The Future Browser: How ChatGPT Atlas Is Redefining the Web – and What It Means for UX

In a world where every sprint is a race and every pixel matters, the idea of artificial intelligence stepping in to support UX work is no longer theoretical. Zeplin — long trusted by product teams for streamlining design-to-dev handoffs — is entering new territory with its latest feature: AI Design Review.

But is it a true productivity boost or just another buzzword?

At UX GIRL, we took a deep dive into how this feature works, who benefits from it, and whether it's ready for real-world product workflows.

What Is Zeplin’s AI Design Review?

Zeplin’s AI Design Review uses a combination of large language models (LLMs) and visual analysis algorithms to automatically evaluate design screens uploaded to the platform. With one click, the tool can assess:

• Adherence to UI/UX best practices (contrast, visual hierarchy, spacing),
• Alignment with your design system,
• Text readability,
• Accessibility issues,
• Element alignment and padding.

The result? Designers and teams receive instant feedback before development even starts — saving time, reducing rework, and improving consistency.

Note: The feature is currently in Beta and available for Team and Organization plans

How It Works in a Real Sprint

Let’s say a designer finalizes a set of screens. Normally, they’d hand it over to the team for manual review, often leading to rounds of feedback, corrections, and delays.

With AI Review, here’s how it looks:

1. The designer uploads the file to Zeplin.
2. They trigger AI Design Review, which instantly scans layout, color, type, spacing, and accessibility.
3. The AI suggests corrections like:
   • “Low contrast between button and background.”
   • “Heading typography breaks consistency with subheaders.”
4. The designer adjusts accordingly.
5. PMs and developers receive a cleaner, more polished file with fewer errors.

Zeplin claims the feature can reduce design-related issues passed to development by up to 30%

Why Product Teams Should Care

For Product Owners, Project Managers, and CTOs, AI Design Review can:

• Accelerate iteration cycles by catching issues early,
• Improve design consistency across large or fast-changing UI systems,
• Help non-designers (like PMs) understand design quality without relying solely on design reviews.

Future updates will allow teams to customize review rules based on internal design systems, making the tool even more relevant for enterprise environments.

Limitations to Watch Out For

Despite its promise, Zeplin’s AI Review isn’t a silver bullet — and it shouldn’t be treated as one. Key caveats include:

• Lack of design intent: The AI can't understand why a designer made a specific decision.
• No user context: It doesn’t analyze user goals, flows, or emotions behind the interface.
• No support for non-English reviews (as of now).
• Risk of over-automation: Teams may over-rely on AI and deliver “technically correct but uninspired” UI.

This makes it a great supporting tool, but not a replacement for thoughtful human review.

How to Integrate It Into Your Workflow

To make the most of AI Design Review, we recommend the following integration model:

1. Designer finishes a screen and uploads it to Zeplin.
2. AI Review is triggered, and suggestions are considered.
3. Project Manager reviews AI feedback before sprint planning or handoff.
4. Developers get cleaner, AI-reviewed designs, reducing back-and-forth and rework.

This model works best when combined with traditional team review sessions and design QA.

Is It Worth It? Our Verdict

If your team:

• Moves fast (Agile, CI/CD),
• Uses a design system,
• Delivers at scale or across multiple platforms,

...then AI Design Review can help reduce errors, align expectations, and deliver better experiences faster.

Smaller teams may find it a "nice to have," but even then — testing it in one sprint can offer real insights.

Conclusion: So, Should You Use It?

Zeplin’s AI Design Review isn’t about replacing designers — it’s about giving them better tools to work smarter. It acts as a second pair of eyes, offering clear, structured feedback before handoff.

At UX GIRL, we help teams like yours build processes that combine AI-powered tools with UX strategy, research, and design expertise. If you’re curious about bringing AI into your workflow, let’s talk — we’ll show you how to make it work without losing the human touch.

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In the high-speed world of Agile, where user expectations evolve faster than sprint cycles, clear communication is not just a nice-to-have — it’s critical. Yet even in well-functioning teams, UX designers and developers often struggle to stay perfectly aligned. The result? Beautifully crafted prototypes that don’t quite behave as intended once implemented, or ambiguous flows that leave testers guessing.

Enter Gherkin — a simple, structured language that can help bridge the gap between design, development, and product.

What is Gherkin (and why should UX designers care)?

Gherkin is a structured, plain-language format used to write behavior-driven development (BDD) scenarios, typically used by QA and dev teams to write automated tests. But its value goes far beyond testing.

Its real strength lies in its simplicity — it describes user behavior in a "Given–When–Then" format, making it the perfect candidate for aligning cross-functional teams around how a feature should behave.

Example:

This isn’t code — it’s user intent, written in plain English. And that makes it a powerful communication tool for designers.

How UX Designers Can Use Gherkin to Document Intent

Designers don’t need to become developers to leverage Gherkin. Instead, they can use it to clearly define interaction logic — supplementing wireframes, prototypes, and user flows with behavior-driven context.

By embedding Gherkin-style scenarios into design documentation or user stories, designers ensure that the team understands not just what the interface looks like, but how it should behave.

Here’s a UX-specific example:

This format reduces ambiguity and ensures that design intentions translate into correct implementations.

What’s in it for your team (and your bottom line)?

Introducing Gherkin into the UX process may feel like an extra step, but it pays off. Studies show that reducing ambiguity in handoffs and requirements can lead to major efficiency gains.

