears successful but the project cannot actually run.”

3.5 Quality Validation Layer

AI-generated code must be validated. Common validation methods include:

• Static checks
• Type checks
• Build checks
• Automated tests
• Regression checks for key flows
• Visual checks for pages
• Exception path testing

Without a quality validation layer, AI coding can easily turn into “a large amount of generated code that is not actually usable.”

3.6 Security Governance Layer

The security governance layer controls access permissions, records operation logs, filters sensitive information, audits model invocation behavior, and prevents secrets, tokens, internal interface addresses, and other sensitive information from being written into generated code. If an organization hosts related capabilities in compliant cloud environments such as Baidu AI Cloud, it can more easily implement permission control, invocation auditing, and data isolation.

4. Prompt Design for a Shopping Site

For complex projects, a prompt should not be a single sentence. It should include goals, scope, structure, constraints, and acceptance criteria. Using a shopping site as an example, a structured prompt can be used as follows:

Use a component-based frontend technology to build a shopping site with the following requirements:
1. Page scope:
   - Home page: display a product list
   - Product detail page: display product information, inventory, price, and purchase entry
   - Product purchase page: confirm order, delivery information, and payment method
   - Purchase success page: display order number and next-step entry points
   - User login page
   - User registration page
   - User center dashboard
   - User center orders page
   - User center wallet and coupon page
   - User center settings page
2. Engineering requirements:
   - Separate responsibilities among pages, components, service layer, state layer, and style files
   - Keep routing structure clear
   - Validate login state
   - Share shopping cart data across pages
   - Do not directly couple mock data into page components
   - Provide basic responsive adaptation for all pages
   - Include basic form validation and error messages
   - Use placeholder solutions when image resources are missing
3. Interaction requirements:
   - Redirect unauthenticated users to the login page when they access the user center
   - After successful login, return users to the original target page or send them to the user center
   - Allow users to view product details from the product list
   - Support a complete purchase flow
   - Display order status
   - Display wallet balance and coupon list on the wallet page
4. Output requirements:
   - First output the recommended project structure
   - Then generate core routes and page entry points
   - Finally complete page details and mock data
   - After generation, provide startup instructions and known limitations

This prompt is more effective than “help me build an e-commerce website” because it clarifies page scope, engineering constraints, interaction logic, and acceptance criteria. The tool is more likely to produce stable output and avoid omissions or deviations.

5. Recommended Project Structure for Maintainability

For multi-page applications such as shopping sites, project structure is very important. A good structure is not only easier for humans to read; it also helps AI understand context and reduces confusion when modifying multiple files.

A common structure can be organized as follows:

src/
  pages/              # Page entries
    home/
    product-detail/
    checkout/
    checkout-success/
    login/
    register/
    user/
      dashboard/
      orders/
      wallet/
      settings/
  components/         # Reusable components
    product-card/
    order-item/
    form-field/
    user-menu/
    empty-state/
    loading-state/
  services/           # Data access layer
    product-service.js
    user-service.js
    order-service.js
    wallet-service.js
  stores/             # Global state
    user-store.js
    cart-store.js
    order-store.js
  mock/               # Mock data
    products.js
    users.js
    orders.js
  styles/             # Global styles and design variables
    tokens.css
    base.css
    responsive.css
  utils/              # Utility functions
    validation.js
    formatter.js
    storage.js
  tests/              # Test cases
    login.test.js
    checkout.test.js

This structure has several advantages:

1. The boundary between pages and components is clear, making it harder for AI to put all business logic into page files.
2. The service layer centralizes data requests, limiting the impact when real APIs replace mock data later.
3. Centralized state management makes global data flow easier to understand.
4. Independent mock data management avoids deleting scattered mock data from many pages when connecting to real APIs.
5. Centralized style resources help maintain a consistent visual style.
6. Independent utility functions make validation, formatting, storage, and other capabilities easier to reuse.

