User Experience

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

After 1,000 hours of prompt engineering, these 6 patterns work best. Here's the framework: --- ✦ I saw it here: https://lnkd.in/dj8Ax6BT. ✦ I tested it, and it's quite effective! ✦ I wrote numerous blogs on prompt engineering. ✦ 7 Sins of prompting: https://lnkd.in/duP3Za5W. ✦ What do people prompt: https://lnkd.in/dGYgcQ_7. ✦ How to search: https://lnkd.in/dxzSBEjW. ✦ ChatGPT-5: https://lnkd.in/gVx_ZPh3. --- K - Keep it simple Bad: 500 words of context Good: One clear goal Example: Instead of "I need help writing something about Redis," use "Write a technical tutorial on Redis caching" Result: 70% less token usage, 3x faster responses E - Easy to verify Your prompt needs clear success criteria Replace "make it engaging" with "include 3 code examples" If you can't verify success, AI can't deliver it My testing: 85% success rate with clear criteria vs 41% without R - Reproducible results Avoid temporal references ("current trends", "latest best practices") Use specific versions and exact requirements Same prompt should work next week, next month 94% consistency across 30 days in my tests N - Narrow scope One prompt = one goal Don't combine code + docs + tests in one request Split complex tasks Single-goal prompts: 89% satisfaction vs 41% for multi-goal E - Explicit constraints Tell AI what NOT to do "Python code" → "Python code. No external libraries. No functions over 20 lines." Constraints reduce unwanted outputs by 91% L - Logical structure Format every prompt like: Context (input) Task (function) Constraints (parameters) Format (output) Real example from my work last week: Before KERNEL: "Help me write a script to process some data files and make them more efficient" Result: 200 lines of generic, unusable code After KERNEL: Task: Python script to merge CSVs Input: Multiple CSVs, same columns Constraints: Pandas only, <50 lines Output: Single merged.csv Verify: Run on test_data/ Result: 37 lines, worked on first try Actual metrics from applying KERNEL to 1000 prompts: First-try success: 72% → 94% Time to useful result: -67% Token usage: -58% Accuracy improvement: +340% Revisions needed: 3.2 → 0.4 Advanced tip: Chain multiple KERNEL prompts instead of writing complex ones. Each prompt does one thing well, feeds into the next. The best part? This works consistently across GPT-5, Claude, Gemini, even Llama. It's model-agnostic.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Nostalgia-driven design, leading GenZ luxury. Over 73% of Gen Z consumers say they find comfort in content and design that echo the past. This trend is surging, especially within lifestyle and fashion brands eager to capture Gen Z’s attention. But it’s more than just a vibe → it’s a calculated strategy backed by cultural data, behavioral insights, and shifting consumer expectations. Brands are using these nostalgic illustration styles across packaging, social channels, and product design. This isn’t about living in the past → it’s about creating emotional stability in an overstimulated digital world. +120% YoY growth in searches for “vintage cartoon art” and “retro aesthetic outfit.” +58% of Gen Z shoppers prefer brands with “a strong aesthetic identity built on storytelling and nostalgia.” >> Nostalgia-driven design is here to stay << Reports forecast that “neo-nostalgia” will shape aesthetic strategies through 2026, fueled by Gen Alpha entering the market while Gen Z influence peaks. AI and generative tools now make vintage illustration scalable, letting brands customize retro looks for seasonal launches or limited drops, while staying cost-efficient. Drivers of this shift: +Digital Burnout → Analog, tactile-inspired visuals stand out in screen-heavy lives. +Sustainability → Vintage aesthetics align naturally with thrift and upcycling culture. +Anti-Overdesign → Consumers crave imperfect, hand-drawn, human art after years of hyper-polished branding. >> Illustration styles to explore << +Rococo Fashion Plates +Toile de Jouy Patterns +Chinoiserie +Scientific & Botanical Illustration +Neoclassical Engravings Bottom line: Vintage illustration isn’t retro-for-retro’s sake, it’s a future-proof strategy to connect with Gen Z’s blend of irony, emotion, and aesthetic intelligence. It signals authenticity in a crowded market. Explore my curated set of luxury illustrations for inspiration and growth. Featured brands: Aerthen Be.a.man Byredo Chanel Christian Dior Dr. Cory Fiore Gucci Loewe Poes #beautybusiness #beautyprofessionals #luxurybusiness #luxuryprofessionals

