Gemini app feed UI: Why Google’s Shift to a Scrollable Prompt Feed Changes AI App UX

Quick answer

Gemini app feed UI is Google’s experimental redesign that replaces a chat-first screen with a vertically scrollable feed of suggested prompts paired with eye-catching photos, shortcut buttons (e.g., Create Image, Deep Research), and visual prompt affordances to improve prompt discoverability and engagement. The teardown reported by Android Authority and summarized by TechCrunch surfaced UI assets that point to a feed-first home screen with cards like “Teleport me to deep space,” “Turn my drawing into a storybook,” and one-tap actions for image generation through Google’s image stack (Nano Banana). This pattern aims to steer users from prompt recall to prompt discovery, lowering cognitive load and increasing conversion from curiosity to action (TechCrunch; Android Authority).

Intro — What this post covers

This article unpacks the Gemini app feed UI experiment and analyzes why a shift from chatbot-first to a scrollable AI feed matters for designers, product managers, and AI-savvy users. We cover:
- A concise description of the discovered UI elements and how they differ from the chat paradigm.
- UX and product implications for prompt discoverability, retention, and monetization.
- Practical recommendations for product teams building prompt feed design and mobile AI interfaces.
Key takeaways:
- Google is testing a visual revamp that shifts Gemini from a chat-first UI to a scrollable AI feed modeled around discovery and action.
- The feed emphasizes visual inspiration and shortcut actions, likely influenced by visual-first apps like Sora that have proven high discoverability and engagement.
- This experiment signals a broader trend: mobile AI interfaces increasingly adopt feed-based discovery to bridge imagination and action through images, examples, and low-friction entry points.
Why this matters for design: chat UIs excel at conversation continuity and context, but they often fail at discovery. A prompt feed converts latent user intent into immediate, actionable tasks. Think of the feed as a curated museum of possibilities—each card is an exhibit that answers “What could I ask?” and “What happens if I try?”

Background — What we know so far

The feed-first concept surfaced after a teardown of a Gemini Android build. Android Authority discovered UI artifacts indicating a home screen with shortcut buttons (e.g., “Create Image,” “Deep Research”) and a vertical feed of suggested prompts with photos. TechCrunch summarized the teardown, noting Google hasn’t publicly announced the change and a spokesperson said there’s no announcement “just yet” (TechCrunch). The leaked prompts imply a cross-modal emphasis—text prompts paired with image-generation shortcuts and a “Live” brainstorming affordance.
Current vs. planned experience:
- Current: Chatbot-style sessions that rely on users to conceive prompts or follow conversational threads.
- Planned/experimental: A discovery-first feed that surfaces inspiration, suggested tasks, and immediate actions you can take on one tap.
Notable UI elements reported:
- Shortcut actions for image generation and research workflows.
- Feed cards with visual thumbnails and evocative microcopy (“Give me a vintage or grunge look”).
- Workflow prompts such as “Brainstorm out loud with Live” that hint at real-time collaboration tools.
Why these artifacts matter: they reveal a deliberate move to reduce the friction of prompt creation and to make model capabilities more visible. The design direction also reflects competitive pressure: visual-first AI apps (Sora and others) have demonstrated strong App Store traction, and Google appears to be productizing its strengths—search signals, image models (e.g., Nano Banana), and a massive dataset—to compete in the mobile AI interface arena (TechCrunch; Android Authority).

Trend: Why feed-based AI UIs are rising

Short definition (featured-snippet friendly): A scrollable AI feed is a UI pattern that surfaces suggested prompts, examples, and visual content in a vertically scrollable layout to increase discovery, inspiration, and conversion.
Drivers behind the trend:
- Prompt discoverability: Many users don’t know how to phrase effective prompts. A feed reduces cognitive load by giving examples and showing expected outputs.
- Visual inspiration: Photos and thumbnails help users imagine outputs; visual cues bridge the gap between an abstract prompt and an expected concrete result.
- Mobile-first habits: Users are conditioned to scan feeds (social, news). Adopting those interaction patterns lowers onboarding friction for mobile AI interfaces.
- Engagement & monetization: Feeds create more surface area for feature discovery, upsells (e.g., higher-resolution images), and cross-promotions, directly impacting retention and ARPU.
Design rationale: A scrollable AI feed functions like a discovery marketplace. Where a chat is a single-thread conversation, a feed is a curated gallery—a user can quickly scan multiple ideas, preview likely outputs via images, and convert their curiosity into a task. An analogy: a culinary app that only offered a blank recipe box (chat) versus a magazine-style feed of plated dishes with “Cook this” shortcuts (feed). The latter drastically increases the likelihood of action.
Related UX keywords naturally converge here: AI app UX, prompt feed design, mobile AI interfaces, prompt discoverability, scrollable AI feed. Designers should treat the feed not as passive content but as an interactive scaffold that leads users from inspiration to output with minimal friction.

