Tips to Create Engaging AI Interactions

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

Anyone can write a prompt. But only experts know how to engineer context. If you want precise, reliable, and human-like AI responses, it’s not just what you ask - it’s how much context you provide. This guide breaks down the 10 key elements that make a world-class prompt through the lens of Context Engineering: 1. Task Context – Clearly define what the model should do and in what role. 2. Tone Context – Set the voice and communication style for consistency. 3. Background Data – Add relevant documents, facts, or images for grounding. 4. Detailed Rules – Include do’s and don’ts to shape the AI’s behavior. 5. Examples – Provide sample interactions to guide response style. 6. Conversation History – Maintain continuity by giving recent context. 7. Immediate Request – Specify the current user’s question or action. 8. Step-by-Step Thinking – Encourage logical reasoning before answering. 9. Output Formatting – Tell the model how to structure its response. 10. Prefilled Response – Use starter responses to set direction or tone. When all 10 layers come together, your prompt stops being a simple query, it becomes a complete instructional environment. That’s the difference between a good answer and an expert-level interaction. What works well according to you?

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Most brands sound robotic with AI. The smartest companies use AI to sound more human. After analyzing hundreds of brand transformations, I've discovered that successful companies use AI strategically to amplify their authentic voice. Here are 11 ways to use AI to make your brand sound more human: 1. Voice Mining ↳ Analyze your best-performing content ↳ Extract patterns in tone and language 💡 Pro Tip: Focus on posts with highest engagement-to-view ratios, not just total numbers. 2. Competitor Analysis ↳ Study successful voices in your space ↳ Identify gaps in brand positioning 💡 Pro Tip: Look for what competitors aren't saying. That's your opportunity. 3. Audience Feedback Loop ↳ Use AI to analyze customer comments ↳ Adjust voice based on engagement 💡 Pro Tip: Pay special attention to comments that disagree as they reveal blind spots. 4. Consistency Framework ↳ Build voice guidelines with AI ↳ Create tone variations for channels 💡 Pro Tip: Create a simple "voice spectrum" from casual to formal for different situations. 5. Value Proposition Enhancement ↳ Extract key differentiators ↳ Align voice with core benefits 💡 Pro Tip: Your voice should reflect your most valuable differentiator, not just sound good. 6. Storytelling Elements ↳ Generate narrative frameworks ↳ Test different story angles 💡 Pro Tip: What problem do you uniquely solve? 7. Cultural Alignment ↳ Map brand values to voice ↳ Ensure authenticity in messaging 💡 Pro Tip: If your culture is casual, your AI outputs shouldn't be formal 8. Content Calibration ↳ A/B test different voices ↳ Measure engagement metrics 💡 Pro Tip: Test one element at a time. Changing everything at once teaches you nothing. 9. Persona Development ↳ Create detailed brand personas ↳ Map voice to target audience 💡 Pro Tip: Interview your best customers. Their language should influence your AI prompts. 10. Emotional Intelligence ↳ Analyze emotional impact ↳ Fine-tune brand empathy 💡 Pro Tip: Every post should trigger at least one strong emotion. 11. Voice Evolution System ↳ Monitor voice performance ↳ Adapt to market changes 💡 Pro Tip: Schedule monthly voice audits. Brands that don't evolve disappear. The brands winning with AI aren't chasing perfection. They're doubling down on authenticity. By using these tools strategically, you can scale content without sacrificing the human touch that builds trust and loyalty. Which of these techniques will you implement first? Share below 👇 ♻️ Repost this if someone in your network would like this. Follow Carolyn Healey for more content about AI.