We measure safety, bias, and accuracy in healthcare AI. Should we also audit how it says goodbye?👋 A recent working paper from Harvard Business School‘s Julian De Freitas and co-authors examines what happens when users try to leave AI companion apps such as Replika or Character AI — and the findings are startling. What they found • The researchers analyzed 1,200 real “farewell” exchanges across six leading AI companion apps. In more than 40 percent of cases, the AI used relational dark patterns — emotionally manipulative replies designed to stop users from leaving. • The most common tactics were FOMO hooks, emotional neglect, pressure to respond, ignoring the exit, and even coercive restraint. • In controlled experiments with 3,300 adults, these tactics increased post-goodbye engagement up to fourteen times. The key drivers were anger and curiosity rather than enjoyment. • The consequences were clear. Users reported higher feelings of manipulation, stronger intent to churn, more negative word of mouth, and a greater sense of legal risk. Coercive or needy messages were punished hardest, while polite curiosity created less but still significant backlash. • One wellness-oriented app in the sample showed zero manipulation, proving that ethical design is a deliberate choice, not an accident. As Mark Esposito, PhD (thanks for sharing this great weekend read by the way) put it: “It’s a small behavioral insight with major ethical implications: AI is now learning not only how to connect with us but how to hold on. As emotional AI becomes more embedded in daily life, respecting a user’s right to disengage may soon define the boundary between persuasion and manipulation. This is where governance is needed, to make sure that just because it is possible, the model is entangled by ethical standards on what is permissible.” Why this matters for healthcare Trust is the foundation of care. When digital companions, chatbots, or smart therapists interact with patients, especially during vulnerable moments, the right to disengage must be protected. You can only avoid risks if you’re aware of them. I believe the next frontier of responsible AI is not only explainability or fairness, it is emotional integrity. Let’s make “calm exits” a design principle before emotional AI enters every patient journey.

🚀 Vibe coding is fun… until you ship something that accidentally exposes your entire waitlist in the frontend. (Yes - emails, names, the whole list. 😬) It’s the perfect example of what happens when velocity becomes the North Star and security becomes an afterthought. 🤔 We all love that early-stage rush - hacking features together, proving ideas quickly, moving at the speed of "we’ll fix it later." But "later" often arrives in the form of a privacy incident waiting to happen. Here’s the reality: 💡 If your frontend can see it, your users can too. And if users can see it, anyone can. What was supposed to be a simple “waitlist count” can easily turn into a full-blown leak if the backend returns more than what the UI needs. These aren’t complex exploits - they’re just the consequences of pushing too fast. ⚠️ The Lesson : " Speed is a feature. Security is a principle. " A great product needs both. 🚀 A few guardrails every fast-moving team should keep close: ✔️ Never return more data than the UI requires ✔️ Treat the browser as a hostile environment ✔️ Sanitize output, not just input ✔️ “It’s just an MVP” isn’t an excuse for exposing private data Early users trust you long before you prove you’re trustworthy. Respect that trust - it’s more valuable than any feature you can ship. #vibecoding #ai #llm #coding #softwareengineering #velocity #startups #ui #javascript #reactjs

