AI For Enhancing User Experience

🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾 AI experience can go way beyond a text box. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experiences. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Risky ↔ Risk-averse, Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise. Instead of expecting large and highly detailed text prompts, we could slow people down when they prompt — e.g. with prompt constructors, prompt strength meters, presets or templates. Perhaps by defining an expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming). Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, level of expertise, sources or even a set of videos or PDFs. We need pre-screening of sources, and proactive alignment with users. These are features that would make output much more specific without having to write a long prompt. And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not via text prompts, but contextually — acting on the relevant parts of AI outcome. We can go way beyond a text prompt. Better results come from combining good old-fashioned design patterns such as search, filtering and sorting with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people. 💎 Design Patterns For AI Interfaces Prompt UX Patterns, by Sharang Sharma https://lnkd.in/eCytfAe9 Where should AI sit in your UI?, by Sharang Sharma https://lnkd.in/dyyMKuU9 AI UX Patterns, by Luke Bennis https://lnkd.in/dF9AZeKZ Design Patterns For Building Trust, by If https://lnkd.in/eEJngtVv AI Design Patterns Catalogue, by Maggie Appleton https://lnkd.in/ebAp9Sb8 --- 🚀 Fantastic AI Examples: Elicit (research tables): https://elicit.com Consensus (confidence levels): https://consensus.app/ Scispace (search + AI): https://scispace.com v7 Labs (AI auto-fill): https://v7labs.com/ Exa (semantic grid): https://exa.ai DeepL (translation): https://deepl.com NotebookLM (scoping): https://notebooklm.google/ [continues in comments] #ux #ai

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

How proactive AI will change UX - 📆 schedule ChatGPT requests! OpenAI has introduced a new task scheduling feature for ChatGPT. This means you can now ask ChatGPT to handle tasks at a future time — like sending you a weekly global news update, recommending a daily personalized workout, or setting reminders for important events. 💡 Why is this interesting from a UX perspective? This shift is a step toward proactive AI — moving from reactive systems (waiting for user input) to anticipatory, context-aware experiences that help users save mental energy and stay on top of their routines. Let’s break it down from a real-life use case - creating daily recipes: I currently eat sugar-free, gluten-free (because I am celiac), and generally low-carb and like to let ChatGPT create recipes for me. I don’t want a fixed meal plan, but I do need flexible, personalized recipe suggestions that fit my nutrition goals. Ideally, I’d want ChatGPT to  → suggest automatically 3-4 recipes daily around 3 PM → send them to me  → and based on my choice adjust future suggestions for the next days based on what I’ve already eaten that week (for balanced nutrients). With the new task feature, this kind of personalized experience could become much much more seamless. I wouldn't need to ask repeatedly — the assistant would learn my preferences over time and adapt its suggestions accordingly. 🎯 What can we learn from this in AI-UX design? 1️⃣ From static interactions to dynamic experiences: We often design AI tools that rely on users asking for something. But this update shows the value of continuous, evolving interactions. Users shouldn’t need to start from scratch every time — systems can proactively adjust to their needs and context. 2️⃣ Mental models of AI assistants: For users to trust AI routines, they need to understand what the assistant will do and when. It’s about designing predictability and transparency in a way that still allows for flexibility and spontaneity. 3️⃣ Proactive ≠ intrusive: There’s a fine balance between helpful and annoying. The best AI interactions feel like a supportive partner — offering assistance at the right time, based on context and past behavior, without overwhelming users with irrelevant notifications. In AI-UX, we’re increasingly designing for systems that adapt and evolve with the user.  This new feature is a great example of how AI can shift might be able rom a passive tool to an active assistant — can’t wait to try it. How do you see proactive AI changing the way we design user experiences? Would love to hear your thoughts! 👀

