Building AI Features in SwiftUI the Right Way

Over the past year, a lot of developers have experimented with adding AI to their apps.

A chat interface here.
A “Generate” button there.
Maybe a summarisation feature bolted onto an existing screen.

Technically, this works. The model responds. The feature demos well. But the deeper you go, the more you realise that building AI features well is less about calling an API and more about shaping the system around it.

When I started building AI-powered functionality into apps like AteIQ, I quickly discovered something: the model call itself is often the smallest part of the problem.

The real work happens before and after that call.

It’s about context, state, architecture, and how the experience fits naturally into your UI.

This post explores some of the patterns I’ve found helpful when building AI features inside SwiftUI apps.

Start With the Product Problem

Before writing any code, the most important question isn’t which model you’re using.

It’s what problem you’re actually solving.

A surprising number of AI features exist simply because the technology allows them to. But the most useful ones tend to emerge from clear product needs.

In AteIQ, for example, the goal wasn’t “add AI.” The goal was to make food logging easier and more natural.

Instead of forcing users to manually enter nutritional information, the system interprets plain language meal descriptions.

That’s a very different framing.

The model isn’t the feature.

The experience is.

Treat the Model Like a Service

In a SwiftUI app, it’s tempting to call the model directly from a view or a view model.

It works at first, but quickly becomes messy.

A better approach is to treat the model like any other external service. Create a dedicated layer responsible for interacting with it.

Something conceptually like this:

struct AIService {
func analyzeMeal(description: String) async throws -> MealAnalysis {
// model request
}
}

Your views and view models shouldn’t care about prompts or API details. They should only deal with the result.

This separation keeps your UI clean and makes the AI layer easier to evolve over time.

Structure Context Explicitly

One of the biggest mistakes developers make when working with language models is treating the prompt as the entire input.

In reality, the prompt is just the final instruction.

Everything else is context.

Instead of building giant prompt strings, it’s far more reliable to structure the information you send to the model.

For example:

{
"user_profile": {
"goal": "increase protein intake",
"diet": "balanced"
},
"meal_input": "Chicken wrap and a latte"
}

This approach has several benefits.

First, it makes the system easier to reason about. You can clearly see what the model knows about the user and the current action.

Second, it makes debugging much easier. If the output is wrong, you can inspect the context instead of guessing which part of a long prompt caused the issue.

And third, it allows your context to evolve independently of the instruction you give the model.

Keep the UI Calm

One of the easiest ways to make an AI-powered feature feel awkward is to expose too much of the model.

A common pattern looks like this:

User taps a button.
A spinner appears.
A wall of generated text shows up.

This often feels more like a demo than a product feature.

Instead, the goal should be to integrate the result quietly into the interface.

In a SwiftUI app, this might mean:

• Updating an existing component with AI-generated data
• Showing a subtle suggestion rather than a full response
• Confirming an action instead of asking the user to interpret a large block of text

The AI should support the interface, not dominate it.

Handle Latency Thoughtfully

Even fast models introduce some delay.

SwiftUI makes it fairly straightforward to manage asynchronous operations, but the UX around those operations still matters.

A few useful patterns include:

• Optimistic updates where possible
• Clear loading states when necessary
• Graceful fallbacks if a request fails

For example, a user logging a meal shouldn’t feel blocked by the AI processing step. In many cases, it’s better to capture the input immediately and enrich it asynchronously.

Designing for latency is part of designing the AI experience.

Treat AI as Probabilistic

Traditional app logic is deterministic.

Given the same input, you get the same output.

Language models behave differently. They operate probabilistically. That means you need to design systems that can handle variation.

Some practical considerations:

• Validate outputs before trusting them
• Add guardrails around critical actions
• Design UI flows that allow easy correction

In AteIQ, for instance, the user always has the final say over interpreted meal data. The model suggests, but the user confirms.

This keeps the system flexible without sacrificing reliability.

Build for Evolution

The AI ecosystem moves quickly.

Models improve. APIs change. New capabilities appear.

If your architecture tightly couples the UI to a specific model or prompt, your app becomes fragile.

A better strategy is to treat the model layer as something that can evolve independently.

That might mean:

• Abstracting model calls behind a service layer
• Keeping prompts configurable
• Separating context generation from the model request itself

This allows you to upgrade models or experiment with new capabilities without rewriting your interface.

Where This Is Heading

We’re still early in the era of AI-native apps.

Most products are experimenting. Many are still figuring out how intelligence fits into their architecture and UX.

But one thing is becoming clear.

Building great AI features isn’t just about choosing the right model.

It’s about designing systems that understand context, respect the user, and integrate intelligence into the product in a way that feels natural.

SwiftUI is a fantastic environment for this kind of work because it encourages clear data flow and composable interfaces.

When those principles meet well-designed AI architecture, the result can feel surprisingly powerful.

This post explored how to structure AI features in SwiftUI apps so they feel natural, reliable, and genuinely useful — not bolted on.

In upcoming posts, I’ll share:

• How context blocks shape real AI systems
• What calm AI-native UX looks like in practice
• How I’m evolving AteIQ with context-first design

If you’re exploring this space too, I’d genuinely love to connect on X or you can drop me a line at hi@stphndxn.com.

And if you're just getting started, I hope this blog becomes a place you revisit and grow alongside.

Until next time — structure that context.