# generative UI Published articles for generative UI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Artificial Intelligence: Glossary DevFeed: [Artificial Intelligence: Glossary]() Original publisher: [Read original article]() Author: Caleb Sponheim Published: 2026-08-21T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [AI Agent](), [AI Development](), [Algorithms](), [Prompt Engineering]() Tags: [agentic-ai](), [agents](), [ai-agent](), [ai-definitions](), [ai-evals](), [ai-glossary](), [ai-glossary-for-ux](), [ai-hallucination](), [ai-terminology](), [ai-terminology-for-product-teams](), [ai-terms](), [ai-terms-for-designers](), [ai-vocabulary](), [article](), [artificial-intelligence](), [artificial-intelligence-glossary](), [context-window](), [genai-glossary](), [generative-ai](), [generative-ui](), [glossary](), [knowledge-cutoff](), [large-language-model](), [machine-learning](), [model-context-protocol](), [prompt-engineering](), [prompt-injection](), [rag](), [tokens](), [ux](), [vibe-coding]() ### AI overview A plain-language glossary of artificial-intelligence terminology used in products and UX work. It explains concepts including agents, agentic systems, AI development, algorithms, AI-generated content, and AI-related claims, while noting that terminology can vary among vendors and researchers. ### Source excerpt Plain-language definitions of the AI terms that come up in product and design work, from tokens and context windows to agents, evals, and prompt injection. ## GenUI Beyond Chat: Designing a Grammar Book with Flutter GenUI -- Part 1 DevFeed: [GenUI Beyond Chat: Designing a Grammar Book with Flutter GenUI -- Part 1]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-08-03T15:15:45Z Content type: article Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [App](), [Framework](), [personalization](), [ui](), [Learning](), [SDK]() Tags: [ai](), [app](), [article](), [feature](), [firebase](), [flutter](), [framework](), [gemini](), [generative-ui](), [genuis](), [learning](), [personalization](), [ui]() ### AI overview This article describes using Flutter GenUI to build an interactive Finnish grammar course inside a language-learning app. It focuses on generating personalized lessons from the learner's language and presenting them as Flutter pages rather than as a long Markdown response. ### Source excerpt Last year on #FlutterFlightPlans live stream, I asked the Flutter team a question that has stayed with me: Are there any use cases for Generative UI other than chat responses? Can you share some inspiration? Seth Ladd answered, "Let's see what is beyond chat together." 🚀 Since that moment, exploring GenUI beyond chat has been my primary focus. https://medium.com/media/7c53d065806a0337fcf3a8db1412ec82/href I shared my first journey with GenUI in an article series: a small GenUI framework built with Gemini structured outputs, and a migration to Flutter GenUI SDK and A2UI surfaces. Then I tried a different kind of use case: a Finnish 🇫🇮 grammar course inside my Finnish it language-learning app. In this feature, the learner browses a curriculum in their own language, opens one small grammar topic, and moves through a sequence of Flutter pages. https://medium.com/media/3ac419e10db257e4f9652bdfb9ad5165/hrefProblem Definition I had four constraints: I do not speak Finnish, and I am not a language teacher. I don't have time to manually plan and structure a grammar book's worth of micro-lessons in every language. I want interactive application UI, not a long Markdown response. Hyper-personalization is crucial. The explanation should begin from the learner's language. A Turkish learner can approach Finnish vowel harmony through Turkish vowel harmony; an English learner needs another bridge since it has no similar concept. Vowel harmony lessons in English and TurkishThe same rule does not feel equally foreign That fourth constraint is more than UI localization. Turkish speakers will find two familiar concepts in Finnish grammar: attaching grammatical markers directly to the ends of nouns, and changing suffix vowels to match the root word. For example, expressing "in" or "at" requires the exact same mental process in both languages: Turkish: ev + -de = evde (in the house) Finnish: talo + -ssa = talossa (in the house) An English speaker has a completely different starting point. ## From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK DevFeed: [From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-07-08T15:52:59Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [SDKs](), [Finite-state machine](), [JSON](), [Firebase](), [Database]() Tags: [a2ui](), [ai](), [firebase](), [flutter](), [gemini](), [generative-ui](), [how-to](), [json](), [sdk](), [state]() ### AI overview A tutorial on migrating a hand-built generative UI feature based on Gemini structured outputs to the official Flutter GenUI SDK. It describes a Finnish language-learning app that generates images on demand and uses a five-step wizard to improve image variety. ### Source excerpt Part 3 of a series "Building Image Assisted Language Learning Practice with Flutter, Firebase and Gemini." Part 1 built the image generation and annotation pipeline. Part 2 built a generative UI framework by hand, using Gemini structured outputs. This part is the migration: same feature, official Flutter GenUI SDK. A year ago, Finnish It was just a side project. This spring, I watched it at the Google Cloud Next developer keynote, and again in the Flutter keynote at Google I/O. It still feels surreal to type that. I am grateful to the Flutter and Firebase teams for the collaboration. This series is my way of giving back. I hope it makes Flutter GenUI SDK click for you the way building this feature made it click for me. Finnish it app featured in Cloud Next and Google IOQuick Recap One of the app's practice