I went to an AI UX workshop last night expecting recycled LinkedIn advice about "building AI trust through transparency." Instead, Isabella Yamin tore down LinkedIn's job posting flow using her CarbonCopies AI framework in real-time, while founders shared raw implementation struggles. It completely changed how I'm rethinking Maibel's onboarding flow. Here's what I stole from B2B SaaS principles to redesign emotional AI for B2C: 1️⃣ Progressive disclosure with purpose LinkedIn's fatal flaw? Optimizing for completion ease > Outcome quality. Recruiters are drowning in irrelevant applications because AI never learns what "qualified" means. The personalization paradox: How do we give users enough control without overwhelming them? Users don't want "frictionless". They want INFORMED control. 📌 At Maibel: I was falling into the same trap, making emotional coaching setup so simple that the AI couldn't understand user context. Now? Progressive complexity with clear trade-offs. Show users how their choices impact outcomes. → Want deeper insights? Add more context. → Want faster setup? Here's what the AI can't personalize. 2️⃣ Closed-loop data intelligence: What Platfio gets right They've built a platform for software agencies where where every data point feeds back into the entire system. User preferences in marketing flows shape proposals. Campaign performance shapes future recommendations. Every interaction becomes intelligence for future recommendations. 📌 At Maibel: Most wellness apps store emotional check-ins like digital journals. I'm turning them into predictive feedback loops. Emotional intelligence isn’t static but COMPOUNDS. Today's reflections shift tomorrow's suggestions. Patterns fuel prevention. Users' inputs on Monday could predict AND prevent Friday's breakdown. 3️⃣  Multi-modal creativity: Wubble's transparency approach Translating images and files into music - who'd have thought? They've cracked multi-modal creativity where users become co-creators, not passive consumers. The breakthrough moment for me: What if users could see how their visual environment contributes to emotional context? 📌 At Maibel: Users upload images of their day and see how AI analyzes emotional cues: cluttered workspace = overwhelm, junk food = stress eating. Multi-modal understanding users can contribute to and influence. 💡 The bottom line? B2B Saas gets one thing right: Every interaction has to earn trust. In B2B, failed AI means churn. In emotional AI, failed trust breaks belief in tech entirely. 📌 Here's what we're doing differently at Maibel: → Progressive complexity → Context-aware feedback → Multi-modal participation → Intelligence that compounds with every input. It's not just about building WITH AI. I'm designing systems that learn understand YOU before you even need to explain yourself. Kudos to Isabella, Shivang Gupta The Generative Beings, Shaad Sufi Hayden Cassar and everyone who shared deep product insights.

🚀 I Stopped Designing Alone. I Started Designing With AI. And honestly? It changed my entire UX process. Over the past few months, I’ve been integrating AI Figma plugins directly into my real-world client projects,not as shortcuts, but as thinking partners. Here’s how I actually use them in real projects 👇 1. UX Pilot: My Rapid Prototyping Engine When I receive a PRD or rough client requirements, I don’t jump straight into polished UI. I prompt UX Pilot to: • Generate quick wireframes • Create possible user flows • Explore multiple layout structures This helps me validate direction in hours instead of days. I never ship AI output directly, I refine it with business logic and user behavior insights. 2. Clueify: My Pre-User-Test Check Before showing designs to stakeholders, I run an AI usability audit. It helps me analyze: • Visual hierarchy • CTA focus • Cognitive overload • Attention flow It’s like doing a “silent usability test” before real users ever see it. 3. Stark: Accessibility Is Not Optional Real-world products serve real people. I use Stark to: • Check contrast ratios • Simulate visual impairments • Ensure WCAG compliance Accessibility isn’t a feature. It’s responsibility. 4. Octopus.do: I Structure Before Screens In large projects (especially SaaS dashboards), structure matters more than UI. Before designing anything, I: • Map the entire sitemap • Validate navigation depth • Align user journeys Because messy structure = messy experience. 5. Magician: Fast Ideation Mode When brainstorming: • Placeholder content • Icon ideas • Micro-interactions • Empty states Magician speeds up exploration so I can focus on strategy. 6. MagiCopy: UX Writing That Converts Good UI means nothing without clear communication. I use it to: • Generate button variations • Test tone (friendly vs professional) • Improve clarity Then I humanize it with brand voice. 7. Uizard: From Sketch to Prototype Sometimes clients send hand-drawn ideas. Instead of rebuilding from scratch: I convert sketches → editable wireframes → interactive prototypes. Faster iteration. Faster validation. 💡 My Personal Approach AI doesn’t replace UX thinking. It accelerates it. In real projects, I follow this rule: - AI for speed. - Human for strategy. - Users for validation. The result? • Faster delivery • Better alignment with stakeholders • More time spent on problem-solving • Less time on repetitive tasks And most importantly, better user experiences. If you’re a designer still afraid AI will replace you… It won’t. But designers who use AI effectively? They will replace those who don’t. Let’s build smarter. 💜 Whats your way of design? Comment below👇 UX Pilot AI Clueify #UXDesign #UIDesign #Figma #AIinDesign #ProductDesign #UXResearch #DesignProcess #Accessibility #SaaSDesign #UserExperience #DesignThinking #Prototyping #UXWriting #FutureOfDesign #designtools #uiux