• Fewer misunderstandings: A McKinsey report found that companies improving requirement clarity saw up to a 40% increase in team productivity (Source: McKinsey & Company).
  
  
• Faster onboarding: Gherkin scenarios give new team members immediate context for how the product should behave.
  
  
• Better alignment with business goals: Stakeholders can validate behavioral flows early — even before development starts.
  
  
• Improved testability: QA teams can use Gherkin to write automated or manual tests directly aligned with design.
  
  

Gherkin becomes a shared language between design, product, dev, and QA — cutting down feedback loops and minimizing rework.

How to Start Using Gherkin in Your Design Process

You don’t need to overhaul your workflow overnight. Start small:

1. Pick one key user flow — such as login, checkout, or onboarding.
   
   
2. Write 1–3 scenarios in Given–When–Then format.
   
   
3. Share them during refinement or planning with developers and testers.
   
   
4. Attach the scenarios to your design files or link them in your backlog.
   
   

This lightweight addition can dramatically improve alignment — even in teams that already communicate well.

Design Is Behavior — Not Just Visuals

UX is about more than how things look — it's about how they work. While wireframes and prototypes show structure and visuals, they often leave room for interpretation when it comes to logic, rules, and edge cases.

Gherkin helps designers express interaction logic in a way that’s unambiguous and testable. And in an Agile team, that means fewer assumptions, faster delivery, and better user outcomes.

At UX GIRL, we encourage product teams to experiment with Gherkin as a way to reduce misalignment and build stronger bridges between design and development. You don’t need to be technical — you just need to care about clarity.

What’s Next?

Ready to give your design handoffs a boost? Start with a single Gherkin scenario for your next feature. Use it to open a conversation between design, dev, and QA. You might be surprised how quickly your team aligns when you’re finally speaking the same language.

Need help integrating UX practices like Gherkin into your Agile process? Reach out to UX GIRL — we help teams turn design decisions into product clarity.

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Conversational AI has changed the rules of user experience. With OpenAI Apps SDK, teams can now create embedded applications that live directly inside ChatGPT — offering users seamless, intelligent, and contextual support.

But building these apps isn't just about writing smart code. It's about designing meaningful, intuitive interactions. That’s why OpenAI published official design guidelines — and why UX GIRL is here to help you translate them into real results.

What Are ChatGPT Apps and the Apps SDK?

ChatGPT apps are mini-tools that users can access directly in the ChatGPT interface. They allow users to perform tasks, analyze data, create documents, fetch information from external sources, and more — all within the flow of conversation.

The Apps SDK lets developers define these app interactions using JavaScript while maintaining full compatibility with the ChatGPT interface. But to deliver real value, apps need to feel intuitive — and that’s where UX comes in.

The Core Design Principles from OpenAI

OpenAI’s UX guidelines are built on six core principles. Here’s what they mean in practice, with insights from the UX GIRL team:

Clarity is key

Your app’s interface must clearly communicate what it does, how it works, and what users can expect. Avoid vague labels or overloaded screens. Guide users with simple language and clean layout.

Respect the user’s intent

Let users take the lead. Your app should support user goals, not hijack the conversation. Avoid aggressive prompts or forced flows.

Make progress visible

Users need feedback. Loading indicators, success confirmations, and microinteractions help users trust the process — especially in a conversational UI.

Minimize user effort

Reduce friction wherever possible. Use smart defaults, context-aware suggestions, and auto-filled values to streamline user input.

Be consistent

ChatGPT has a defined look and tone — follow it. Use system UI components and maintain consistency in voice, spacing, and layout.

Fail gracefully

Errors are inevitable. Design them to be informative and friendly. Offer users clear explanations and next steps without making them feel lost.

How Product Teams Can Apply These Guidelines

Following these principles doesn’t require a full UX overhaul — but it does require strategic thinking. Here are two practical ways your team can implement them:

1. UX-aligned development workflow:

• Define realistic user conversations and app responses early.
• Prototype conversations using mock UIs or prompt flows.
• Test early and often — even with basic, Wizard-of-Oz style setups.
• Build in real-time feedback elements (confirmation messages, visual states).

2. UX checklist for Product Owners:

• Does the user always know what they can do next?
• Are all actions and outcomes clearly explained?
• Is app progress or system state visible?
• Is tone and layout consistent with ChatGPT?
• Do error messages guide users constructively?

The Unique UX Challenges of Designing Inside a Chat Interface

Unlike traditional apps, ChatGPT apps don't rely on menus, tabs, or visual hierarchies. Users interact through text — with fluid, nonlinear intent. This makes context one of the biggest UX challenges.

Small design gaps (e.g., unclear responses or missing context) can lead to confusion. That’s why good conversational design includes scenario testing, intelligent defaults, and visible state changes — even without a traditional UI.

Final Takeaways

Designing inside ChatGPT isn’t just about building functionality — it’s about earning user trust through clarity, empathy, and consistency.

At UX GIRL, we recommend:

• Start with a small MVP to test a focused user goal.
• Use OpenAI’s design principles as a design audit tool.
• Involve UX early — especially for dialogue design and testing.
• Don’t rely on AI to do everything. Guide the user intentionally.

Building with Apps SDK? Let UX GIRL help you design AI-powered experiences that convert, engage, and delight.