3 UI Patterns for AI-Infused Apps

Almost every new app is infused with AI in one way or another. But the way we design for it is still very new and changes rapidly.

For a while, a lot of AI features looked the same: add a chat box, let the user type anything, send it to the model, then show the answer. And to be fair, that works for a demo. It can even feel magical the first time you use it. But when you start building real products, the cracks show quickly. As models evolve and each one comes with different capabilities, context windows, and system prompts, it becomes harder for users to know what to say and how to say it so the model does its best work. That is not the user's problem to solve. It is ours. As developers and product builders, it is our job to close that knowledge gap so the experience feels effortless regardless of what is running under the hood.

That is why I think AI UI patterns are worth understanding now. Not because the patterns we use today will stay the same forever. They probably will not. But some interaction problems keep showing up regardless of which model you are using. Just look at the shift from fast non-reasoning models to reasoning models that pause, plan, and work through steps. That small wait between asking and answering creates a new product question: what should the user see while the AI is working?

There are still a lot of unanswered questions here. But there are three practical patterns I think already make AI-infused apps feel much better.

Before we get into them, here is how I think about an AI-infused app. It is not just an app with a chatbot bolted on. It is an app where the model becomes part of the workflow. If you removed the AI, the product would fundamentally change. And that means the UI around the AI matters a lot.

1. Guided Input

The first pattern is guided input. This means the interface helps the user give the AI better context before the model starts working.

Sometimes a blank prompt box works fine. Some models handle open-ended input well, and for power users it can feel flexible and fast. But in most cases, if you want users to get the best experience, you can help guide them toward what to include for details, what format to ask for, what constraints matter, and how specific to be. An average user or someone just starting out with AI would not think about any of that, and they should not have to. If a product is supposed to be seamless, it should work for everyone, not just people who already know how to prompt.

Guided input gives them a path. This could be intent chips, dropdowns, short questions, templates, examples, or a small form. The goal is not to remove free text. The goal is to help the user say the right things without needing to figure out what "right" means.

For example, instead of asking "How can I help?", an AI support tool could ask:

• "What are you trying to do?"
• "Is this a reply, summary, escalation, or form fill?"
• "What tone should the response have?"
• "Is there anything the AI should avoid?"

That gives the model richer input, and it gives the user a clearer path.

This is the important part: guided input is not about making the product less flexible. It is about helping the user express intent clearly enough for the AI to do useful work. The benefits go both ways:

For the user:

• Better results on the first try, less frustration and back-and-forth
• No need to learn how to prompt. The product does the thinking about what to ask
• The experience feels predictable and accessible, regardless of technical skill

For you as a developer:

• Structured input means fewer wasted tokens on vague or misunderstood requests
• You can scope the model's task more tightly, which improves quality and reduces cost
• Easier to measure, debug, and iterate on a guided flow than an open-ended one

2. Visible Process Feedback

The second pattern is visible process feedback. This becomes critical as AI apps move beyond simple text generation.

Most AI-powered products today are not just chatbots. They are increasingly agentic. They reason through problems, search for information, call external tools, fill out forms, and orchestrate multiple steps before producing a result. When the model is doing that much work behind the scenes, leaving the user staring at a spinner is a bad experience. A spinner tells the user the app is busy, but it does not tell them whether progress is being made. A better UI shows small, useful process signals. For example:

• Reading the customer message.
• Extracting key details.
• Checking missing information.
• Drafting a reply.
• Ready for review.

This does not need to be a huge panel. In most apps, I think it should be small by default and expandable when needed. The collapsed version might just show: "AI is working, 2 of 4 steps complete." Then, if the user wants more detail, they can expand it and see the actual steps.

The point is not to expose the model's private reasoning. The point is to show product-level progress so the user stays oriented. This turns waiting time into understandable progress. It also builds trust. The user can see that the AI is not just magically producing an answer. It is working through a task.

3. Editable Output

The third pattern is editable output. This one matters because AI is useful, but it is not always right.

A weak AI interface gives the user one big answer. If the answer is wrong, the user has to re-prompt, regenerate, or start again. A better interface turns the AI output into editable product state. That could mean editable fields, accept/reject buttons, section-level regeneration, before and after comparisons, or "apply selected changes."

For example, imagine the AI drafts a customer email for you. Instead of showing you a final block of text with an "Accept" button, it could show the draft as an editable document with inline diffs, highlighting what it changed, letting you tweak the tone, adjust a timeline from "3-5 business days" to "2-3 business days," or swap out the closing line. You keep the parts that work and fix what does not.

This is important because the final action still belongs to the user. The AI can draft. It can suggest. It can prepare. But the user should be able to shape the final result before anything is sent, saved, or published. That is what makes the experience feel safer.

And honestly, this is where a lot of AI products either feel useful or annoying. If the AI gets you 80% there, the UI should make it easy to fix the final 20%. It should not make you start from zero again.

Final Thought

When I first started hearing that designers and frontend developers would be replaced by AI, it honestly felt like it could be true. But the more work I do in this space, the more I realise the opposite is happening. I might write a little less code than before, but I spend far more time thinking about how humans interact with AI. Problems I would never have considered if these models did not exist.

AI has solved a lot of issues, but it has also created a whole set of new and genuinely fun problems for developers to solve and designers to explore. It pushes you to be more creative, to think about UI patterns like the ones above, and to design experiences where the AI is not a black box but a collaborator the user actually trusts. Guided input helps them give better context. Visible process feedback helps them understand what is happening. Editable output helps them stay in control.

Together, these patterns point toward a more accessible, AI-native web where products feel less like magic boxes and more like tools that add real value.