Want to create AI-powered products that users actually love? Master the essential UX principles for AI and build experiences that are intuitive, trustworthy, and effective principles include..... 1) Human-centered AI design Prioritizing user needs and aligning AI features with user expectations to augment human capabilities 2) Seamless human-AI interaction Designing intuitive interfaces and clear communication to ensure a smooth collaboration between humans and AI 3) Balancing AI capabilities and constraints Understanding the strengths and limitations of AI to optimize algorithms and data quality 4) Explainability and Transparency Explaining to the user why the AI behaves, recommends, or suggests a result by providing clear explanations for AI decisions 5) User control balancing AI automation with user control by offering settings and preferences to adapt AI behavior and override AI decisions 6) Feedback mechanisms Establishing channels for users to offer feedback on system performance, enabling continuous improvement based on real user experiences 7) Managing user expectations Providing a detailed description of what users can expect from the app to manage expectations successfully 8) Error Handling Providing clear feedback and guidance to help users understand and address errors effectively #ux #ui #uxui #ai #aiux #llm #generativeai #productdesign #deepseek #chstgpt

🧠 What is human-centric design, and why does it matter? In too many organizations, humans have become variables to optimize rather than the source of innovation and growth. That's why human-centered design isn't a "soft" discipline — it's a strategic necessity. Real human-centered design begins with empathy: understanding people deeply and designing with them, not just forthem. It connects customer experience to employee experience and creates lasting value. Here's what changes with AI: When deployed intentionally, AI doesn't diminish what makes us human — it amplifies it. Rather than automating empathy away, AI can scale it across cultural divides, knowledge silos, and geographic boundaries. What becomes possible: Empathy at scale. AI helps humans respond with context and care at every interaction point. Knowledge without barriers. AI connects teams across traditional boundaries and disciplines. Human reach extended. AI enables connection across cultures and languages previously impossible at scale. This isn't AI or humans. It's AI plus humans, designed deliberately around human values. Practical Steps: 1. Map your human touchpoints. Document every person who will interact with or be affected by the system. If you can't name them, you're not ready to build. 2. Observe before you build. Watch what users do, not just what they say. The gap between the two is where design insight lives. 3. Design personas deliberately. Specify how your AI should interact differently with different stakeholders. Document and revisit these choices. 4. Build in human audit points. Identify where human judgment must remain and design those roles explicitly. 5. Don't stop — cycle. Build feedback mechanisms for continuous refinement as needs evolve. Leaders who embed human-centered design with AI as an enabler aren't just preparing for the future — they're shaping it. 📍 Find out more in our Fast Company article here: https://lnkd.in/eMgyz5jN. 📍 And in our IMD article here: https://lnkd.in/eAuVbHM5