Adding similar structural requirements to the prompt can significantly improve the engineering quality of the generated result.

6. State Management: A Key Challenge for Complex Shopping Sites

A shopping site is not simply a collection of display pages. It is a system with clear state transitions. Poor state management can easily lead to data inconsistency, abnormal navigation, lost login state, or incorrect cart totals.

The following types of state usually need attention.

6.1 User State

User state includes login status, user information, permission identifiers, and login credentials. It affects whether pages such as the user center, orders page, and wallet page are accessible.

User state example:
{
  isLoggedIn: boolean,
  user: {
    id: string,
    name: string,
    avatar: string,
    token: string
  }
}

6.2 Shopping Cart State

Shopping cart state includes the product list, quantity, unit price, selected status, and total amount. The purchase page needs to generate an order preview based on the cart state.

Shopping cart state example:
{
  items: [
    {
      productId: string,
      name: string,
      price: number,
      quantity: number
    }
  ],
  totalAmount: number
}

6.3 Order State

Order state includes pending payment, paid, shipped, completed, canceled, and other statuses. The user center orders page needs to display different action buttons based on different statuses.

6.4 Wallet and Coupon State

Wallet state includes balance, coupons, cards, expiration dates, and related information. This part should be bound to user identity and should not be hard-coded directly inside page components.

The key to state management is not using a specific technology. The key is making the source of state unique, making transitions clear, and making modifications controllable. When generating code, AI tools should be required to separate state logic from pages. Otherwise, data inconsistency across pages is likely to occur.

7. Data Service Layer: Preventing Mock Data from Polluting Business Logic

During the prototype stage, many projects use mock data. However, if mock data is not properly isolated, it can become deeply embedded in page logic. When real APIs are introduced later, this can require large-scale refactoring. Therefore, when AI generates a project, it is better to establish a service layer from the beginning.

For example, a unified product service can be defined as follows:

async function fetchProductList(params) {
  // Internally, this can return mock data or call a real API.
  return request('/api/products', {
    method: 'GET',
    params
  });
}

async function fetchProductDetail(productId) {
  return request(`/api/products/${productId}`, {
    method: 'GET'
  });
}

The page layer only needs to call services. It does not need to care where the data comes from. The benefits include:

1. When real APIs are introduced later, only the service layer implementation needs to be replaced.
2. Page components can focus more on display and interaction.
3. Unit testing becomes easier.
4. Mock data and real data can be maintained in parallel.
5. The risk of hard-coded data in generated code is reduced.

Adding a clear requirement to the prompt, such as “mock data must be encapsulated in an independent service or mock layer and must not be scattered across page components,” can significantly improve generated code quality.

8. Form Validation and Exception Handling: Details That Determine Usability

Whether a shopping site feels professional is often not determined by the homepage visuals, but by whether detailed flows are reliable. Especially for key steps such as login, registration, purchase, and payment confirmation, complete form validation and exception handling are required.

8.1 Login and Registration Forms

Login and registration pages should at least handle the following validations:

• Whether the username or email is empty
• Whether the password length meets requirements
• Whether the two passwords match during registration
• Whether verification codes or agreement checkboxes are completed
• Whether clear error messages are shown when requests fail
• Whether the submit button is disabled during repeated submissions

8.2 Purchase Confirmation Form

The purchase page should validate:

• Whether the recipient name is empty
• Whether the mobile phone number format is correct
• Whether the address is complete
• Whether a payment method has been selected
• Whether inventory is sufficient
• Whether coupons or card packages are valid

8.3 Exception State Handling

Common exceptions include:

• API timeout
• Server-side error
• Expired login credential
• Product removed from sale
• Insufficient inventory
• Failed order creation
• Failed page resource loading

If an AI tool generates only the happy path without handling exception paths, the project may look complete during a demo, but defects can easily appear in real use. Therefore, task prompts must explicitly require the generation of exception-handling logic.

