1. Model Capability Analysis in Requirements Development

The completeness and accuracy of requirements documentation directly impact software development efficiency. Two distinct model architectures demonstrate significant differences in requirement expansion tasks:

1.1 Structured Extension Models

These models employ hierarchical attention mechanisms optimized for technical documentation processing. When handling input like [A, B, C, D], they maintain strict structural integrity:

A → A1, A2, A3
B → B1
C → C2, C3
D → D1, D2

This behavior stems from training on standardized technical specifications, enabling precise structural recognition through positional encoding and hierarchical transformers. In table-to-Markdown conversion, their pipeline architecture combines OCR recognition with text generation for format preservation.

1.2 Creative Generation Risks

Models trained on creative writing samples exhibit looser structural constraints:

A → A1
C → C2, C3
D → D2
E → E1 (unrequested feature)

This tendency toward creative association can lead to over-interpretation in multimodal scenarios, such as adding common UI elements not present in original specifications.

2. Model Selection Framework

According to Gartner’s Hype Cycle, LLMs in requirements development are in the “Peak of Inflated Expectations” phase. Four critical evaluation dimensions emerge:

1. Structural Fidelity: Preservation of input hierarchy
2. Detail Granularity: Completeness of feature decomposition
3. Error Correction Speed: Response to prompt refinements
4. Multimodal Accuracy: Non-text element recognition (tables/diagrams)

Scenario-Specific Recommendations:
| Development Scenario | Recommended Architecture | Key Metrics |
|———————————|————————————-|——————|
| Agile iterations | Structured extension | Context window ≥32k, stability >95% |
| Innovative products | Creative generation | Association score ≥4.2/5.0 |
| Legacy modernization | Hybrid knowledge-enhanced | Knowledge coverage ≥80% |

3. Prompt Engineering Best Practices

Structured prompts significantly improve output quality. Verified template:

3.1 Requirement Expansion Template

# Role Definition
Senior Product Manager with these constraints:
1. Maintain original feature order
2. Add 2-3 sub-features per item
3. Use numbered list format
4. Exclude unmentioned features
# Input Example
Features:
- User authentication
- Data visualization
- Alert system
# Output Format
User authentication:
1.1 Multi-factor auth
1.2 SSO integration
1.3 Biometric verification
...

3.2 Error Correction Strategy

Progressive refinement approach:

1. Pinpoint errors: “Item 3 should not contain E”
2. Provide examples: “Follow item 1’s pattern”
3. Limit scope: “Only correct B’s expansion”

4. Multimodal Processing Architecture

Table recognition performance depends on three coordinated modules:

4.1 Visual Encoding

ResNet-152 with enhancements:

• Text region detection branch
• Table line recognition subnetwork
• Small font optimization

4.2 Layout Analysis

Graph neural network implementation:

class LayoutGraph:
    def __init__(self):
        self.nodes = []  # Detected elements
        self.edges = []  # Spatial relationships
    def add_relation(self, elem1, elem2, relation_type):
        self.edges.append((elem1, elem2, relation_type))

4.3 Semantic Conversion

Two-stage generation:

1. Intermediate Representation (IR) generation
2. IR-to-target (Markdown/HTML) conversion

5. Enterprise Implementation Guide

For teams handling >50 annual projects:

5.1 Model Deployment Strategy

• Core requirements: Structured model (accuracy ≥98%)
• Innovation: Creative model (diversity ≥4.5)
• Legacy systems: Knowledge-enhanced model (coverage ≥90%)

5.2 Quality Control

Four-tier validation:

1. Automatic format checking
2. Model output comparison
3. Domain expert review
4. User acceptance testing

5.3 Cost Optimization

Model distillation reduces parameters:

Original → Distilled
175B → 7B (<3% accuracy loss)

6. Future Development Trends

IEEE roadmap identifies three key directions:

1. Intent Augmentation: Reinforcement learning for implicit requirement completion
2. Multimodal Fusion: Integration of code comments, UI mockups
3. Real-time Collaboration: Concurrent document editing

While LLMs show transformative potential in requirements development, technical boundaries remain. Enterprises should begin with structured models, gradually building AI-assisted systems aligned with their development workflows. The key lies in balancing creative exploration with technical precision through rigorous evaluation frameworks and engineering practices.