AI models excel at creating content, but typically render it with static, predefined interfaces. Specifically, the output of LLMs is often a markdown "wall of text". Generative UI is a long standing promise, where the model generates not just the content, but the interface itself. Until now, Generative UI was not possible in a robust fashion. We demonstrate that when properly prompted and equipped with the right set of tools, a modern LLM can robustly produce high quality custom UIs for virtually any prompt. When ignoring generation speed, results generated by our implementation are overwhelmingly preferred by humans over the standard LLM markdown output. In fact, while the results generated by our implementation are worse than those crafted by human experts, they are at least comparable in 50% of cases. We show that this ability for robust Generative UI is emergent, with substantial improvements from previous models. We also create and release PAGEN, a novel dataset of expert-crafted results to aid in evaluating Generative UI implementations, as well as the results of our system for future comparisons. Interactive examples can be seen at https://generativeui.github.io
Figures & tables
Figure 1: Results from our implementation (see generativeui.github.io ).
Figure 2: A high level system overview.
Figure 3: Screenshots of Generative UI results with “Classic” styling.
Figure 4: Screenshots of Generative UI results with “Wizard Green” styling.
Format
ELO Score
Website (human expert)
1800.3
Generative UI
1736.2
Generative Markdown
1437.7
Website (top search result)
1352.2
Generative Text
1173.7
Table 1: ELO scores for user preference (LMArena).
Method
Custom Website (human expert)
Generative UI
Markdown
Website (top result)
Text
Website (experts)
-
50.0%
90.6%
91.1%
97.3%
Generative UI
35.3%
-
82.8%
90%
97.0%
Markdown
6.1%
13.9%
-
44.4%
81.1%
Website (search)
6.7%
6.7%
52.2%
-
58.9%
Text
2.7%
3.0%
1.1%
38.3%
-
Table 2: Pairwise user preferences wins (LMArena). Generative UI strongly preferred except vs. human experts.
Backbone Model
Elo Score
Output Errors
Gemini 3
1706.7
0%
Gemini 2.5 Pro
1653.6
0%
Gemini 2.5 Flash
1623.9
0%
Gemini 2.0 Flash
1332.9
29%
Gemini 2.0 Flash-Lite
1183.0
60%
Table 3: Model Performance Comparison (LMArena)
Backbone Model
Elo Score
Output Errors
Gemini 3
1739.31
0%
Gemini 2.5 Pro
1578.53
0%
Gemini 2.5 Flash
1577.74
0%
Gemini 2.0 Flash
1361.75
0%
Gemini 2.0 Flash-Lite
1242.67
1%
Table 4: Model Performance Comparison (Info-Seeking)
Prompt Ablation
ELO Score
Full Prompt
1553.23
Minimal Prompt
1496.00
No Philosophy
1450.77
Table 5: Effect of Prompting Strategy (LMArena)
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: "Explain fractals" generated web-app.
Figure 6: "History of Time Keeping Devices" generated web-app.
Figure 7: "Memory Game" generated web-app.
Figure 8: "Basketball Math" generated web-app.
Format
ELO Score
Website (human expert)
1821.5
Generative UI
1727.3
Website (top search result)
1424.3
Generative Markdown
1385.5
Generative Text
1141.4
Appendix
Table 6: ELO scores for user preference (Info-Seeking).
Method
Custom Website (human expert)
Generative UI
Markdown
Website (top result)
Text
Website (experts)
-
55.5%
98.0%
79.0%
100.0%
Generative UI
30.0%
-
90.5%
73.5%
95.5%
Markdown
0.0%
6.5%
-
36.0%
84.0%
Website (search)
4.0%
19.0%
60.0%
-
70.5%
Text
0.0%
3.5%
13.5%
24.0%
-
Appendix
Table 7: Pairwise user preferences wins (Info-Seeking). Generative UI strongly preferred except vs. human experts.
Prompt Ablation
ELO Score
Full Prompt
1551.34
Minimal Prompt
1488.27
No Philosophy
1460.39
Appendix
Table 8: Effect of Prompting Strategy (Info-Seeking)