exercises is simple: show the learner an image, and they describe it in 🇫🇮 Finnish. The images are generated on demand, not fetched from a fixed image database. In Part 2 the problem was variety. A broad topic like "a native Finnish animal in its habitat" kept producing the same handful of scenes. Turning up temperature did not fix it, because it was never really a sampling problem. An under-specified prompt has a few big probability peaks, and the sampler just jitters around them. The fix was to stop asking the model to "be random" and move the entropy into the UI: a five-step wizard where the user picks the season, the setting, the story, the framing, and the visible details. Gemini designs the questions; the user provides the variety. That worked. The repetition problem was solved. The machine I built by hand To make it work, I built a small finite state machine (FSM) which walked the user through five steps. Each step asked Gemini for question set, constrained by a response schema, and parsed the JSON into a typed object that a widget could render: final response = await model.generateContent( [Content.text(_buildRefinementStateRequest(state, topic, selections ## Build your own Flutter GenUI solution with Gemini structured outputs DevFeed: [Build your own Flutter GenUI solution with Gemini structured outputs]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-06-09T12:28:28Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [Framework](), [Firebase](), [Large Language Model]() Tags: [ai-image-generator](), [building](), [firebase](), [firebase-ai-logic](), [flutter](), [framework](), [generative-ui](), [llm](), [ui]() ### AI overview This tutorial explains how to build a Flutter generative UI framework by hand with Gemini structured outputs. It introduces a guided five-step refinement flow in which Gemini generates topic-aware options, Flutter renders them as UI controls, and the selected values become structured data for a more specific image prompt. ### Source excerpt Part 2 of a series "Building Image Assisted Language Learning Practice with Flutter, Firebase and Gemini." Part 1 built the image generation and annotation pipeline. This part buids a generative UI framework by hand, using Gemini structured outputs. Part 3 is the migration: same feature, official Flutter GenUI SDK. In Part 1, I built the first image description practice pipeline for my Flutter app. A user chooses a topic, Firebase and Gemini generate a practice image for the selected topic, and Gemini 3 adds visual annotations on top of the image. That pipeline worked. But after generating enough practices, a different problem became clear. The system could produce valid images, but the variation was limited. For some topics, Gemini kept returning to the same kind of scene, even when the prompt asked for ramdomness explicitly. This post starts one step before image generation. Instead of making the final prompt longer and hoping for more variety, I changed the input flow: what if the user could shape the scene through a guided UI before the image was generated? That led to a five step refinement flow. For each step, Gemini generates topic aware options, such as season, setting, action, framing, mood, and visual details. Flutter renders those options as normal UI controls. The user chooses what they want, and the app stores those choices as structured data before turning them into a more specific image prompt. I did not begin by trying to build a GenUI framework. I was just trying to stop my image generator from repeating itself. But by the end, the system had schemas, widget catalogs, state transitions, and LLM planned screens. So let's start with the original problem. Selected topic: Shopping and errands in Finnish everyday life1. A quick recap My app helps people practice 🇫🇮 Finnish. One of the core exercises is showing the learners a picture and ask them to describe it out loud, then give feedback. The user experience is simple. The content problem behind it is n ## Google I/O 2026: The Sessions Worth Your Time DevFeed: [Google I/O 2026: The Sessions Worth Your Time]() Original publisher: [Read original article]() Author: blundell Published: 2026-06-02T17:22:00Z Content type: opinion Language: en Sources: [Blundell]() Topics: [Google](), [Artificial Intelligence](), [Android](), [Model Context Protocol](), [Jetpack](), [App]() Tags: [2026](), [agent](), [agentic](), [ai](), [android](), [androiddev](), [antigravity](), [generative-ui](), [google](), [google-i-o](), [google-io](), [intermediate](), [jetpack](), [mcp](), [reference](), [reference-androiddev-google-io]() ### AI overview An opinionated guide to the Google I/O 2026 sessions most relevant to developers. It highlights Gemini updates, agent transaction protocols, generative UI in Google Search, and Android App Functions, which are presented as being in preview and enabling assistants to call app capabilities. ### Source excerpt Like every year, I binge-watched I/O. And like every year I came out the other side with more links than time. A fire-hose of announcements, most of which you'll never touch, a handful of which will change how you work next week. Here I filter for you. Links to the sessions are inline so you [...] The post Google I/O 2026: The Sessions Worth Your Time first appeared on Blundell. ## New updates to A2UI and Flutter's GenUI package DevFeed: [New updates to A2UI and Flutter's GenUI package]() Original publisher: [Read original article]() Author: Andrew Brogdon Published: 2026-05-14T14:18:22Z Content type: tutorial Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [ui](), [Protocol (disambiguation)](), [Framework](), [Large Language Model](), [Error Handling]() Tags: [android](), [architecture](), [error-handling](), [flutter](), [framework](), [generative-ai-tools](), [generative-ui](), [ios](), [language-models](), [protocol](), [retry](), [updates]() ### AI