Evaluating LLMs in Front-End Development: From Page Generation to Requirement Comprehension

1. Background: A Capability Test Driven by a Real Project

LLM-assisted programming has moved beyond the question of “can it write code” into the stage of “can it understand requirements and deliver usable modules.” In web front-end scenarios especially, developers are not dealing with algorithm problems or isolated functions, but with composite tasks that carry business semantics: login flows, list filtering, form validation, state interactions. A model must simultaneously handle visual structure, interaction logic, field relationships, and engineering conventions—and differences in capability quickly become magnified.

To more objectively assess the current real-world level of large models in front-end development, we selected a front-end module from a real project as the test task. The task was not a sample-level demo, but a requirements description close to an actual development document, including page structure, filter conditions, field rules, validation logic, and interaction details.

The test scope focused on three typical page types:

1. Login page: including a carousel area, form inputs, button states, and basic validation.
2. Product listing page: including filter conditions, field display, list structure, and interaction behavior.
3. Complex form page: including multi-field dependencies, validation rules, grouped layout, and submission logic.

The evaluation dimensions went beyond “does it run” to focus on “did it accurately understand and implement the documented requirements.”

2. Overall Experience: From Input to an Accessible Artifact

During testing, the first thing we noticed was a shift in the mode of generation. The model no longer returned only scattered code snippets, but attempted to produce a complete project structure and provide a directly accessible page address. This means the deliverable was upgraded from “suggestion-level code” to a “runnable prototype,” which has real value for developer acceptance and secondary modification.

That said, the actual experience also exposed some engineering-level issues:

• Response stability: During peak hours, queuing or rate-limiting prompts could appear, requiring multiple attempts to enter the generation flow.
• Generation speed: A complete project took roughly half an hour from start to finish, noticeably slower than conventional code completion tools.
• Default tech stack choice: When no language was explicitly specified, the model defaulted to the React stack, indicating that mainstream front-end frameworks remain a high-weight path in its training data.

These characteristics suggest that LLM-assisted front-end development is better suited to “offline task generation” than to high-frequency interactive completion. It behaves more like a junior developer independently producing a first draft than like an instant-response assistant.

3. Login Page: High Completeness, Close to Deliverable Quality

The login page was the best-performing page type in this test. The carousel display area explicitly required in the requirements document was successfully implemented, and the form inputs, button states, basic validation, and visual layout showed no obvious omissions.

From an engineering perspective, this page reached the level of “ready to ship after a few tweaks.” The reason is that login pages appear extremely frequently in public code repositories and training data, their patterns are relatively fixed, and the model has ample basis for pattern matching. At the same time, the business complexity of a login page is low, the number of fields is limited, and the interaction logic is mostly linear—all of which provide friendly generation conditions for the model.

This shows that when a task pattern appears frequently and its business semantics are simple, a large model can stably output code close to production quality. But one caveat is worth noting: the high completeness of the login page may lead developers to overestimate the model’s capabilities in other scenarios.

4. Listing Page: Functional Understanding Shows Clear Shortcomings

Once we moved to the product listing page, problems began to surface in concentrated form. The most typical defect was that filter conditions explicitly described in the document were not implemented. This was not an isolated incident on a single page, but a problem shared across multiple listing pages, with systematic characteristics.

The challenge of a listing page is that it does not merely display data—it requires understanding filter dimensions, condition combinations, data flow, and state management. For requirements such as “filter by category,” “filter by price range,” or “multi-condition combined queries,” the model needs to build a complete interaction state model. Once implicit logic or compound conditions appear in the requirements description, the model tends to produce a “right structure, missing function” outcome.

Specific manifestations included:

• Page structure was basically correct; list cards and table areas could be generated;
• Filter components were missing or generated only as static placeholders, with no interaction logic bound;
• Insufficient understanding of field filter relationships and default states;
• Incomplete coverage of edge cases such as list loading, empty states, and error prompts.

This result indicates that large models already have decent capability in “page skeleton generation,” but still show a clear gap in “interaction semantic understanding.” Listing pages are precisely one of the scenarios that most test interaction semantics.

5. Form Page: Insufficient Ability to Reproduce Complex Requirements

The form page test results further confirmed the above judgment. The page could run overall, but there were many inconsistencies with the documentation in terms of field completeness, validation logic, and interaction details.