I’ve been designing + building products for 20 years. One AI project changed everything I thought I knew. It was 5 years ago. The brief: an AI assistant for financial advisors. "Easy" I thought. I brought the playbook - understand users, map needs, prototype, iterate. Within weeks, every method had failed. User-centred design has given us incredible tools: journeys, personas, usability testing. It created a shared language for innovation and put users at the centre of product development. But it also gave us something dangerous: the illusion that good process guarantees good outcomes. Where design methods break: 🔴 They treat all problems as design problems. Not every challenge needs a workshop. Some need engineering breakthroughs. Some need business model innovation. Some need regulatory change. When your only tool is empathy, everything looks like a user experience problem. 🔴 They assume user needs reveal future possibilities. Advisors thought they wanted better dashboards. Not "AI that predicts my clients needs and anxiety levels". Revolutionary products create needs people didn't know they had. 🔴 Confuses good process with good results. Following the method perfectly doesn't guarantee you're solving the right problem. Great design comes from insight, not adherence to frameworks. What building AI systems has taught me: 🤔 The old tools need rethinking. User research couldn't predict interactions with something that evolves. Journey maps couldn't map AI that creates new paths. Prototypes couldn't capture systems that learn and change. 🤔 The real design challenge isn't the interface - it's the intelligence architecture. Should the system interrupt or wait? Learn from the user or protect their privacy? Optimise for efficiency or explainability? These aren't UX decisions. They're ethical and technical decisions that determine trust, dependency, and agency. 🤔 And critically: AI systems create feedback loops that change user behaviour over time. Traditional design assumes static user needs. AI design requires predicting how your solution will reshape the problem space. We're designing systems that could shape human behaviour for generations. User research and workshops aren't enough anymore. We need a new playbook. What I've learnt: 🟢 Ask "should we?" before "how might we". Consider consequences, not just possibilities. What data does this use? How does it learn? What could break? 🟢 Develop systems thinking. Your decisions ripple through complex networks of technology, behaviour, and culture. 🟢 Design for responsibility, not just iteration. Every design choice becomes a values statement when scaled through AI. 🟢 Question the AI narrative. Not every problem needs an AI solution. Some need better human processes. 🟢 Partner deeply with engineers and data scientists. The best AI experiences emerge from true collaboration, not handoffs. The craft evolves. The responsibility remains the same. Let’s write new rules. Who’s in?

𝗣𝗿𝗼𝗺𝗽𝘁 𝗵𝗮𝗰𝗸𝘀 𝗳𝗼𝗿 𝗯𝗲𝗮𝘂𝘁𝗶𝗳𝘂𝗹 𝗨𝗜. From a designer with 100+ Lovable projects. ( Steps + prompts ↓) 𝟭/ 𝗨𝗻𝗶𝗳𝘆 𝘆𝗼𝘂𝗿 𝘀𝗽𝗮𝗰𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 Most Lovable apps look “AI” because spacing is random. Pick a grid (8px system is best). Your whole app snaps into visual rhythm instantly. 𝟮/ 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗯𝗶𝗹𝗲-𝗳𝗶𝗿𝘀𝘁 𝗢𝗥 𝗗𝗲𝘀𝗸𝘁𝗼𝗽-𝗳𝗶𝗿𝘀𝘁 Premium UI breaks when Lovable tries to satisfy both at the same time. Define it upfront in your prompt. Example: “We design mobile-first. All layout decisions are optimized for vertical interaction, thumbs, and single column flow.” When Lovable knows the primary environment, spacing, density, and component scale become consistent — which is what makes it feel premium. 𝟯/ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗴𝗹𝗼𝗯𝗮𝗹 𝗿𝘂𝗹𝗲𝘀 𝗳𝗶𝗿𝘀𝘁 Example prompt start: "Global Design System: • Spacing = 8pt grid • Radius = 20px • Typography = Inter xs-sm-base-lg-xl • Layout = flex-first, responsive, no absolute widths • Visual Language = LiquidGlass V2 (bg-white/10–20)" This is your foundation. 𝟰/ 𝗣𝗶𝗰𝗸 𝟭 𝘁𝘆𝗽𝗲 𝘀𝗰𝗮𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝘁𝗼 𝗶𝘁 Define text sizes inside your theme: xs / sm / base / lg / xl Consistency of type = instant premium feel. Don’t freehand font sizing. 𝟱/ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗹𝗼𝗰𝗸𝘀. Don’t focus on full pages. Break down your app into reusable elements: • Buttons • Cards • Inputs • Modals You can then reference these components for any new screen. 𝟲/ 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀. AI builds consistency when you tie new elements back to already existing ones. Ex: “Use the same layout as CreateNewPortfolio.tsx, swap chart → table.” Think of it like extending a design system. 𝟳/ 𝗖𝗼𝗻𝘃𝗲𝗿𝘁 𝗮𝗹𝗹 𝗵𝗮𝗿𝗱 𝘄𝗶𝗱𝘁𝗵𝘀 → 𝗳𝗹𝗲𝘅 AI builders default to fixed pixel thinking. Real premium UI is responsive, and fluid. Almost every container should use "w-full flex" logic. 𝟴/ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗱𝗮𝗿𝗸-𝗳𝗶𝗿𝘀𝘁 𝗼𝗿 𝗹𝗶𝗴𝗵𝘁-𝗳𝗶𝗿𝘀𝘁 You ship faster when you commit early. Dark vs Light decides shadows, contrast, gradients, glass values, and brand emotion.