Evaluating LLM Frontend Code Generation: A Practical Assessment and Engineering Perspective

1. Background: When LLMs Start Writing Frontend Code

Over the past year, general-purpose large language models have improved rapidly in code generation. They have moved from completing small function snippets to producing runnable frontend projects directly from requirement documents. In developer communities, claims such as “matching a leading international solution” or “a new generation of UI generation tool” appear frequently.

However, engineering practice depends on results rather than slogans. Whether a model is truly usable in real projects must be tested against a complete requirement pipeline: Does it correctly understand the requirement document? Is the page structure reasonable? Are the interaction logic and behavior implemented? Is the generation speed acceptable? Can the output be deployed directly?

This article uses a real frontend project as a sample and evaluates LLM frontend code generation across three typical page types: a login page, a list page, and a form page. Based on the results, it discusses the engineering implications.

2. Evaluation Design: Real Requirements, Not Toy Demos

2.1 Requirement Source

The evaluation uses a frontend requirement document from a real project, covering three page types:

• Login page: includes a carousel, form inputs, button interactions, and other basic elements.
• Product list page: includes filter conditions, list rendering, pagination, and interaction logic.
• Form page: includes multi-field input, dependencies between fields, validation rules, and submission behavior.

The requirement document mainly uses natural language descriptions and does not mandate a specific technology stack. The model ultimately chose React for implementation.

2.2 Evaluation Dimensions

To avoid subjective judgments based on whether the output looks acceptable, the evaluation focuses on five dimensions:

Dimension	Description
Structural completeness	Whether the page skeleton and component breakdown are reasonable
Requirement fidelity	Whether the features explicitly described in the document are implemented
Interaction accuracy	Whether filtering, validation, and field dependencies behave correctly
Generation speed	Total time from submitting requirements to producing a runnable project
Deliverability	Whether the output can be directly accessed, deployed, and further developed

3. Test Results: Clear Differences Across Capability Levels

3.1 User Experience: Clearing the First Hurdle

In practice, the first issue was not technical but resource queuing. The tool repeatedly indicated that too many users were online and that waiting or payment was required. It became usable only after several refreshes. This is an important signal for engineering teams: if a model cannot provide stable service, it is difficult to incorporate it into a formal development workflow.

Once generation started, the total process took more than half an hour. This may be acceptable for prototype validation, but it is still far from the interactive experience of generating code while writing.

One positive point is that the output was a directly accessible project URL rather than just code snippets. This means the model produced a runnable project rather than reference code, which represents progress in the delivery format.

3.2 Login Page: Relatively High Completion

The login page was the best-performing page among the three types.

• The carousel requirement mentioned in the document was correctly implemented.
• Basic components such as form inputs and buttons were complete.
• The overall page structure was clear, with no obvious missing parts.

From an engineering perspective, this page reached a level of “usable after minor modifications.” For highly standardized pages with simple interaction logic, the model already shows meaningful practical capability.

3.3 List Page: Understanding Starts to Degrade

Problems emerged with the product list page.

The document clearly specified filter conditions and interaction logic, but the generated output had the following issues:

• The filter options were not implemented.
• The page structure was correct, but functional elements were missing.
• The same problem appeared repeatedly across multiple list pages, so it was not an isolated incident.

This suggests that the model has a systematic weakness when handling structured requirements combined with conditional logic. It can generate a page that looks like a list page, but it cannot fully reconstruct how a list page should work.

From an engineering perspective, this type of failure is costly: the structure can be reused, but the logic must be rewritten.

3.4 Form Page: Complex Requirements Are Not Fully Reproduced

The form page is a key scenario for testing how deeply a model understands requirements.

In the test, the form page was runnable overall, but:

• Some fields were not implemented according to the document.
• Validation logic was incomplete.
• Interaction details between fields were missing.

Even though the requirement document was relatively clear, the model still struggled to reconstruct complex form semantics. A form is not simply a stack of input boxes. It is a combination of field relationships, validation rules, and interaction states, which is exactly where current model capabilities remain weak.

3.5 Overall Style: Engineering Usability First

In terms of UI style, the generated results:

• Were better than most hand-written prototypes.
• Were weaker than mature component library templates.
• Leaned toward a simple, engineering-oriented design without a striking visual effect.

In short, the positioning is closer to “engineering usability over product polish.” This is sufficient for internal tools and admin systems, but consumer-facing pages still require design intervention.

4. Side-by-Side Comparison: The Main Gap Is Depth of Understanding

When the same set of requirements was given to a leading international model, a clear stratification emerged:

Page	Performance Comparison
Login page	Similar completion level
List page	The international model performed noticeably better, with more complete understanding of filtering and field structures
Form page	The international model showed a clearer advantage in reproducing field relationships, validation, and interactions
Speed	The international model took only a few minutes, while the domestic model was noticeably slower

It should be noted that the speed difference may be related to free quotas and queuing mechanisms, so it cannot be attributed solely to the model itself. However, the gap in understanding accuracy is consistent:

• Understanding conditional filtering in the document.
• Reconstructing dependencies between fields.
• Implementing validation logic.

These capabilities together determine whether a model is ready for real-world projects.

5. Engineering Implications: Open Source and the Direction of AI Development

5.1 Implications for Open Source Models

If a model can be open-sourced at a capability level close to leading international solutions, its value appears in at least three areas:

1. Private deployment: Enterprises can run the model in internal network environments to meet data compliance requirements.
2. Fine-tuning and customization: Teams can further train the model based on their own coding standards, component libraries, and business semantics.
3. Cost control: Avoiding the long-term uncertainty of pay-per-call pricing.

The precondition is that the capability must be genuinely usable. If a model only generates code with correct structure but missing logic, the value of open sourcing drops significantly.

5.2 Implications for AI Development

From a broader perspective, this evaluation reveals a trend:

• Model capability is shifting from generating code to understanding requirements.
• The competitive focus is moving from “can it write code” to “can it write code correctly.”
• The bottleneck in engineering adoption is not syntax, but semantic reconstruction.

This means future model evaluations should not rely only on demo results. Instead, they should establish evaluation benchmarks based on real projects, covering:

• Requirement understanding accuracy.
• Interaction logic reproduction rate.
• Generation speed and stability.
• Deliverability of the output.

6. Practical Recommendations: Bringing LLMs into Frontend Engineering

Based on this evaluation, the following engineering recommendations can be made:

6.1 Use Models by Scenario

• Simple pages such as login and display pages: the model can be used directly.
• Medium-complexity pages such as lists and filtering: use the model to generate the skeleton, then manually complete the logic.
• Complex forms: the model assists, but human effort leads.

6.2 Structure Requirement Documents

Model understanding of natural language still has limits. Breaking requirements into the following parts can significantly improve fidelity:

• Page structure.
• Field definitions.
• Interaction rules.
• Validation conditions.

6.3 Build an Internal Evaluation Set

Teams should maintain their own evaluation cases instead of relying on impressive community claims. The evaluation set should cover real business scenarios and be run regularly.

7. Conclusion

This evaluation shows that large language models have reached a level of engineering usability in frontend code generation, but their capabilities remain clearly uneven:

• Simple pages achieve relatively high completion.
• List and form pages still show notable weaknesses in requirement understanding and interaction reproduction.
• Generation speed and service stability remain bottlenecks for real-world adoption.

For open source models, the real value is not the claim of “matching a leading international solution,” but whether they can reliably produce deliverable code in real projects. Only when evaluation standards move from “looks good” to “runs correctly” can large language models truly enter the mainstream frontend engineering workflow.