Building Generative UIs: LLM-Powered User Interface Architecture

AI interfaces are evolving from static designs to dynamic, context-aware experiences. Using modern AI and frontend frameworks, Generative UI enables real-time interface adaptation through natural language processing, streaming component generation, and dynamic data visualization.
This hands-on workshop guides developers through the Agentic Component Recognition pipeline, building adaptive UIs with practical examples. Learn to implement streaming UI generation, intent-driven visualization, and autonomous workflow orchestration while maintaining performance and trust.

Key Learning Takeaways:
✅ Build event-driven systems for streaming UI generation
✅ Process user intent for dynamic interface adaptation
✅ Connect modern frameworks with LLM backends
✅ Manage dynamic updates while maintaining application stability
✅ How I deployed production-ready generative UI components for byteanatom.com customers

Transcript

1. Transcend Hype. Discover More. Generate Your Future. Building Generative UIs With Event-Driven Agentic Component Recognition Noble Ackerson Google Developers Expert for Artificial Intelligence AI/ML Strategy Exec and Responsible Tech Risk & Safety Expert Byte an Atom Research
2. “People don't want to buy a quarter-inch drill. They want a quarter-inch hole.” —Theodore Levitt Similarly, users don't want dashboards they want trusted insights.
3. Dashboards and Static UIs suck Dashboards and current UI’s suck because one interface is designed for everyone, hard to navigate, display or filter stale data, and don't deliver insights users need or can find. Link out
4. How we got here From Static Layouts To Data-Dense Interfaces
5. Abridged history of NLU techniques → Present OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon era) LLMs (2018, GPT-2 & BERT) GenUI & ACR (Future Adaptive Interfaces)
6. Understanding Context with NER OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon era) LLMs (2018, GPT-2 & BERT) GenUI & ACR (Future Adaptive Interfaces)
7. Sentiment understanding OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon era) LLMs (2018, GPT-2 & BERT) GenUI & ACR (Future Adaptive Interfaces)
8. Recommendation Systems OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon/Netflix era) LLMs (2018, GPT-2 & BERT) GenUI & ACR (Future Adaptive Interfaces)
9. Large Language Models and Agents OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon era) LLMs (2017, Transformers, GPT-2 & BERT) GenUI & ACR (Future Adaptive Interfaces)
10. Traditional UI vs. Modern Challenges Personalized needs i.e. contextual and relevant Static interfaces that cater to a broad audience
11. What is genUI? “A generative UI (genUI) is a user interface that is dynamically generated in real time by AI to provide an experience customized to fit the user’s needs and context.” Definition source: https://www.nngroup.com/articles/generative-ui/
12. From Passive to Active AI Agentic goal oriented systems Responds to triggers Follows predetermined paths Static decision rules Anticipates needs Adapts to changing conditions Evaluates multiple approaches Rule based reactive systems If user clicks settings button → show settings menu User seems to be looking for temperature controls → proactively offer thermostat options
13. Agentic Component Recognition (ACR) OCR (1914, 1970s mainstream) NER (1995, mainstream in 2010s) Sentiment Analysis (2010s, NLP boom) Recommendation Systems (1990s, Amazon era) LLMs (2018, GPT-2 & BERT) ACR (Future of Adaptive Interfaces with GenerativeUI) ACR Workshop
14. GenUI with ACR in Practice Generative Explainable UI adapts to users to fast-track their goals efficiently. DEMO TIME ACR Workshop
15. GENUI Pipeline
16. User Context & Intent Recognition • What has the user done before? (Interaction history) • What are they trying to do now? (Current intent) • What's their current context? (Session state) ACR Workshop
17. AI State Management Implementation Best Practices • Use a production-ready vector DB for scaling (e.g., Chroma, PGVector) • Implement TTL (time-to-live) for context vectors to manage storage • Create fallback mechanisms for offline state management • Consider batching context updates for performance • Balance memory persistence with privacy requirements
18. Streaming Service Agentic Recognition & Tool Calling Implementation Best Practices • Use heartbeat messages to maintain connection • Implement stream multiplexing for multiple simultaneous updates • Add client-side buffering for smoother UI transitions • Consider WebSockets for bidirectional communication needs • Monitor stream health metrics in production ACR Workshop