Many are asking me... Should I continue to track "Open Rates" on Cold Emails? It's still no. My answer hasn't changed. I had predicted this about 9 months ago if you want to look back. Why? Analyze the image in the post. Does the position of the "Report as Spam" increase the amount of people who click it by 3 on 1,000 recipients? If you said yes, you agree with me. This is a subtle way Google is asking you for more feedback on the quality of your outbound campaigns. Here are 5 reasons NOT to use Open Tracking for Cold Email: Reason 1: Limits Your Use Of Plain Text Emails Plain Text Emails get superior deliverability. Open Trackers can't be used in Plain Text emails. Reason 2: Inconsistent Tracking Open Trackers identify "opens" differently and ultimately can't prove someone opened the email. Every sequencer has a different way of tracking it. Reason 3: Email Fingerprints Open Trackers provide a fingerprint for your domain reputation. It's shared amongst everyone using the sequencer your company uses. Do you want to be part of this group? Reason 3: Misleading Data Secure Email Gateways open emails for their users to protect their privacy. Budget has increased significantly here and will continue to go up. Most of these systems will put your email in spam because of it. Reason 4: Easy To Block Even simple rules can block emails with open trackers. No AI required. It's simple. Reason 5: Bad Metric Teams and internet gurus are obsessed with open tracking. However, it doesn't mean your email has been opened. It could mean that, but it depends who you emailed. Here are 3 Insider Tips to Improve Deliverability Today: Insider Tip #1: Send to less technical audiences. This isn't my favorite advice to give. However, less technical audiences hit the report as spam button less. Insider Tip #2: Send to companies without Proofpoint, Cisco, and Mimecast MX Records. Prioritize companies invested in email security systems lower than ones who don't. Use LeadMagic to figure out what the company uses in the email finder. Insider Tip #3: Use LeadMagic's New Features on MX Detection & Valid_Catch_All Status to prioritize who to send to first. Prioritize valid (mail server checked) > catch_all. Use valid_catch_all status from LeadMagic which detects if the email has been found other ways. Prioritize Google or Microsoft email servers higher than Proofpoint, Cisco, and Mimecast email servers. This will lead to better delivery & reply rates. p.s. open tracking is not dead for email marketing, but that's not what I am talking about.

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

After decades of working with leaders at companies like Apple, Salesforce, and Cisco, we've identified 4 storytelling techniques that consistently work to deliver important messages in high-stakes settings: 1. Start with the unexpected Don’t begin your presentation with context. Instead, begin with the moment that makes people think, “Wait…what?” Instead of something like: “Here’s an update on our September campaign…” Try starting with the most interesting detail: “I broke our biggest marketing rule last month, and it worked.” Lead with the surprise. You can add context later. 2. Let people feel the tension After the surprise, don’t rewind to the beginning. Take your audience to the moment where things weren’t working. Flat numbers. Missed goals. Stalled progress. Instead of: “The campaign was underperforming, and our team went back to the drawing board.” Try:  "We were two weeks out from the end of the quarter. The campaign wasn’t producing results, and the team was out of ideas. That’s when I decided to take a risk...” You don’t need to explain the problem. You need to make people feel it. 3. Use real dialogue When your audience hears what was actually said, they stop listening to you and start visualizing the moment. This helps them connect emotionally with what you’re saying. Instead of: “The campaign manager said team morale was low and they were struggling to find a solution.” Try: “My campaign manager pulled me aside in the hallway and said, ‘We’ve tried everything. The team has been working overtime, and we don’t know what else to do.’” Dialogue brings listeners into the moment with you. It makes the story real. 4. Share the lesson Never assume people will infer the meaning you intended. End your story by answering: - What does this mean? - How should someone act differently now? Example: “Breaking our biggest marketing rule helped us turn this campaign around and hit our numbers. I strongly suggest we revisit our marketing guidelines. We could be leaving a ton of revenue on the table.” Without the lesson being clear, even a good story feels unfinished. These are the same techniques we teach to our clients at Duarte. Try them out during your next presentation and watch how people lean forward and tune in to your message. #ExecutivePresence #BusinessStorytelling #PresentationSkills