Design Systems: Building and Maintaining Consistent UI/UX

In the world of product design, consistency is the cornerstone of delivering a seamless user experience. As digital products evolve and scale, the challenge of maintaining design consistency across multiple platforms, teams, and touchpoints becomes more complex. This is where design systems come in. A design system is a comprehensive set of guidelines, components, and tools that help designers and developers create a consistent and cohesive user interface (UI) and user experience (UX). From a designer’s perspective, building and maintaining a design system requires a strategic approach to ensure scalability, flexibility, and alignment with business goals.

Source: https://www.toptal.com/designers/ux/design-system)

Why Design Systems Matter?

Design systems are more than just style guides or pattern libraries. They offer a unified language for design and development, bridging the gap between creativity and functionality. Here are a few reasons why they are critical:

Consistency Across Products

One of the primary goals of a design system is to ensure uniformity across different platforms and devices. When users interact with a product, whether on a website or a mobile app, they expect a consistent experience. A well-implemented design system ensures that visual elements, interactions, and behaviors are aligned, offering a familiar and intuitive experience for users.

2. Faster Iteration

With a design system in place, teams can reuse components and patterns rather than reinventing the wheel for each new feature. This speeds up the design and development process, allowing teams to focus on solving new problems instead of reworking existing designs. For designers, it means less time spent on repetitive tasks and more time dedicated to innovation.

3. Scalability

As products grow and new features are added, maintaining a coherent UI/UX without a design system can become chaotic. A design system provides a scalable framework, enabling teams to add new elements or modules without compromising the overall consistency of the product.

4. Improved Collaboration

A design system fosters collaboration between designers, developers, and other stakeholders by providing a shared language and reference point. This ensures that design decisions are communicated effectively, reducing friction and misunderstandings between teams.

Building a Design System: A Designer’s Perspective

Designing a system from scratch can seem like a daunting task, but with a clear roadmap and understanding of core principles, it becomes a powerful tool for creating scalable and user-centered products.

1. Define the Core Principles

Before diving into visual components, it’s essential to establish the core principles of your design system. These principles serve as the foundation and guide decision-making throughout the process. Ask yourself:

• What are the key values of your brand?

• How should the design reflect the needs and emotions of users?

• What goals are you trying to achieve with your design system (e.g., accessibility, flexibility, innovation)?

For instance, if accessibility is a top priority, you’ll need to ensure that your design system accommodates users with different abilities, by providing color contrast guidelines, clear typographic hierarchies, and accessible components.

2. Create a Unified Visual Language

Once the principles are established, the next step is creating a cohesive visual language. This includes defining elements such as:

• Color Palette: A well-structured color palette ensures harmony across different UI components. It’s crucial to include primary, secondary, and accent colors, along with usage guidelines to avoid inconsistency.

• Typography: Define a typography system that establishes hierarchies and clarity, including font styles, sizes, line heights, and spacing. Consistent typography helps users navigate content effortlessly.

• Spacing and Grids: An established grid system ensures a balanced layout and harmony between different UI elements. Proper spacing guidelines prevent crowded or misaligned components.

• Iconography and Illustrations: Design guidelines for icons and illustrations help maintain visual consistency. Icons should follow a standard style (outlined, filled) and size, ensuring they align with the overall visual language.

Iconography and Illustrations: Design guidelines for icons and illustrations help maintain visual consistency. Icons should follow a standard style (outlined, filled) and size, ensuring they align with the overall visual language.

Image Source: https://designerup.co/blog/10-best-design-systems-and-how-to-learn-and-steal-from-them/)

3. Design Reusable Components

A design system is incomplete without reusable UI components. These components include buttons, forms, modals, and navigation elements that can be used across various features and pages. Each component should be:

• Modular: Components should be flexible and adaptable for different contexts. For example, a button should have variations for different states (hover, active, disabled) and types (primary, secondary, destructive).

• Documented: Each component needs comprehensive documentation that explains how it should be used, its variants, and the rules governing its application. This ensures consistency when different team members use the same component.

4. Implement Accessibility Guidelines

Accessibility should be at the forefront of every design system. As a designer, consider:

• Color Contrast: Ensure sufficient contrast between text and background for readability.

• Keyboard Navigation: Ensure that interactive components are navigable via keyboard for users who rely on keyboard shortcuts.

• Responsive Design: Components should be responsive, adapting seamlessly across different screen sizes and devices.

• Assistive Technologies: Design components should be compatible with screen readers and other assistive technologies to provide an inclusive experience.

Maintaining a Design System: Evolving with the Product

Once a design system is in place, the work doesn’t stop there. Maintaining it requires ongoing effort as the product evolves and new design challenges emerge. Here’s how designers can ensure the design system remains relevant and effective:

1. Regular Audits and Updates

A design system should be treated as a living document that evolves with the product. Regular audits are necessary to identify outdated or unused components. Designers should collaborate with developers to ensure new features are aligned with the existing design system, and updates should be documented to avoid fragmentation.

2. Version Control and Documentation

Version control is essential for keeping track of changes in the design system. Documenting why and how changes are made ensures that the design system grows in an organized manner. It also helps onboard new team members quickly, as they can reference updated guidelines.

3. Foster Collaboration Across Teams

Designers need to maintain an open line of communication with developers and other teams using the design system. Tools like Figma, Sketch, and Zeplin allow designers to share components and guidelines in real-time, fostering collaboration and ensuring that everyone is aligned.

Some well-known examples of design systems that have set benchmarks in the industry:

1. Google Material Design

URL: Material Design
Overview: Google’s Material Design is one of the most comprehensive design systems available. It provides guidelines on how to create visual, motion, and interaction design across platforms and devices. Material Design is known for its grid-based layouts, responsive animations, and depth effects like shadows and lighting.
Features:
- Comprehensive color palettes, typography, and iconography.
- Extensive component library (buttons, cards, etc.).
- Customizable UI components for both web and mobile applications.

2. Salesforce Lightning Design System

URL: Salesforce Lightning
Overview: Salesforce’s Lightning Design System provides a comprehensive library of UI components and resources to help build applications on the Salesforce platform. It ensures a consistent experience across Salesforce products and enables developers to create custom apps easily.
Features:
- Ready-to-use components, design tokens, and code snippets.
- Comprehensive accessibility guidelines to ensure inclusivity.
- Detailed documentation for developers to integrate components into Salesforce applications.

3. Microsoft Fluent Design System

URL: Fluent Design
Overview: Fluent Design is Microsoft’s design system, created to provide an engaging and scalable UI across all Microsoft products. It is heavily focused on interaction, animation, and motion while ensuring accessibility across various device ecosystems.
Features:
- Focus on depth, motion, and light to create modern and interactive UIs.
- Cross-platform components supporting web, desktop, and mobile applications.
- Accessible components with detailed guidelines for developers.

Conclusion

Building and maintaining a design system is an investment that pays off in the long run. It ensures consistency across your product, improves collaboration between teams, and allows for faster iteration and scalability. For designers, a design system is more than a collection of reusable components — it’s a framework that enables you to create user-centric products with confidence and clarity. By embracing modularity, documentation, and accessibility, you’ll ensure that your design system remains a valuable asset as your product grows.

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

Design Systems: Building and Maintaining Consistent UI/UX

Why Design Systems Matter?

Building a Design System: A Designer’s Perspective