overview This migration guide explains updates to Flutter's genui package and the A2UI protocol. The package adopts A2UI v0.9, shifts from a Structured Output First to a Prompt First approach using JSON blocks in agent responses, and separates UI state and rendering, message transport, and conversation management into distinct layers. ### Source excerpt Generative UI, or GenUI for short, is a user experience pattern in which an agent not only generates content, but also makes decisions about how that content should be displayed and made interactive for the user. For Flutter developers, implementing GenUI means using A2UI, an open protocol that defines a way for agents and clients (or "renderers") to collaborate on the composition and state of a user interface. To capitalize on this, the Flutter team built genui, a package that uses A2UI to connect with an agent and provide it with a catalog of widgets to use, and then presents those widgets to the user. Both the genui package and the A2UI protocol recently got an update! The latest release of genui introduces several architectural changes to the framework. Driven by adoption of v0.9 of the A2UI protocol, this update shifts genui from a "Structured Output First" philosophy in which A2UI messages were streamed through structured output APIs to a "Prompt First" approach in which agents include blocks of JSON as text in their responses. It also decouples the architecture, providing more direct control over how your application interacts with Large Language Models (LLMs). If you are migrating an app from v0.7.0 to v0.9.0 of the genui package, this guide covers the necessary steps, from dependency cleanup to wiring up your new chat loops. Architecture decoupling In previous versions, GenUI relied on a series of classes based on ContentGenerator. These classes hid the details of prompt construction, LLM network calls, and response parsing. The latest version of package:genui removes ContentGenerator. Instead, the framework is now split into distinct layers: Engine (SurfaceController): Manages the state and rendering of your UI. Transport (A2uiTransportAdapter): Streams messages between the agent and renderer. Facade (Conversation): Provides a high-level API for managing chat states. This decoupling means you have control over chat history, retry logic, and error handling. ## Gemini and Angular, Part II: Creating Generative UIs DevFeed: [Gemini and Angular, Part II: Creating Generative UIs]() Original publisher: [Read original article]() Author: Armen Vardanyan Published: 2026-01-22T13:48:08Z Content type: tutorial Language: en Sources: [Daniel Glejzner]() Topics: [Angular](), [Large Language Model](), [function calling](), [ui](), [text-generation](), [JSON](), [App]() Tags: [angular](), [article](), [code](), [function-calling](), [gemini](), [generative](), [generative-ui](), [json](), [llms](), [model](), [schemas](), [structured](), [text](), [text-generation]() ### AI overview This tutorial continues a series on LLMs and Gemini by exploring Generative UI in Angular. It describes dynamically rendering interactive Angular components from model responses, combining Gemini with Google's Nano Banana Pro image generation model, and using Angular signal forms. The article emphasizes minimizing boilerplate while balancing model capability, performance, and cost. ### Source excerpt Let's continue our journey into LLMs and Gemini! In the previous article, we moved beyond simple text generation and learned: how to force the model to speak our language using structured outputs (JSON schemas) how to connect the model to our actual code and logic using function calling ## Generative UI: A rich, custom, visual interactive user experience for any prompt DevFeed: [Generative UI: A rich, custom, visual interactive user experience for any prompt]() Original publisher: [Read original article]() Published: 2025-11-18T16:00:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [ui](), [Google](), [Large Language Model](), [agentic-coding](), [AI Models]() Tags: [2025](), [agentic-coding](), [ai](), [ai-models](), [generative-ai](), [generative-ui](), [google](), [human-computer-interaction-and-visualization](), [llms](), [product](), [ui]() ### AI overview Google Research introduces a generative UI implementation that enables AI models to dynamically create customized visual experiences, interactive interfaces, tools, games, web pages, and applications in response to prompts. The capability is rolling out through experiments in the Gemini app and Google Search, including AI Mode. ### Source excerpt Generative AI ## Gemini and Angular, Part II: Structured Outputs and Tool calls DevFeed: [Gemini and Angular, Part II: Structured Outputs and Tool calls]() Original publisher: [Read original article]() Author: Armen Vardanyan Published: 2025-11-04T13:42:00Z Content type: tutorial Language: en Sources: [Daniel Glejzner]() Topics: [Angular](), [Large Language Model](), [function calling](), [Google AI](), [SDKs](), [API]() Tags: [angular](), [api](), [articles](), [configuration](), [function-calling](), [gemini](), [generative-ui](), [sdk](), [tokens]() ### AI overview This tutorial continues a series on using Gemini with Angular. It explains structured outputs and function calling (tool use), showing how these capabilities can support AI-powered UI and UX decisions and provide a foundation for generative UI. The article begins building an Angular writing assistant that compares two paragraph versions through the Gemini API. ### Source excerpt Let's continue our journey into LLMs and Gemini! In the previous article, we learned how LLMs generate text, what are tokens, what configuration parameters like temperature, topP and so on mean how to create a Google Cloud project, get a Gemini API key, and use the SDK to