9. Responsive Design and Visual Consistency

Shopping sites usually need to switch between desktop and mobile views. Without clear constraints, AI-generated interfaces may encounter problems such as:

• The layout is mainly designed for desktop, causing cramped layouts on mobile devices
• Insufficient color contrast on buttons
• Form fields overflowing on narrow screens
• Unreasonable product card arrangement
• Inconsistent image placeholder sizes
• Misaligned login and registration pages on small screens
• The user center sidebar cannot collapse on mobile

To avoid these problems, visual and responsive constraints can be added to the prompt:

1. Use a mobile-first strategy.
2. Use a multi-column layout on desktop and a single-column layout on mobile.
3. Ensure all buttons have sufficiently large clickable areas.
4. Form input fields must not overflow on narrow screens.
5. Product cards must maintain consistent aspect ratios.
6. Use placeholder images for missing images.
7. Keep primary colors, secondary colors, and font hierarchy consistent.
8. Do not use unreadable color combinations such as white text on a white background.

Visual consistency is not only about aesthetics. It also affects user trust in the system. For e-commerce pages, clear information hierarchy, stable layout structure, and recognizable action buttons are important parts of usability.

10. Debugging Closed Loop: From Generation to Successful Execution

The key way AI coding tools improve efficiency is not by generating a large amount of code at once, but by entering a debugging closed loop. A debugging closed loop means the tool can continuously correct its output based on runtime results.

A typical closed loop looks like this:

1. Generate the project skeleton.
2. Generate page entry points.
3. Start the project.
4. Check build logs.
5. Locate issues based on errors.
6. Modify related files.
7. Restart and verify.
8. Perform regression checks on key flows.

In this process, developers should avoid asking the AI to rewrite the entire project at once. A more reasonable approach is to require minimal fixes. For example:

Please fix the issue based on the following error information:
1. Only modify files directly related to the error.
2. Do not rewrite the entire project structure.
3. Keep existing page routes unchanged.
4. Keep the existing state management approach unchanged.
5. After the fix, explain which files were modified and why.

This helps reduce regression problems and avoids fixing one issue while introducing three new ones.

11. Automated Quality Gates: Improving the Reliability of AI-Generated Code

For teams, relying entirely on manual review of AI-generated code is not realistic. A more reasonable approach is to establish automated quality gates so that machines can perform the first round of filtering.

Common quality gates include:

11.1 Syntax and Format Checks

Check for obvious syntax errors, inconsistent indentation, incorrect import paths, missing files, and similar issues.

11.2 Static Checks

Detect unused variables, duplicate definitions, missing exception handling, potential null access, and similar problems.

11.3 Build Checks

Confirm that the project can be built successfully. Many runtime errors can be discovered during the build stage.

11.4 Unit Tests

Write tests for core logic such as login, registration, cart calculations, and order state transitions. AI can help generate test cases, but developers need to confirm that the tests themselves are effective.

11.5 Page Route Checks

Ensure that all page entries are accessible and that there are no blank pages, incorrect redirects, or duplicate routes.

11.6 Key Flow Checks

Check main flows such as accessing the user center after login, moving from product details to the purchase page, and generating an order after successful purchase.

11.7 Security Scans

Check for risks such as hard-coded secrets, insecure links, sensitive information output, and dangerous script injection.

The value of automated quality gates is that they turn AI-generated code from an uncontrollable black box into a verifiable, traceable, and continuously improvable engineering artifact.

12. Enterprise Adoption: From Personal Productivity Tool to Development Infrastructure

For individual developers, the value of AI coding tools may appear as reduced repetitive coding, faster prototype building, and a lower learning barrier. For enterprises, the concerns are more complex.

