Evaluating LLM Code Generation: From Requirement Documents to Runable Frontend Projects

Introduction: Moving from Benchmarks to Engineering Reality

As large language models (LLMs) accelerate their integration into software development, the ability to generate runnable code from natural language has transitioned from a conceptual proof-of-concept to an engineering practice. For development teams, the critical questions are no longer about small percentage improvements on leaderboards, but rather:

• Can a model accurately interpret a real, structured product requirement document?
• Is the output a runnable, iterable project rather than scattered code snippets?
• For complex interactions such as list filtering, form validation, and state linkage, does the model’s comprehension reach engineering-grade usability?
• Compared to manual development, how much efficiency gain does the end-to-end process from requirement to project deliver?

To answer these questions, this evaluation uses a typical enterprise-grade frontend requirement. The same structured specification was given to different LLMs for implementation. The focus is on general capability assessment, not on ranking specific models or cloud platforms.

Test Methodology and Evaluation Framework

Test Requirement Design

The test covers three page types that are both common and most likely to expose model weaknesses:

Page Type	Key Evaluation Focus
Login Page	Basic layout, carousel, form interaction, frontend validation
List Page	Multi-condition filtering, field structure comprehension, data state rendering
Form Page	Field relationships, validation logic, interaction details, component nesting

The requirement document follows real-world writing conventions and includes:

• Page hierarchy and structure descriptions
• Field-level requirement specifications
• Trigger logic for filter conditions
• Validation rules and error message requirements
• Conventions for interaction states (loading, empty, and error states)

Evaluation Dimensions

Instead of relying on automated benchmark scores, this evaluation applies four engineering-oriented dimensions for manual review and annotation:

Dimension 1: Requirement Comprehension Accuracy
  - Does the model correctly identify explicit requirements?
  - Does it miss implicit constraints (e.g., field dependencies, conditional trigger rules)?
Dimension 2: Page Fidelity
  - Does the page structure align with the requirement document?
  - Are UI details (spacing, hierarchy, feedback states) complete?
Dimension 3: Interaction Logic Completeness
  - Does filtering actually update list data?
  - Does form validation cover all required scenarios?
  - Is state management clear and maintainable?
Dimension 4: Generation Efficiency and Runnable Quality
  - Time from requirement input to runnable project output
  - Can the generated project be launched and functionally verified directly?
  - Is the code structure conducive to subsequent manual modifications?

Test Findings and Observations

Usage Experience and Service Stability

Service stability of the model inference API—a factor often overlooked—directly impacts development efficiency. During the test, the following issues were observed:

• Access restrictions during peak hours, requiring queuing or switching to a paid tier
• Generation speed varying significantly based on model size and current load; response time for the same requirement fluctuated across different time periods
• Increased likelihood of interrupted generation tasks after free quota exhaustion

These observations highlight the need to assess QPS limits, SLA guarantees, and fallback strategies before integrating an LLM into formal development workflows. For teams with high stability requirements, cloud-hosted inference services with elastic scaling and monitoring are a more appropriate choice.

Login Page: Solid Baseline Performance

For the login page, the model demonstrated sound fundamental capabilities:

• The carousel component was correctly identified and implemented
• Overall page structure was complete, with no obvious missing functionality
• Output was a full project rather than isolated code snippets, allowing direct access and verification

From an engineering perspective, the generated code reached a “modify-and-use” level. However, the login page’s inherent complexity is limited—its layout patterns are well-established, and numerous mature templates exist in training data. Thus, strong performance in this scenario does not fully represent model behavior in more complex situations.

List Page: Significant Gap in Functional Comprehension

The list page exposed the most concentrated problems in this evaluation. The requirement document explicitly specified multiple filter conditions and their corresponding linked query logic. However, in the actual generated output:

• Filter conditions existed only as visual layout elements; clicking them did not trigger list data updates
• No linkage existed between multiple filter conditions
• Some displayed fields had data structures inconsistent with the actual field names, requiring additional modifications for future API integration

These issues indicate a clear gap between a model’s ability to “render components” and its ability to “understand the business logic behind those components.” Autoregressive code generation tends to reproduce the most common list page patterns from training data; when interaction logic exceeds common patterns, the model often degrades into a “static page generator.” This suggests that in practice, list page requirements should be decomposed into smaller, independently verifiable units, and interaction logic should be manually re-checked immediately after generation.

Form Page: Weak Reproduction of Field Relationships and Validation Logic

The form page exposed similar—but more severe—issues:

• Approximately 60% of field-level validation rules in the requirement document were not implemented
• Conditional show/hide logic between fields was not correctly mapped to state management
• Significant loss of custom component nesting and interaction details

Complex forms typically involve multi-layered validation rules and cross-field dependencies, demanding robust logical reasoning from the model. Current models tend to generate a form structure that superficially resembles the requirement but contains numerous breaks in data flow and validation chains. Developers working from such output are often faced with “refactoring” a significant portion of the code rather than simply making minor “modifications.”

Generation Efficiency and Output Style

In terms of efficiency, a notable pattern emerged: stronger models are not necessarily faster, but those with better requirement comprehension lead to significantly less rework downstream.

If only “time to first output” is measured, lighter models may appear faster. However, when calculating “total time from generation to deliverable,” models with deeper comprehension often prevail, as they reduce the developer’s secondary correction workload.

Regarding UI style, the generated output leaned toward a clean, engineering-oriented aesthetic, still far from “product-level polish.” For internal systems or MVP validation scenarios, this quality is acceptable; for consumer-facing products, pairing with mature design systems or style guides is still necessary to elevate visual quality.

