Intro: When Words Become the Unsung Heroes of Design
We don’t talk enough about how tiny words can make or break a user experience. Buttons, tooltips, onboarding hints — the microscopic stuff we call microcopy.
I’ve been designing for over a decade, and I can tell you this: a well-placed “Got it” or a perfectly tuned “Oops! Something went wrong” can create more user trust than any pixel-perfect gradient ever will. But here’s the catch — writing this stuff consistently and effectively across an entire product isn’t easy.
That’s where AI comes in.
I’m not talking about letting ChatGPT write your entire interface (please don’t). I’m talking about using AI as a creative partner to brainstorm phrasing, ensure consistency in tone, and even help you craft more human-sounding error messages.
In this post, I’ll walk through how I use AI for UX writing — specifically in microcopy, error messaging, and voice consistency — and why you should consider doing the same.
Microcopy: The Small Words That Do Heavy Lifting
Microcopy is the friendly whisper guiding users through a digital product — those little snippets of text that explain, reassure, and occasionally make users smile. But as any designer knows, writing microcopy can be an exhausting game of trial and error.
How AI Fits In
When I’m working on new interfaces, I often use AI tools like ChatGPT, or Notion AI as brainstorming partners. Here’s how:
- Idea Generation: I’ll feed the AI a scenario: “User is entering sensitive payment info. The tone should be trustworthy but light.” Then I asked it for ten variations. Half of them are too robotic, three are borderline cheesy, but two might spark an idea that works. I don’t expect perfection — I expect inspiration.
- Tone Exploration: AI can adapt its tone like a chameleon. Want your copy to sound more confident, casual, or empathetic? Prompt it with tone adjustments. This helps me quickly test different voices before finalizing one that feels on-brand.
- Localization Prep: Consistent phrasing helps when you’re planning for translation. AI can help identify where cultural nuances might break your message.
Example:
Let’s take a simple example — the “Submit” button on a form. It’s boring. But if your brand voice is playful, AI might suggest alternatives like:
- “Let’s go!”
- “Send it off!”
- “Done and dusted.”
From there, I can refine to match brand tone, accessibility guidelines, and readability. The magic isn’t in the AI’s first draft — it’s in how I iterate on it.
Tip for Designers:
When using AI for microcopy, treat it like a writing assistant, not an author. Use it to uncover tone directions, not to automate empathy.
Error Messages: Where Empathy Meets Utility
If microcopy is the whisper, error messages are the apologies. And no one likes an apology that feels automated. Bad error messages can cause friction, frustration, or distrust. (“Invalid input” is basically UX for “figure it out yourself.”) Great ones, on the other hand, show that your product cares.
AI’s Role in Crafting Better Errors
When I design user flows, I prompt AI to help rewrite technical error responses in plain English. For example, instead of: “Error 403: Authentication failed.” I might ask AI to rephrase it empathetically:
“We couldn’t verify your login — please check your password and try again.”Then I test variations for tone:
- Polite: “Let’s try that again — your login didn’t go through.”
- Supportive: “Hmm, looks like your login didn’t work this time. Mind checking your password?”
- Direct: “Login failed. Check your password or reset it.”
AI can produce several tone variations instantly, giving me language options that I can A/B test with users later. It saves me hours that I can spend refining interaction design instead.
Building Emotional Intelligence Into Errors
AI models trained on human text patterns can even help identify how empathetic or cold a message sounds. By using tools like Grammarly or Writer.com, I can run tone analysis on existing copy and adjust accordingly. I often run user-testing scenarios where participants read potential error messages out loud — you can feel when a line lands wrong. AI helps me test phrasing more quickly before it ever reaches a user’s screen.
Example:
Imagine a lost internet connection during checkout.
Instead of:
“Network error. Please try again.”
AI might generate:
“We lost connection for a moment — your items are still safe in the cart. Try again when you’re back online.”
That’s a micro-moment that reassures and prevents panic — exactly what UX writing should do.