Enterprise adoption usually needs to address the following issues:

1. Data security: Whether source code will be accessed by external models and whether this creates leakage risk.
2. Permission control: Whether different roles can access different code repositories, models, and generation capabilities.
3. Auditability: Whether model invocations, code generation, and file modifications can be tracked.
4. Compliance requirements: Whether generated code meets internal standards, industry requirements, and applicable regulations.
5. Stability: Whether obvious delays or service unavailability occur under high-concurrency usage.
6. Cost control: Whether model invocation, context length, generation frequency, and manual review costs are manageable.
7. Knowledge retention: Whether internal conventions, historical components, and interface documentation can be used as generation context.

In such cases, enterprises care not only about whether a tool is easy to use, but also about whether it can be integrated into the development system. For example, model services, code access control, invocation logs, and permission audits can be deployed in compliant environments such as Baidu AI Cloud. This helps AI-assisted programming move from a personal tool to enterprise-level development infrastructure.

13. Practical Recommendations for Using AI Coding Tools on Complex Projects

Based on the analysis above, a practical set of operating recommendations can be summarized.

13.1 Define Acceptance Criteria Before Generation

Do not simply say “build an e-commerce website.” Instead, clarify:

• Which pages are required
• What task each page should complete
• Which flows must work end to end
• Which exceptions must be handled
• What code structure is required

The clearer the acceptance criteria, the more stable the AI output.

13.2 Generate in Stages

It is recommended to proceed in the following order:

1. Project structure and routing
2. Shared components
3. Home page and product list
4. Product detail page
5. Login and registration
6. User center
7. Purchase and orders
8. Wallet and settings
9. Mock data completion
10. Build checks and error fixing

Staged generation exposes problems earlier and makes them easier to locate.

13.3 Maintain Context Consistency

When making later modifications, clearly tell the AI about the current project structure and existing constraints. For example:

The current project already uses a layered structure for pages, components, services, state, and mock data.
Please continue development on top of the existing structure. Do not create duplicate directories.

This helps avoid file structure confusion and duplicate implementations.

13.4 Let the AI Fix the Smallest Possible Scope

When an error occurs, ask the AI to modify only related files and explain the reason for each change. Avoid letting it rewrite an entire page just to “fix one issue.”

13.5 Separate Mock Data from Real APIs

Mock data can be used early to validate flows, but it must be encapsulated through the service layer. When real APIs are introduced later, only the service implementation should need adjustment, rather than modifying pages one by one.

13.6 Establish a Manual Review Mechanism

AI-generated code can serve as a draft, but it cannot completely replace manual review. Key review points include:

• Whether login state is handled securely
• Whether order amount calculations are correct
• Whether form validation is complete
• Whether page navigation matches expectations
• Whether sensitive information is hard-coded
• Whether enterprise coding standards are met

13.7 Turn Common Mistakes into Prompt Rules

If a tool frequently generates a certain type of problem, such as inconsistent image placeholders, invisible button colors, or mock data coupled into pages, those requirements can be fixed into prompt templates. This significantly reduces the chance of repeated mistakes.

14. Conclusion: The Value of China-Based AI Coding Tools Lies in Engineering Closed-Loop Capability

Returning to the original question: what alternatives exist to traditional AI coding tools? A more accurate answer is not simply a list of names. It is to establish a judgment standard: a truly valuable AI coding tool must support complex engineering tasks and form a closed loop across requirement understanding, project generation, code maintenance, debugging and repair, security, and compliance.

For a shopping site project built from scratch, an AI coding tool has limited value if it only generates a large amount of page code. It becomes truly productive only when it can reasonably decompose tasks, maintain engineering structure, encapsulate state and data, handle form exceptions, support responsive layouts, and continuously fix problems through runtime validation.

For developers and enterprises, the best way to use these tools is not to treat them as “one-click generators,” but to integrate them into a standardized development process: clarify requirements, constrain structure, generate in stages, validate automatically, review manually, and iterate continuously. Only in this way can AI coding tools move from experimental toys to stable, controllable, and sustainable development infrastructure.