Frontend Engineering with Domestic LLMs: Capability Evaluation and AI Evolution

Frontend Engineering with Domestic LLMs: Capability Evaluation and AI Evolution

As the iteration frequency of large language models accelerates, discussions about whether certain domestic models have reached industry-leading performance levels are widespread. On social platforms, one can easily find enthusiastic claims about new UI tools and top-tier domestic models. However, as developers with rigorous engineering mindsets, we must look past the hype and return to real-world development scenarios, using actual project requirements to test the true capabilities of these models.

This technical evaluation did not focus on a specific programming language. Instead, it tasked the model with building a complete frontend project with real business complexity from scratch. Through this process, we objectively analyze the current capabilities of domestic LLMs in code generation, logic comprehension, and engineering implementation, while exploring the implications for the open-source ecosystem and broader AI industry development.

1. Engineering Experience: From Resource Queues to Deliverables

When invoking LLMs for complex project generation, the first challenge is often concurrent resource scheduling. During the initial testing phase, the system repeatedly prompted that there were too many concurrent users, requiring queuing or a paid tier. It took multiple session retries to successfully obtain computing resources. This indicates that under imbalanced supply and demand for computing power, free or basic-tier API calls often face strict rate limiting.

Once the generation phase began, the model autonomously selected a mainstream frontend framework for project construction. The entire generation process took over half an hour, covering dependency installation, directory structure creation, and core page code output. Compared to lightweight interactions that only generate code snippets, full-project generation naturally requires more time. However, its core advantage is that the final deliverable was not just scattered code strings, but a complete frontend engineering artifact with a running environment, directly accessible via local services. This leap from “code snippet generation” to “runnable project delivery” is a crucial indicator for evaluating the engineering maturity of AI coding tools.

2. Login Page Implementation: Solid Basic UI Construction

For the first module, the login page, the model demonstrated solid basic UI construction capabilities. Based on the requirement for a carousel display, the model accurately created the carousel component and implemented basic auto-play and switching interactions. The overall page structure was clear, and interactive components like input fields and buttons had no missing functionality.

From an engineering usability perspective, the completion level of this login page was ready for joint debugging and minor adjustments before deployment. This shows that for display-oriented pages with relatively simple structures and linear interaction logic, current LLMs can effectively capture design intent and convert it into W3C-compliant, reusable component code.

3. List Page Implementation: Slowdown in Complex Semantic Understanding

However, when tackling the product list page, which involves complex business logic, the model’s shortcomings became apparent. The requirement document specified multi-dimensional data filters (such as status filtering and time range selection) with corresponding linked interaction logic. Yet, the actual generated code only stacked the UI structures for the filters, failing to implement data layer binding and filtering interactions.

This omission was not an isolated incident but occurred across multiple similar list pages. It indicates that when facing long-context instructions with multiple constraints, the model’s attention mechanism experiences information decay. Although it correctly generated the external skeleton of tables or lists, it fell short in the deeper dimension of functional comprehension and business logic mapping. The model tended to match visual prototypes rather than execute rigorous business logic contracts.

4. Form Page Implementation: Missing Details and Validation Logic

The form page is one of the frontend scenarios that most tests a model’s logical rigor. A complete form involves not only rendering various field types but also linked validation between fields, asynchronous data submission, and error capture mechanisms.

In this test, while the generated form page could run the basic rendering process, it deviated significantly from deeper requirements. For example, conditional display of specific fields based on prerequisites, and custom validators for special formats (like phone numbers and emails), were not accurately implemented. This exposes the model’s blind spots in handling conditional branching logic and state management. Faced with complex engineering contracts, the LLM’s hallucinations and shortcut thinking led to unmet standards in interaction details and validation closures.

5. Overall Style: Engineering-Focused, Lacking Design Aesthetics

Visually and stylistically, the model’s output was mediocre. The generated UI was more interactive than a static prototype, but compared to mature component libraries (like enterprise-level UI templates with strict design specs), it appeared immature in details such as visual consistency, whitespace ratio, and color psychology.

Its code artifacts showed a clear “engineering-first” characteristic: focusing on functional implementation and structural decoupling, but lacking stunning aesthetics or polished user experience. The overall positioning can be summarized as engineering usability far exceeding visual innovation.

6. Industry Benchmarking: Deep Understanding Defines the Technical Ceiling

To objectively benchmark the current level, we fed the exact same requirement document to an industry-leading closed-source model for a parallel test, revealing clear comparative dimensions:

1. Login Page: Both models achieved similar completion levels, showing no significant generational gap in basic UI generation.
2. List Page: The top-tier model showed higher precision in parsing filter conditions and field data structures, successfully restoring the binding logic between data flow and views.
3. Form Page: The top-tier model demonstrated a significant advantage in implementing field associations and dynamic validation rules, accurately executing complex interaction contracts.
4. Generation Efficiency: The top-tier model completed the project scaffolding and core code output in minutes. Limited by resource scheduling or architectural optimization, the domestic model took significantly longer.

The essence of this gap lies not in the volume of computing power, but in the model’s capability for “deep semantic understanding” and “complex instruction following.”

7. Open-Source Ecosystem and AI Development Significance

When examining “domestic models benchmarking against industry leaders,” the core value lies not in whether the current version surpasses others in every pixel-level detail, but in its driving force for the open-source ecosystem and long-term AI development.

1. Bridging the Technology Gap and Democratizing Base Capabilities
Strengthening open-source attributes means releasing complex code generation and UI construction capabilities from a few oligopolies. Even if the current version lags in complex logic understanding, it provides small and medium development teams with a base that can be privately deployed and fine-tuned. This significantly lowers the barrier to accessing cutting-edge AI technology.

2. Promoting Industry Collaboration and Accelerating the Data Flywheel
Discovering model shortcomings in real engineering practices (like missing list filters or inaccurate form validations) provides highly valuable negative feedback data. The open-source community can build high-quality code instruction fine-tuning datasets based on these real R&D pain points. This closed loop—application discovering defects, community providing feedback, and model iterating—is the core driving force pushing general AI into the deep waters of specific engineering fields.

3. Forcing Underlying Architectural Innovation and Compute Optimization
Issues like slow generation speed and concurrent queuing expose not just engineering scheduling bottlenecks, but also optimization opportunities in underlying inference architectures and memory management. This requires continuous breakthroughs in underlying technologies like MoE (Mixture of Experts), KV Cache quantization, and operator fusion. When domestic models dare to compete directly with top-tier closed-source models in real projects, this pressure acts as a catalyst for technical breakthroughs.

In summary, current domestic LLMs have shown solid progress in frontend engineering practices, transitioning from “toys” to “tools.” Although there is still room for improvement in deeply understanding complex business semantics, their open-source nature and continuous iteration are accumulating undeniable foundational power for the intelligent transformation of the software engineering industry. Developers should rationally embrace their current imperfections and, through real code collaboration, jointly witness and drive the prosperity of this technological ecosystem.