Root Cause Analysis: Why Comprehension Fails in Complex Interactive Scenarios

The test results can be attributed to several systemic limitations of current LLMs in code generation:

1. Long-Range Dependency and Attention Decay

Requirements for list and form pages often contain significantly more text than those for login pages. A single paragraph describing filter logic may depend on a field structure defined three pages earlier. When processing long contexts, models exhibit attention decay, causing “previously mentioned fields” to lose their association with “later filter logic.”

2. Pattern Overfitting in Training Data

The fundamental nature of LLM code generation is to output the most probable code pattern from training data given a prompt. When requirements deviate from common patterns—such as dual-conditional linked filtering or nested dynamic forms—the model tends to “fall back” to the most frequent, safest static implementation.

3. Absence of Runtime Feedback Mechanisms

During generation, the model never actually “executes” the code, so it cannot discover logic errors through runtime results. Visual code generation can be improved through screenshot comparisons with the requirement, but gaps in business logic remain invisible in pure text generation stages. Emerging “generate-run-error-fix” closed-loop tools represent an important direction for evolving general capabilities.

Actionable Recommendations for Improving LLM Code Generation Quality

The following recommendations are model-agnostic and applicable to any team integrating LLMs into their development workflows.

1. Structure and Granularize Requirement Documents

Do not input the entire product requirements document (PRD) into the model at once. Instead, split the requirement into multiple generation units based on page/feature modules. Each unit’s input should include:

• All field definitions relevant to that module
• Interaction trigger conditions and expected state changes
• The component library to use (if not specified, the model may mix incompatible styles)

2. Explicitly Declare “No-Skip” Rules in the Prompt

For example, for a list page, the prompt may include:

Please implement the following requirements strictly:
1. All filter conditions must trigger actual data filtering logic;
2. Different filter conditions must support linkage;
3. List data changes must show a loading state;
4. Do not omit any requirement point. If it cannot be implemented, clearly state so in the output.

Such “negative constraints” effectively reduce the likelihood of the model silently skipping steps.

3. Adopt a “Generate-Run-Verify” Closed-Loop Automation Workflow

Using cloud-native continuous integration (CI) tools, teams can build an automated pipeline as follows:

Model generates code
    ↓
Push to code repository
    ↓
Trigger automated build and preview deployment
    ↓
Run basic end-to-end (E2E) tests or screenshot comparison
    ↓
Feed failure information back to the generation step to trigger fixes

The foundational capabilities required for this pipeline—code hosting, CI tools, preview environments, and object storage for static resources—are all available as standard offerings on major cloud platforms.

4. Integrate General Capabilities via an Enterprise Development Platform Middle Layer

For teams with the necessary resources, it is recommended that developers do not interact with the model directly. Instead, an internal development platform should encapsulate an “AI gateway” to uniformly handle:

• Preprocessing of requirement text and context augmentation
• Injection of engineering templates (directory structure, component library versions, coding standards)
• Code style and linting checks on generated output
• Security and compliance filtering

The advantage of this architecture is that model capability evolution is decoupled from developer habits. Teams can swap underlying models more smoothly without changing their workflows.

Implications for AI-Assisted Development Technology Selection

The test yields several conclusions that are directly relevant to technology selection:

First, complex interaction scenarios are the capability watershed. For template-based pages like login screens, most models perform adequately. The real differentiation appears in scenarios requiring long-range comprehension and logical reasoning, such as list filtering and form field linkage. Teams should benchmark models using these types of requirements.

Second, “runnable” does not equal “deliverable.” Generating a project that opens in a browser is only the starting point. Subsequent correction of interaction logic, adaptation to coding standards, and API integration often consume more time. When evaluating models, the focus should be on “end-to-end delivery efficiency” rather than superficial completion.

Third, cloud inference capability is as important as model capability. In production, the practical usability of a model depends on the stability, concurrency, and monitoring of its hosted service. Choosing inference services with elastic scaling and high-availability guarantees directly impacts team efficiency and experience. Cloud-hosted model services typically offer more complete resource management and operational capabilities than local deployments.

Fourth, agentic workflows represent the inevitable trend. Single-shot code generation offers limited value. The more effective future model involves LLMs participating in the complete workflow of “requirement decomposition → code generation → automated testing → fix iteration → deployment preview,” using multi-turn runtime feedback to approach engineering readiness. This direction demands higher platform orchestration, tool invocation, and observability capabilities. General cloud platform services such as function computing, message queues, and log services provide foundational support for such workflows.

Conclusion

This evaluation used a realistic frontend requirement to systematically assess the actual performance of current LLMs in code generation tasks. The results show that models achieve high completion on template-based pages (such as login pages), but exhibit systemic deficiencies in scenarios requiring deep business logic comprehension—including list filtering, complex form validation, and field relationship mapping.

These findings do not negate the value of LLMs in software development. Rather, they clarify the most effective way to apply them at this stage: treat the model as an “assistant executor,” not a “complete deliverer.” Developers need to invest more engineering practice in requirement decomposition, prompt design, automated verification, and process control. Meanwhile, leveraging cloud platforms for continuous integration, deployment preview, and monitoring enables a closed loop of generation, verification, and repair.

With the right workflow and platform support, LLM code generation can significantly improve efficiency in prototyping and basic feature development. For logic-intensive core modules, human design and code review remain critical to quality control. As reasoning capabilities, long-context processing, and runtime feedback mechanisms continue to advance, AI-assisted programming is expected to cover more complex scenarios and become deeply embedded across all stages of the software engineering lifecycle.