19. LLM Endpoint Implementation Best Practice • Use temperature settings to balance creativity vs. determinism • Implement retry logic with backoff for transient API errors • Cache similar requests to reduce latency and costs • Consider a few-shot approach for complex component decisions • Monitor token usage and optimize prompts for efficiency ACR Workshop
20. Tool Definition (Core of Component Generation) ACR Workshop Implementation Best Practice • Use temperature settings to balance creativity vs. determinism • Implement retry logic with backoff for transient API errors • Cache similar requests to reduce latency and costs • Consider a few-shot approach for complex component decisions • Monitor token usage and optimize prompts for efficiency
21. Handling Text Streaming ACR Workshop
22. Component Implementation Example ACR Workshop
23. Streamed Component Rendering ACR Workshop Component Design Patterns • Keep components focused on a single responsibility • Implement consistent error boundaries for resilience • Use semantic HTML for accessibility • Design for both desktop and mobile viewports • Optimize re-renders for performance
24. GenUI in Prod: Conceptual Flow For Generative UI Server Client User Interaction Behavioral event… Rendered content… Rendered components… User input.. Fine-Tuned LLM 🤗stigsfoot/gemma3-2b-acr “reporting-tool”: { … } “Invoice-tool”: { ... } “RAG-tool”: { … } Tools Guardrail Interleaving Agent Tool Invoke Service Tool selection “reporting-tool”:{ … } Tool Invocation Tool execution “reporting-tool”: { } “Invoice-tool”: { } “RAG-tool”: { } Tool -> component mapping Stream function Results Update UI Data Store Memory ACR Workshop
25. IMPLEMENTATIO N The Dataset Challenge Creating the perfect training dataset
26. Sample Training Data
27. Technical Setup Balancing performance and resource constraints
28. Deployment Architecture Google Cloud Platform Cloud Run Gemma-2B-CAITI-ACR Fine-tuned LLM REST + SSE Endpoints Component Reasoning Client Application Next.js Application Component Libraries Recharts, D3, Dynamic Imports User CAITI-ACR-Core Component Registry & Gen Rendering Engine Dynamic Component Evaluation Server-Sent Events Real-time Component Streaming Updates Data Flow API Gateway
29. Beyond Personalization Personalized content vs Personalized interfaces
30. Implications for Designers Traditional UX vs Dynamic system design with component relationships and behavioral rules
31. Challenges and The Future of User Interaction Artificial Intelligence (GenAI) IS NOT a panacea, but it is capable of transforming and redefining how we interact with digital interfaces…if we think PRIVACY?
32. Key Takeaways Architecture Patterns 01 Client-Server Split State Management Streaming Concepts I’ve learned in 18 months User Experience Principles 02 Immediate Feedback Progressive Disclosure Animation/Motion LLM Integration 03 Tool Definitions Parameter Schemas Component Selection Evals!! Extension Patterns 04 Declarative Structure Separation of Concerns Modular Design
33. Generative Explainable UI in Practice Generative Explainable UI adapts to users while offering transparent, contextual explanations, ensuring users understand AI decisions and fast-track their goals efficiently.
34. WHERE DO WE GO FROM HERE?
35. Why Any of This genUI Stuff Matters Progressive disclosure and a seamless digital experience isn't just a luxury; it’s the difference between frustration and fulfillment.
36. Thanks! Questions? Resources AI Product Management Nanodegree 50% discount code using NOBLE50 GenXAI: A Survey, Conceptualization, and Research Agenda https://bit.ly/3Af2hTg https://langchain-ai.github.io/langgraph/cloud/how-tos/gen erative_ui_react/ Nielsen Norman Group on Generative UI • https://www.nngroup.com/articles/generative-ui/ • Foundational explanation of GenUI concepts and applications React.js Documentation for Streaming • https://react.dev/reference/react/experimental_use • Covers React's experimental streaming capabilities Zod Schema Validation • https://zod.dev/ • Documentation for schema validation used throughout ACR youtube.com/@stigsfoot github.com/ByteanAtomResearch
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38. From User Input to AI Tool Selection Flow ACR Workshop
40. From User Input to AI Tool Selection Flow
41. ACR Workshop
42. Tool Definitions & Agentic Tool Calling ACR Workshop