UI/UX Design Tips for AI Apps: Fonts, Contrast, and Visual Hierarchy

I once watched a perfectly good AI feature die in a demo… because the font was tiny and grey.

Not “tastefully grey”. Not “subtle”. Just grey-on-grey, with a sprinkle of low-contrast placeholder text that vanished the moment the projector warmed up. The model was doing something genuinely clever—summarising messy notes into a tidy plan—but everyone in the room was squinting like they’d forgotten their glasses. And when people can’t read, they don’t trust.

AI apps have this extra hurdle: you’re already asking users to believe something invisible is working. If the UI feels even slightly slippery, they’ll assume the AI is slippery too. So yeah… we should probably talk about fonts, contrast, and visual hierarchy. The boring stuff that quietly decides whether your AI automation app feels solid or sketchy.

Fonts: pick boring on purpose

There’s a moment in most AI app projects where someone says, “Should we use a more futuristic font?” and I have to fight the urge to throw my laptop into the sea.

Futuristic fonts are great for movie posters. For an AI product UI, they usually just make basic tasks harder—reading, scanning, comparing. And when users are trying to judge an output (“Did it actually understand me?”), the last thing you want is friction at the eyeball level.

For UI/UX design tips, this one is painfully unsexy: use a clear, legible font that’s been battle-tested on screens. System fonts are not a failure. They’re a gift.

If you want a simple shortlist that won’t embarrass you later:

Inter (clean, modern, reads well at small sizes)
SF Pro / Segoe UI / Roboto (system-ish, dependable)
IBM Plex Sans (a bit of character, still very readable)

And please—whatever you pick—commit to it. AI apps already have enough going on. Mixing three typefaces because “this one feels friendly” is how you end up with a UI that looks like it was assembled during a power cut.

Typography for AI output: where things get messy

AI output isn’t like normal UI copy. It’s long. It’s variable. It sometimes rambles. It sometimes arrives as a neat list and sometimes as a wall of text that makes you question your own life choices.

So your typography has to handle worst-case scenarios gracefully. That means:

Use a comfortable base size. 16px is a solid default. 14px can work, but only if your contrast and line-height are spot on.
Increase line-height for generated text. Somewhere around 1.5–1.7 is usually kinder on the eyes.
Keep line length under control. If your AI output stretches across a huge monitor, users lose their place. Constrain the text column or add generous padding.

I’ve shipped AI interfaces where the model was fine, but the output felt worse because it was cramped. The user reads it and thinks, “This is chaotic.” They’re not judging the typography consciously… but they’re judging it.

Font weight is a UX decision, not a vibe

Light font weights look elegant in a design file. Then you put them on a real screen, in real lighting, with real people who are tired, and suddenly your UI becomes a whisper.

If your app is for AI automation—dashboards, logs, workflows—go a touch heavier than you think. Regular (400) for body text, maybe 500–600 for headings. Save the thin weights for marketing pages where nobody is actually trying to do anything.

And don’t forget numerals. If your AI app shows token counts, confidence scores, time saved, run history—numbers matter. Consider fonts with tabular numerals so columns line up. It’s a small thing that makes the whole UI feel more “engineered” and less “random”.

Contrast: stop being polite to your background colour

Low contrast is the silent killer of AI app usability. It sneaks in because designers (and I include myself here) like “soft” interfaces. Gentle greys. Muted borders. Placeholder text that’s basically a ghost.

But AI apps often involve reading. A lot of reading. Prompts, outputs, diffs, explanations, error states, human-in-the-loop edits. If your contrast is weak, you’re taxing users every second they’re in the product.

The easiest UI/UX design tip here is also the most ignored: make your text darker than you think it needs to be. Especially the stuff users rely on—labels, values, buttons, and anything that explains what the AI just did.

Contrast isn’t just accessibility—it’s trust

People talk about contrast like it’s a compliance checkbox. For AI apps, it’s trust-building. Clear text says: “We’re not hiding anything.” It says: “This output is stable enough to read.”

When the AI makes a mistake—and it will—users need to spot it quickly. If the UI is low-contrast and fuzzy, mistakes slide by. Then the user blames the AI… and they’re right to.

