Evaluating Chinese LLMs for Code Generation: A Real-World Frontend Project Test

Evaluating Chinese LLMs for Code Generation: A Real-World Frontend Project Test

As large language model technology evolves rapidly, Chinese LLMs are being applied more widely in code generation and programming assistance. But how do they actually perform in real business scenarios? Can they genuinely replace parts of basic engineering development? This article uses a real web frontend project to test and analyze the code generation capabilities of a commonly used industry LLM, exploring its engineering usability and directions for improvement.

1. Test Background and Baseline Setup

To objectively assess the model’s engineering practicality, this test selected a real frontend project involving multi-page interactions, data flow, and complex validation logic. The evaluation criteria were not simply “does the code run,” but focused on three dimensions:

1. Requirement comprehension accuracy: Can the model accurately extract filtering conditions, field relationships, and interaction logic from the documentation?
2. Engineering output completeness: Is the generated code a runnable, complete project rather than isolated code snippets?
3. UI visual and interaction fidelity: Does the page style meet the standard for direct secondary development?

During the test, a requirements document was fed to a Chinese LLM without specifying a particular tech stack. The model defaulted to a mainstream frontend framework for generation.

2. Generation Efficiency and Basic Experience

During actual invocation, free-tier access often encountered queuing or rate-limiting prompts due to compute resource constraints. After several retries, the model began parsing the project structure and progressively generating code.

The entire generation process took roughly half an hour, showing no significant speed advantage over manual coding. However, in terms of output form, the model directly produced a standalone deployable project URL with a basic engineering directory structure. This indicates the model has moved beyond the “code snippet completion” stage toward “full-stack project scaffolding generation,” saving developers time on environment configuration and initialization.

3. Deep Analysis of Core Page Fidelity

3.1 Login Page: High Completeness, Engineering-Ready

For the login page, the model demonstrated solid foundational capabilities. Beyond standard form inputs and a login button, it implemented interactive features such as a carousel background and basic form validation based on additional descriptions in the requirements document. The page had no obvious gaps and the code structure was clear—at a “ready to ship with minor polish” level. This suggests that for single-page, low-complexity logic scenarios, the LLM already offers considerable substitution value.

3.2 List Page: Shortcomings in Complex Logic Comprehension

When it came to the business-complex list page, the model’s weaknesses surfaced. The requirements document explicitly defined multi-dimensional filter conditions (such as dropdown selects, cascading selects, and time range filters) along with corresponding interaction logic, but the model only generated a basic table display structure—the filter functionality was entirely missing.

This is not an isolated incident but reflects the current limitations of LLMs in processing long contexts and complex conditional branches. The model tends to generate a page skeleton conforming to conventional patterns first, and when faced with non-standard or high-density logic descriptions, “attention loss” easily occurs, degrading functional comprehension.

3.3 Form Page: Insufficient Field Relationship and Validation Fidelity

The form page is one of the most critical components in a backend management system. In this test, the generated form page could render and submit normally, but details clearly deviated from the requirements document.

For example, the model failed to accurately reproduce inter-field linkage controls (e.g., selecting type A should display field B), complex regex validation logic, default values for specific fields, and asynchronous data source binding. This indicates that the model’s depth of understanding in translating “business rules described in natural language” into “rigorous program control flow” remains insufficient.

3.4 Overall UI Style: Engineering-Oriented, Lacking Design Polish

From a visual design perspective, the generated pages had a conventional overall style. While better than a typical hand-drawn prototype, a gap remains compared to the best practice of directly using a mature UI component library combined with atomic CSS. The page layout leaned toward simple stacking, lacking refined spacing control, color hierarchy, and micro-interaction animations—presenting an overall characteristic of “engineering usability far exceeding product aesthetics.”

4. Capability Comparison and Industry Pain Points

Comparing horizontally with top-tier code generation solutions, clear gaps emerge primarily in “depth of understanding”:

• Basic page generation: Chinese models and top-tier models show comparable completeness, both capable of rapidly producing usable skeletons.
• Complex logic fidelity: Top-tier models can more precisely capture document semantics when handling list filtering and form field relationships, even automatically supplementing reasonable edge-case handling. Some Chinese models fall short in this area.
• Generation efficiency: Top-tier models typically complete full output within minutes, while some Chinese models take considerably longer due to compute scheduling or architectural design constraints.

5. Practical Recommendations for AI Engineering Adoption

Despite current bottlenecks in complex logic handling, the engineering value of code generation models should not be overlooked. To maximize the use of LLMs for improving development efficiency, developers and enterprises can adopt the following strategies:

1. Structured decomposition of requirements documents: Avoid feeding lengthy, complex requirements to the model all at once. Split the project into independent page modules, and within each module, use structured lists (such as Markdown tables) to clearly define fields, types, validation rules, and linkage conditions—reducing the model’s parsing difficulty.
2. “Skeleton generation + manual filling” collaboration model: Do not expect the model to complete a perfect project with complex business logic in one pass. Position the model as an “advanced scaffolding tool”—use it to rapidly generate project directories, basic routing, and standard page layouts, then have developers manually take over core business logic implementation.
3. Inject enterprise component library context: To improve the usability of generated code, inject the base specifications of internally used UI component libraries into the prompt, or instruct the model to use a mature component library standard—enhancing the consistency and design quality of generated code.

6. Future Outlook for LLM-Assisted Programming

The progress of Chinese LLMs in code generation is evident. From simple code completion to full-stack project generation, they have demonstrated enormous potential. The current bottleneck in complex logic understanding is essentially a boundary issue of model reasoning capability and context window management.

As model capabilities iterate—supported by general-purpose compute foundations and container platforms such as those provided by Baidu AI Cloud—LLMs are expected to understand business semantics at deeper levels in the future. By combining RAG (Retrieval-Augmented Generation) technology to inject enterprise-internal component documentation and business specifications into the model, the accuracy and engineering quality of code generation will be greatly enhanced, truly achieving an AI-driven leap in development efficiency.