Building Trust Through Chatbot User Experience

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

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

*To build trust in complexity, offer small choices and fast feedback* I strongly believe product simplicity and predictability are a superpower. They give the user a sense of control, which is a gift when the world feels so complicated. But some things are legitimately complex. What gives the user a sense of control when predictability is hard to come by? My take: Giving the user a chance to *participate* in the process by laying out steps, enabling them to make specific choices, and offering a clear feedback loop on each small decision. This may make the flow longer, but it gives users a chance to viscerally understand what’s happening. A while ago I got an alarming privacy notification on an important account. I was immediately worried. But the product’s recovery flow calmed me down. Why? It: 1. Laid out all the steps I’d go through, giving me a clear roadmap for what to do. 2. Channeled my anxiety into actions, even if they were small. There were prompts like “Check whether password is compromised? Yes / No”. Is that a necessary prompt? Who would say “no”? But in the moment, the ability to participate in the process of securing my account gave me a sense of control. 3. Gave me fast feedback on each choice by turning each step green on completion. By the end of the list, I felt a sense of relief. Realistically, that product could have taken all those actions without my input. But getting to participate in each step gave me a sense of control. I saw the same thing with a new AI tool my team was working on. Our temptation was to take user input up front and come back with a solution. But our customers didn’t yet trust the magic black box of AI recommendations. Instead, what helped was inserting feedback steps explaining what we were considering and offering the user a chance to change direction at each step. It added friction, but it built trust faster. Then over time, we could remove those interim feedback steps and automatically make decisions. Compare that to a customer service page where you type a question into a contact form and get a message that says, “Thanks, we’ll take care of it.” You don’t really get an understanding of the overall process, a chance to make smaller decisions, or feedback on whether you made the right choices. I’m always stressed about whether I did it right! This applies to people too. When I’m building a relationship with a new manager or peer, I try to frequently outline what I’m doing and give them a chance to redirect. After a few weeks, we know each others’ style and I can stop. Action is the best antidote to fear. Especially when someone is stressed out and longing for control, it helps to ground them in a clear step-by-step process, give them a chance to participate in solving their problem, and letting them know the impact of each choice. That naturally creates some relief, and helps them channel their concern into action. (For regular updates, check out amivora.substack.com!)