Practical moves that don’t require a design system overhaul:

Use near-black for primary text (not pure black, but close). On light backgrounds, something like #111 or #1A1A1A is often better than “cool grey”.
Don’t rely on colour alone for status. Pair colour with icons, labels, or patterns. “Green means good” is fine until someone can’t see green.
Make disabled states readable. Disabled doesn’t mean invisible. It means “not available right now”. Users still need to understand what it is.

If you’re working in dark mode, the same rules apply—just flipped. Dark mode is where contrast mistakes go to multiply. Light grey text on a slightly different dark grey background might look “sleek”… until you try reading it for ten minutes and your brain melts.

Contrast in AI-specific UI: citations, confidence, and “why”

A lot of AI apps now show extra layers: citations, confidence indicators, tool calls, reasoning summaries, “what changed” views. These are often styled as secondary text—smaller, lighter, more subtle.

Which is fair… but don’t make them too subtle. If citations are unreadable, users assume you’re faking them. If confidence is hard to see, it’s pointless. If “why this happened” is buried, you’ve basically built a magic trick, not a product.

I like to treat AI transparency features as “secondary, but not optional”. Give them contrast that’s clearly readable—just not competing with the main output. Think: a smaller size, maybe a slightly lighter colour, but still crisp. Still there.

Visual hierarchy: group things like you mean it

Visual hierarchy sounds fancy, but it’s mostly just answering one question: where should the eye go first?

AI apps are especially prone to hierarchy problems because they have at least three competing “centres”: the user input, the AI output, and the controls/settings. If you don’t deliberately organise them, the interface becomes a negotiation. Users bounce around, unsure what’s important.

Grouping related elements is the fastest way to fix this. Humans love clusters. If things belong together, put them together. If they don’t, give them space so the brain can separate them without effort.

The prompt box is not just a text field

In a lot of AI app UI, the prompt area is treated like a boring form input. But it’s the steering wheel. It’s where the user decides what they’re asking, what constraints matter, what tone they want. It deserves a proper “zone”.

Give the prompt area enough size to feel intentional. Add clear labels. If you have options like temperature, model choice, tools, or automation triggers—group them near the prompt, not scattered across the page like forgotten condiments.

And if you support templates, variables, or multi-step instructions, show structure. Bullets, sections, subtle dividers. You’re not just styling text—you’re helping someone think.

Output needs framing, not just a box

AI output without context is like receiving a parcel with no return address. Users want to know: what did the AI do, and can I trust it?

So frame the output area. A few small hierarchy choices go a long way:

Clear heading (“Draft”, “Summary”, “Suggested actions”) so users know what they’re looking at.
Timestamp or run state (“Generated 12:41”, “Using GPT-4.1”, “Tool: CRM lookup”)—not as a brag, just as grounding.
Action buttons near the result (copy, insert, approve, retry). Don’t make users hunt for the next step.

One thing I’ve learned the hard way: if “Retry” is more visually prominent than “Use this”, people will retry compulsively. They’ll treat the AI like a slot machine. Hierarchy nudges behaviour—whether you meant it to or not.

Whitespace is a feature

I used to cram. I thought dense UIs looked “powerful”. Sometimes they do… but often they just look anxious.

Whitespace is what turns a complicated AI workflow into something that feels manageable. It creates separation between prompt, output, and controls. It signals what’s grouped. It gives the eye a place to rest so the brain can actually evaluate what the AI produced.

If you’re unsure, increase spacing by 10–20% in the areas where users read and decide. Not everywhere—just where it matters. You’ll be surprised how quickly the UI starts to feel calmer, and how much less you need to “decorate” to make it feel designed.

A quick reality check before you ship

When I’m building AI automation tools or app interfaces, I do a tiny test that catches most of the embarrassing stuff.

I turn my screen brightness down. I step back from the monitor. I imagine I’m slightly tired (easy). Then I try to complete the core loop: write a prompt, run it, read the output, take action.

If I hesitate—if I squint, if I misread a label, if I can’t immediately tell what’s clickable—that’s the UI telling me the truth.

Fonts, contrast, and visual hierarchy aren’t “polish”. They’re the difference between an AI app that feels dependable and one that feels like it might change its mind mid-sentence. And when you’re asking users to trust an AI system… dependable is kind of the whole point.

Most days, I still have to remind myself to choose the boring font, make the text darker, and group things properly. Not because I don’t know better… but because it’s weirdly easy to forget that the user’s eyes are doing half the work.

And eyes, unlike models, don’t get smarter with more tokens.