Insight: UX and product implications for Gemini app feed UI

What the feed will likely optimize for:
- Discovery → Action pipeline: Each card must clearly suggest a prompt and a next step (Try prompt, Create image, Deep Research).
- Progressive personalization: Surface prompts based on user signals (search history, saved prompts, session patterns) while avoiding invasive patterns that harm trust.
- Progressive complexity: Start users with approachable, high-conversion prompts; surface advanced workflows and multi-step recipes as users demonstrate competence.
Benefits:
- Improves prompt discoverability by lowering cognitive barriers; the feed becomes a “prompt tutor.”
- Makes capabilities more visible—image-generation, Live brainstorming, and deep research become front-and-center.
- Competes directly with visual-first apps like Sora by leveraging Google’s strengths (image models, search intent signals, contextual personalization).
Risks and UX challenges:
- Anchoring & bias: Suggested prompts can confine users’ imagination and steer them toward platform-favored use cases.
- Prompt fatigue: Repetitive or irrelevant cards reduce perceived value and encourage churn.
- Privacy and trust: Personalization requires careful data practices and transparent controls—if not handled correctly, personalization could erode trust.
Practical UX recommendations:
- Use high-quality photos and contextual microcopy that explain why a prompt matters and what the expected output looks like.
- Provide quick-action affordances: Try, Edit prompt, Save, Share. Make the path from card to output one or two taps.
- A/B test placement, density, and card complexity to measure conversion → engagement → retention funnels.
- Add one-tap rollback and clear model-behavior indicators (e.g., “This image will be generated using Nano Banana with style X”) to build trust and reduce surprise.
Example design pattern: a “compact card” shows a 1:1 image, a 10–12 character evocative title, three micro-actions (Try, Edit, Save), and an affordance that previews the expected output size/time cost. This pattern reduces cognitive load and accelerates first-time success.

Forecast: How this could reshape the AI app landscape

Short-term (3–6 months):
- Expect Google to A/B test the feed with limited Android cohorts and iterate quickly on prompt copy and card templates. Early signals to watch: time-to-first-action, prompt-to-output conversion, and immediate retention lift (DAU changes for test cohorts).
- Developers of rival mobile AI interfaces will monitor engagement metrics and likely prototype similar feed experiments.
Medium-term (6–18 months):
- If analytics show lift, other AI apps will adopt richer visual prompt discovery patterns, blurring lines between search, chat, and creative tools. Metrics to watch: prompt discoverability rate, average session depth, and retention by cohort.
- Product teams will invest more in content design and editorial flows to keep the feed fresh (seasonal prompts, curated collections, and collaborative prompts).
Long-term (18+ months):
- The industry could standardize a hybrid UI: a persistent feed for discovery and a conversational mode for extended workflows. This hybrid model becomes the norm for mobile AI interfaces.
- New design standards for prompt feed design will emerge—microcopy best practices, thumbnail taxonomies, and model-specific affordances (e.g., image model preview badges, Live collaboration indicators).
- Competitive implications: Visual, feed-first UX could help Google productize its model suite (image models like Nano Banana + search signals) and usurp market share from apps that rely solely on chat metaphors (OpenAI, Sora, etc.). The end result is a more discoverable, productized AI experience that emphasizes immediate creation.

CTA — 3-step checklist to evaluate a prompt feed UX

Try this short audit for designers and PMs evaluating a prompt feed:
1) Discoverability: Can a first-time user find useful prompts within 15 seconds? List the top 3 prompts you’d surface and test them in a usability session.
2) Actionability: Does each feed card include a clear next action (Try, Edit, Save) and an expected output preview (image, length, time)?
3) Personalization & safety: Are personalization signals transparent? Have you documented privacy defaults and built in rollback/clarity affordances to mitigate anchoring?
Want a tailored UX critique or a downloadable prompt-feed checklist? Reply with your app’s key flows or subscribe to updates — I can draft a 1-page audit focused on prompt discoverability, mobile AI interfaces, and prompt feed design.

Sources & context

- TechCrunch summary of a Gemini Android teardown: “Google’s Gemini AI app could soon be getting a big makeover” (TechCrunch). https://techcrunch.com/2025/10/03/googles-gemini-ai-app-could-soon-be-getting-a-big-makeover/
- Original teardown reporting by Android Authority (summarized in TechCrunch) revealed UI assets indicating a scrollable feed with suggested prompts and shortcut buttons.
- Related context: Sora’s App Store momentum and Google’s image model work (Nano Banana) — used to explain competitive and technical implications.
If you want, I can convert this analysis into a one-page UX spec with wireframe suggestions for a Gemini-style prompt feed (compact cards, CTAs, personalization rules).