In complex form scenarios especially, the model’s problems manifested as:

1. Missing fields or incorrect order: Some fields were not generated, or their order did not match the requirements document.
2. Missing validation rules: Required fields, format validation, and dependency validation were not fully implemented.
3. Broken field dependencies: Dependency logic such as “load cities after selecting a province” or “show different field groups after selecting a type” was easily overlooked.
4. Incomplete error prompts: Error localization and prompt information after submission failure were weak.

Even when the requirements document was written relatively clearly, the model still struggled to reproduce it completely. This exposes a key issue: the current ability of large models to transform “natural language requirements into front-end state logic” remains limited. It can write an isolated component well, but it struggles to build a consistent and complete logic chain across multiple fields and rules.

6. Visual Style: Engineering Usability Over Design Expression

In terms of overall visual style, the generated pages were at a level of “better than an average prototype, but still some distance from a mature design system.” The pages leaned toward a clean, engineering-oriented style, with no obvious visual highlights and no highly customized or branded feel.

Specifically:

• Basic layout and spacing were handled reasonably;
• Component visuals were passable but unremarkable;
• Design details were lacking, such as motion effects, micro-interactions, and visual hierarchy optimization;
• There was still a gap compared with mature component libraries or design templates.

Therefore, the front-end pages currently generated by large models are better positioned as “engineering validation prototypes,” used to quickly confirm functional ideas and page structure, rather than delivered directly as final product interfaces.

7. Breaking Down the Gap: Depth of Understanding Is the Core Dividing Line

In a horizontal comparison of similar tasks, the gaps between different models were mainly concentrated in the following dimensions:

Dimension	Stronger performer	Weaker performer
Login page completeness	Basically consistent	Basically consistent
Listing page filter logic	Accurately understood and implemented	Filter functionality missing
Form dependencies and validation	Field relationships fully reproduced	Multiple logic omissions
Generation speed	Noticeably faster	Noticeably slower
Design expressiveness	Closer to a mature solution	More engineering-oriented

The core conclusion from this comparison is: depth of understanding is the current dividing line for large-model front-end capability. Page skeleton generation is no longer the main bottleneck. Whether the model can accurately understand field relationships, interaction dependencies, state transitions, and boundary conditions determines the usability level of the generated artifact.

8. What It Means for Developers: Usage Recommendations and Acceptance Criteria

Based on this test, the following recommendations can be formed for the practical use of LLM-assisted front-end development:

1. Suitable as a First-Draft Accelerator

For high-frequency standard scenarios such as login pages, basic detail pages, and simple configuration pages, it is safe to let the model generate the first draft and then make local modifications.

2. Complex Interactions Must Be Broken Down

Scenarios such as list filtering, dependent forms, and multi-step flows should not be handed to the model all at once. A more effective approach is to split them into multiple subtasks and clearly describe field rules and state relationships one by one.

3. Focus Acceptance on “Logical Consistency”

Do not just check whether the page renders successfully. Focus on checking whether filters take effect, whether validation triggers, whether fields are linked, and whether empty states and error prompts are complete.

4. Visual Design Still Requires Human Intervention

If a project has design quality requirements, model output is better used as a structural reference, and the final styling should still be refined in combination with a mature design system.

9. Conclusion: Capability Boundaries Are Clear, and the Direction of Evolution Is Defined

This real-project test demonstrated the current real level of LLM-assisted front-end development:

• What it can do: generate a complete project skeleton, implement standard pages, and produce accessible prototypes;
• What it does not do well: complex filtering, dependent forms, detailed validation, and visual design;
• Core weakness: depth of requirement understanding and interaction state modeling.

The value of LLM-assisted front-end development can no longer be ignored, but it is still some distance from “reliably completing real business modules independently.” For development teams, the most pragmatic strategy is not full reliance, but positioning it as an efficient prototype generator and a substitute for repetitive labor, while retaining necessary human control over critical interactions and design.

In the future, as models improve their understanding of front-end state management, component interactions, and design systems, we have reason to expect higher-quality generation results that are closer to production standards. But until then, clear requirement descriptions, reasonable task decomposition, and strict acceptance processes remain the key to ensuring delivery quality.