What makes good AI UX different from regular UX?

Good AI UX has to handle output that's probably right rather than definitely right. That adds work regular UX rarely needs: showing sources, flagging uncertainty, previewing changes before they apply, letting users reverse actions and designing distinct loading states for slow, streaming responses. The basics of clarity and consistency still apply, but trust becomes the main design problem.

Should every AI product use a chat interface?

No. Several of the strongest examples here have no chat at all, such as ghost-text suggestions, in-place edits and triage suggestions. Chat suits open-ended questions and exploration. For repeated, structured tasks, putting AI inline where the work happens is usually faster and easier to trust. Many products combine both for different jobs.

How should I use these examples in my own design work?

Use them to find the interaction pattern that fits your feature, then design it for your users and data. Note how each product triggers the AI, shows progress and lets users accept or reverse the result. Avoid copying visual details like icons or colors, since those don't carry the trust that makes the pattern work.

Why does this list not include usage numbers for these products?

The companies behind these products don't publish data on how their individual AI interface patterns perform, and we don't make numbers up. The value of the list is in the patterns themselves, which are publicly visible. If you adopt one, measure acceptance, edits and reversals in your own product to see whether it works for your users.

Which pattern should an early-stage AI startup start with?

Start with the pattern that matches your core task. If your product answers questions from documents, start with citations. If it writes or edits, start with previews or diffs. If it acts on the user's behalf, start with approvals and rollback. Getting one trust pattern right early matters more than adding many AI features at once.

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Product design

AI UX Examples: 15 AI Products With Interfaces Worth Copying

AI UX examples from 15 well-known products, grouped by pattern: citations, inline edits, diffs, approvals, memory and handoff, with what to copy from each.

By Mehdi HassanOct 29, 20268 min read

The short answer

The AI UX examples worth copying solve one trust problem clearly. Perplexity and NotebookLM tie answers to sources, GitHub Copilot and Gmail make suggestions free to ignore, Cursor shows AI edits as reviewable diffs, Replit lets users roll back agent work, and ChatGPT shows and manages what it remembers. Copy the pattern, not the look.

Key takeaways

The best AI interfaces each solve one trust problem well: sourcing, reviewing, reversing or controlling.
Ghost text works because ignoring it costs nothing. Diffs work because users see every change.
Source-grounded tools tie each claim to a passage, which turns trust into something users can check.
Agent products that feel safe show their plan and offer rollback, not just a final result.
Copy the interaction pattern and adapt it to your users. Copying the visual style teaches nothing.

Part of AI Product UX: How to Design AI Features Users Trust

Which AI products have the best user experience and why?

The AI products with the best user experience make AI output easy to check, easy to correct and easy to ignore when it's wrong. They don't win on a clever chat window. They win by solving a specific trust problem, such as where an answer came from or what an edit changed, in a way users understand at a glance.

We've grouped these 15 AI interface examples by the pattern they show best, so the page works as a pattern library as well as a list of inspiration. Each entry describes what the product does well and why it works. We've left out metrics, because the companies don't publish them for these features. Products change fast, so treat the details as how these interfaces worked at the time of writing. For the principles behind them, see our hub on AI product UX.

The best AI UX examples share a handful of patterns: citations, ghost-text suggestions, in-place edits, reviewable diffs, plans and rollback, visible memory, explanations and handoff to a human. Each product below is a strong example of at least one.

Pattern	Problem it solves	Products to study
Citations and grounding	Where did this answer come from?	Perplexity, NotebookLM, ChatGPT search
Ghost-text suggestions	Help without interrupting	GitHub Copilot, Gmail Smart Compose
In-place edits	Change my text without copy and paste	Notion AI, Grammarly
Reviewable diffs	What exactly did the AI change?	Cursor
Variations and preview	Show me options before I commit	Midjourney, v0 by Vercel, Claude artifacts
Plans, checkpoints and rollback	Can I trust the agent to act?	Replit Agent
Suggestions inside workflows	AI where the work already is	Linear triage suggestions
Memory, explanation and handoff	What does it know, why, and who helps when it can't?	ChatGPT memory, Duolingo Max, Intercom Fin

Which AI interface examples handle citations best?

Perplexity, NotebookLM and ChatGPT search are the clearest examples of citations in AI answers. All three tie claims to sources at the point of reading, which is what makes a citation checkable rather than decorative.

1. Perplexity

Perplexity pairs numbered inline markers with a row of source cards above the answer. Users get claim-level links while reading and a quick sense of where the answer came from before they read at all. Copy the combination: markers for checking, cards for breadth.

2. NotebookLM

Google's NotebookLM answers from the documents a user uploads and links citations to the passages it used. Clicking a citation shows the source text, so users can verify an answer against their own material. Copy the passage-level preview; it's what our guide to AI citations UI recommends for any product that answers from documents.

3. ChatGPT search

When ChatGPT searches the web, it adds compact source links next to the relevant parts of the answer and a list of sources users can open. Copy the restraint: the links are visible without crowding the text.

Which products show inline AI suggestions and edits well?

GitHub Copilot, Gmail Smart Compose, Notion AI and Grammarly show how AI can help inside an existing workflow without a chat window. Their shared strength is that the user never has to leave the text they're working on.

4. GitHub Copilot

Copilot's inline suggestions appear as gray ghost text in the editor and are accepted with Tab. Ignoring a suggestion costs nothing; the developer just keeps typing. Copy the zero-cost dismiss, which is the main reason ghost text works.