Voice Consistency: Keeping Your Brand’s Personality Intact
A brand’s voice is like its handshake — it tells users who you are in every interaction. Consistency across pages, states, and errors builds trust. But when multiple teams (designers, devs, marketers) touch the product, voice drift happens.
AI as the Keeper of Voice
This is where I’ve found AI tools to be incredibly helpful — as tone guardians. Using platforms like Writer.com, Grammarly Business, or ChatGPT with custom instructions, you can define tone rules that AI helps enforce. For instance, I might train an AI prompt to follow guidelines like:
- “Write in a confident but conversational tone.”
- “Avoid jargon and double negatives.”
- “Use active voice and inclusive language.”
AI then becomes a “style linter” for UX text, catching inconsistencies or deviations before they are shipped.
Example Workflow
- Create a Tone Profile: Feed your AI model a set of approved examples — error messages, tooltips, confirmations, etc. This establishes a pattern.
- Run Consistency Checks: When someone on the team writes new copy, AI can flag sentences that feel off-brand.
- Version Control for Language: I’ve even used AI to compare two sets of interface text and report stylistic differences — like a Grammarly for UX.
AI helps democratize voice consistency. You don’t need a full-time content strategist on every feature sprint (though that would be lovely). With the right tone model, even engineers can contribute copy that sounds aligned.
Balancing Human Touch and Automation
Let’s be honest — the fear of AI replacing UX writers is floating in the air. I get it. But the truth is, AI is a tool, not a takeover. It’s only as good as the human shaping it.
I once ran an experiment: I asked an AI to write all the onboarding messages for a mobile app I was designing. The result? Technically correct but emotionally hollow. It lacked the warmth that makes users feel welcome.
So I used the AI draft as scaffolding, added my human insight, and suddenly it sounded right. The key wasn’t choosing between AI or human writing — it was combining both.
AI gives you the first 70%. Human intuition fills in the last 30% — the nuance, humor, and empathy that users connect with.
Practical Tools and Prompts I Actually Use
Here are a few AI tools and methods I regularly use in UX writing workflows:
- ChatGPT (custom tone prompts): For brainstorming microcopy and exploring tone variations.
- Grammarly Business / Writer.com: For tone and clarity enforcement across large teams.
- Notion AI: For quick microcopy ideation within design documentation.
- Figma Plugins (Writer or GPT-powered): For generating text directly inside mockups.
- Voice Style Guide Generator (via GPT): To create or update brand voice guides.
Sample prompts I’ve used:
- “Rewrite this microcopy for a friendly but confident brand voice.”
- “Generate 10 variations of this error message that sound empathetic.”
- “Analyze the tone of this text and suggest adjustments to match Apple’s UX tone.”
Simple, repeatable, and saves a surprising amount of time.
AI’s Limitations: Don’t Outsource Empathy
AI doesn’t know your users — you do. It can mimic empathy but not feel it. That distinction matters.
I’ve tested AI-written copy that sounded polished but didn’t match the context. For example, an AI once suggested humor in an error message about lost form data. Not cool. Users were understandably annoyed.
You still need to test and validate. AI doesn’t replace user feedback sessions; it just makes iteration faster.
Also, remember accessibility. AI might suggest words that sound clever but confuse screen readers or users with cognitive disabilities. Always run usability checks after using AI for writing.
Final Thoughts: Writing with a Digital Co-Author
Using AI for UX writing doesn’t mean you lose your voice — it means you refine it faster. I use AI as a writing partner that helps me think through tone, craft better copy, and maintain consistency. But the best UX writing still starts with empathy — something no machine can truly automate.
If you’re curious about bringing AI into your UX writing process, start experimenting. You’ll be surprised at how much creative friction it removes.
And if you’ve already been using AI in your writing workflows, I’d love to hear about it — what’s worked for you, what hasn’t, and where you see it going next.
Because at the end of the day, UX writing is about making products sound human — and maybe, just maybe, AI can help us be a little more human, too.