# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 4 Traditional and Modern Processes of Architectural Design
### GAI-Driven Architectural Design Workflow  
As shown in Figure 4, in the architectural design workflow driven by GAI, the designers first build a computational model or AI keywords according to the design objectives and constraints. GAI generates the design through the model or keywords, iterating to generate numerous variations for evaluation. If the evaluation is passed, the designer combines the AI design to refine the solution and finally integrates it into a complete design, while failed evaluations or improved ideas prompt adjustments to the model or keywords for recalculation. Figure 4: GAI-driven architectural design workflow. In the pre-analysis phase, shapefile data of the urban space are used to extract building outlines and heights, providing precise geographic and architectural information. These data are then imported into platforms such as Noah or the Rhino and Grasshopper-based solution scheduling mini-program, for initial block arrangement and optimization, ensuring compliance with planning conditions such as floor area ratio and height limits. Further performance simulations and data analysis ensured that the scheme's daylight, lighting, and wind environment complied with building codes and standards. Through these analyses, the optimal block model can be selected, or adjustments to optimize the design based on the results can be made. During the conceptual optimization phase, GAI tools such as Midjourney, Stable Diffusion, and Runway generate diverse realistic renderings, offering designers a wide range of visual references and options. The finalized conceptual scheme uses algorithms and simulations to enhance the structure, components, and building performance to maximize building quality and economic benefits. As a result, GAI workflows can be applied at different stages of the entire design process, fully reflecting the integration of technology and innovation, which not only enhances efficiency, but also ensures compliance and revolutionizes the architectural design industry. 2.1 Quantitative and Qualitative Analysis of AI's Impact on Architectural Design Efficiency and Accuracy  
Although limited studies have compared the performance of AI platforms with similar functions, key quantitative metrics like Inception Score (IS) and Frechet Inception Distance (FID) have emerged as reliable indicators due to their high discriminability and strong correlation with perceptual outcomes. However, while classification-based metrics demonstrate robust discriminability, they often fail to capture the full spectrum of design diversity. Traditional measures such as average log-likelihood have limited utility due to their weak correlation with visual quality and human perception [72; 73]. On the qualitative side, human evaluation remains a critical component, despite being subjective and time-consuming. Supplementary methods like nearest neighbor analysis can help identify potential overfitting, but their quantitative comparison capabilities are somewhat constrained [74]. In [PERSON]'s study on AI's role in the design process, platforms such as DALL-E, Midjourney, and Stable Diffusion were compared across various design phases. The study found that DALL-E and Stable Diffusion were more efficient in generating ideations, sketches, and diverse building and style variants than Midjourney, leading to faster concept development. When it comes to accurate representation of construction plans and interior/exterior designs, Stable Diffusion excels in accuracy, closely followed by DALL-E. DALL-E is also effective in handling a wide range of ideation needs, offering strong editing support. Midjourney, while lacking in features like in/out-painting and image combination, is still useful for basic sketches. Stable Diffusion strikes a balance, excelling in both generative design and detailed construction planning. Despite these valuable insights, the literature remains sparse in comprehensive, real-world comparisons of AI platforms in architectural design.