5. Gmail Smart Compose

Smart Compose offers short phrase completions as gray text while users write email. Suggestions stay short enough to judge at a glance. Copy the length discipline: a phrase is easy to accept or ignore, a paragraph isn't.

6. Notion AI

Notion AI acts on selected text from a menu of named actions and offers options like replacing the selection or inserting the result below. Copy the choice between replace and insert, which lets users keep the original until they're sure.

7. Grammarly

Grammarly underlines issues in place and lets users accept or dismiss each suggestion individually, with a short explanation of why. Copy the per-suggestion control and the one-line reason. Our guide to inline AI UX patterns covers when each of these approaches fits.

Which AI products make edits and outputs easy to review?

Cursor, Midjourney, v0 and Claude's artifacts make AI output easy to review before it's used. Each shows the result as something to inspect, compare or iterate on, rather than a final answer to accept.

8. Cursor

Cursor shows AI code changes as diffs that users can accept or reject, rather than silently rewriting files. Copy the diff: for any edit longer than a sentence, showing additions and removals is the fastest way for users to check the AI's work.

9. Midjourney

Midjourney returns a grid of variations and lets users pick one to vary further or upscale. Copy the idea of options over a single answer for creative work, where there's no one right result.

10. v0 by Vercel

v0 generates interface code alongside a live preview, and users refine the result through follow-up prompts. Copy the preview-first loop: users judge the output by seeing it, then dig into the code only if they need to.

11. Claude artifacts

Anthropic's Claude can put substantial outputs, such as documents or code, in a separate panel beside the conversation. Copy the separation of chat and output, which keeps the thing being made from getting buried in messages. Our guide to AI loading states covers how these outputs should stream in.

Which AI agents show approvals and rollback well?

Replit Agent and Linear's triage suggestions show two ends of the autonomy spectrum: an agent that acts with checkpoints you can roll back to, and suggestions that stay inside a workflow until a person accepts them.

12. Replit Agent

Replit's agent builds and changes apps in steps and saves checkpoints users can roll back to. Copy rollback as a first-class control: users let an agent do more when they know they can return to a known good state. Our guide to agentic UX covers plans, approvals and logs in more depth.

13. Linear triage suggestions

Linear suggests properties like team, labels or assignee for incoming issues inside the triage view, and a person accepts or changes them. Copy the placement: AI suggestions sit exactly where the decision is made, not in a separate AI screen.

How to run a pattern teardown on your own product

Pick one AI feature you're planning. For each product above that uses a similar pattern, write down three things: what triggers the AI, what the user sees while it works, and how they accept, change or reverse the result. Then fill in the same three lines for your feature. Gaps usually show up in the third line, because teams design the output and forget the reversal.

It's a short exercise with screenshots, and it's far cheaper than building the wrong control.

Which products handle memory, explanations and handoff?

ChatGPT's memory, Duolingo Max and Intercom Fin show three quieter patterns that matter as AI gets more personal and more customer-facing: showing what the AI remembers, explaining a decision, and handing off to a human.

14. ChatGPT memory and Duolingo Max

ChatGPT tells users when it has saved something to memory and lets them review and delete saved memories in settings. Copy the visible notice plus a place to manage it. Duolingo Max adds AI explanations of why an answer was right or wrong. Copy the timing: the explanation arrives at the moment the learner wants to know why.

15. Intercom Fin

Intercom's Fin answers support questions and hands conversations to a human agent when it can't resolve them. Copy the clear handoff, which keeps the customer moving when the AI reaches its limit. For more interface inspiration outside AI, see our collections of SaaS UI design examples and mobile app UI examples.

Frequently asked questions

What makes good AI UX different from regular UX?: Good AI UX has to handle output that's probably right rather than definitely right. That adds work regular UX rarely needs: showing sources, flagging uncertainty, previewing changes before they apply, letting users reverse actions and designing distinct loading states for slow, streaming responses. The basics of clarity and consistency still apply, but trust becomes the main design problem.
Should every AI product use a chat interface?: No. Several of the strongest examples here have no chat at all, such as ghost-text suggestions, in-place edits and triage suggestions. Chat suits open-ended questions and exploration. For repeated, structured tasks, putting AI inline where the work happens is usually faster and easier to trust. Many products combine both for different jobs.
How should I use these examples in my own design work?: Use them to find the interaction pattern that fits your feature, then design it for your users and data. Note how each product triggers the AI, shows progress and lets users accept or reverse the result. Avoid copying visual details like icons or colors, since those don't carry the trust that makes the pattern work.
Why does this list not include usage numbers for these products?: The companies behind these products don't publish data on how their individual AI interface patterns perform, and we don't make numbers up. The value of the list is in the patterns themselves, which are publicly visible. If you adopt one, measure acceptance, edits and reversals in your own product to see whether it works for your users.
Which pattern should an early-stage AI startup start with?: Start with the pattern that matches your core task. If your product answers questions from documents, start with citations. If it writes or edits, start with previews or diffs. If it acts on the user's behalf, start with approvals and rollback. Getting one trust pattern right early matters more than adding many AI features at once.

Mehdi Hassan · CEO & Design Director

Copywriter, designer and developer, and founder of Designify Studio. Over five years, 60+ projects across 14+ industries, for startups that have raised $300M+.

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