Mastering Conversations with AI 🤖💬 Here’s a guide to making the most of AI conversations: 1. Be Clear and Specific: Narrowing the Probability Space 🎯 Instead of vague requests like “Tell me about cars,” ask specific questions: “Explain the top technological advancements in electric vehicles in the last decade, focusing on batteries and autonomous driving.” Why it works: Specific prompts narrow the range of possible responses, making it easier for the AI to give you a relevant and accurate answer. 2. Provide Context & Examples: Optimizing the Input Window 🧠 Provide context and examples to ensure the AI understands your request. For instance, in legal tasks, context-specific details improve results. Why it works: LLMs process information within a context window, and context helps them make better-informed connections between concepts. 3. Break Complex Tasks into Smaller Steps: Computational Efficiency ⚙️ Rather than asking an AI to do everything at once, break tasks down. Start with an outline, then expand on each part. Why it works: Breaking tasks into steps helps the AI focus and reduces the risk of errors, making the process more efficient. 4. Use the Politeness Principle: Pattern Recognition in Training Data 🙏 Being polite, using "please" and "thank you," can improve the AI’s responses. Why it works: Polite queries activate patterns linked to higher-quality responses, providing more thoughtful and detailed output. 5. Iterate Through Follow-up Questions: Feedback Loop Optimization 🔄 If the first answer doesn’t quite hit the mark, refine your question and ask again. Use follow-ups to clarify or dive deeper. Why it works: Each follow-up helps refine the AI’s understanding, gradually leading to a more accurate answer, much like optimization in machine learning. 6. Encourage Creativity: Activating Diverse Neural Pathways 🎨 Ask the AI to think "outside the box" when you need creative ideas. Why it works: This broadens the AI’s output range, leading to more unconventional and creative ideas, perfect for brainstorming. 7. Treat Each AI as an Individual 👤 Each model has its strengths. Some are great at writing, others at technical tasks. Use the right assistant for the right job. Why it works: Different LLMs are fine-tuned for various tasks, so knowing their strengths helps you maximize their potential. 8. Consider Starting Fresh When Needed 🔄 If the conversation becomes irrelevant or cluttered, start fresh to reset the context. This ensures the AI’s full attention on your new prompt. Why it works: LLMs have limited context windows, and starting fresh ensures the AI processes your input without prior distractions. 9. Engage in Two-way Communication 💬 Don’t just ask and move on. Keep the conversation going with follow-ups to refine the answers and explore deeper. Why it works: Ongoing dialogue helps the AI adjust to your preferences, leading to more relevant and refined responses.

HOW we work with AI matters. Emerging modes of interaction are reshaping roles. Most people are stuck at method 1 or 2. Here are 4 key AI interaction types—and when to use each: 1️⃣ AI as a Microtasker One-shot problem solver. Ideal for quick, contained tasks: rewriting a sentence, generating a one-off image, answering a data question, or fixing a bit of code. High precision, low overhead. 2️⃣ AI as a Copilot Persistent, live support for extended tasks. It stays with you in pairing mode—watching your screen, listening, coding, brainstorming. A back-and-forth partner for creative or technical work in real time. Human in the loop, always. 3️⃣ AI as a Delegate Assign it a goal and let it work autonomously, for minutes or days. Great for complex, long-form tasks like research—no human in the loop. It self-directs, self-checks, and reports back after/while completing tasks. Think: Manus AI, autonomous agents. 4️⃣ AI as a Teammate A presence across your team or org. It joins meetings, takes notes, surfaces insights, runs simulations, offers opinions. Can even be in a manager role. Not just assisting YOU but enhancing the collective. An ambient, participatory AI system. And roles 3 and 4 mean the AI can work in a completely different way than our human systems. Knowing which role to use—and when—is the new AI literacy.

Whether you're using Replit Agent, Assistant, or other AI tools, clear communication is key. Effective prompting isn't magic; it's about structure, clarity, and iteration. Here are 10 principles to guide your AI interactions: 🔹 Checkpoint: Build iteratively. Break down large tasks into smaller, testable steps and save progress often. 🔹 Debug: Provide detailed context for errors – error messages, code snippets, and what you've tried. 🔹 Discover: Ask the AI for suggestions on tools, libraries, or approaches. Leverage its knowledge base. 🔹 Experiment: Treat prompting as iterative. Refine your requests based on the AI's responses. 🔹 Instruct: State clear, positive goals. Tell the AI what to do, not just what to avoid. 🔹 Select: Provide focused context. Use file mentions or specific snippets; avoid overwhelming the AI. 🔹 Show: Reduce ambiguity with concrete examples – code samples, desired outputs, data formats, or mockups. 🔹 Simplify: Use clear, direct language. Break down complexity and avoid jargon. 🔹 Specify: Define exact requirements – expected outputs, constraints, data formats, edge cases. 🔹 Test: Plan your structure and features before prompting. Outline requirements like a PM/engineer. By applying these principles, you can significantly improve your collaboration with AI, leading to faster development cycles and better outcomes.

I brainstormed a list of things I ask myself about when designing for Human-AI interaction and GenAI experiences. What's on your list? • Does this person know they are interacting with AI? • Do they need to know? • What happens to the user’s data? • Is that obvious? • How would someone do this if a human was providing the service? • What parts of this experience are improved through human interaction? • What parts of this experience are improved through AI interaction? • What context does someone have going into this interaction? • What expectations? • Do they have a specific goal in mind? • If they do, how hard is it for them to convey that goal to the AI? • If they don't have a goal, what support do they need to get started? • How do I avoid the blank canvas effect? • How do I ensure that any hints I provide on the canvas are useful? • Relevant? • Do those mean the same thing in this context? • What is the role of the AI in this moment? • What is its tone and personality? • How do I think someone will receive that tone and personality? • What does the user expect to do next? • Can the AI proactively anticipate this? • What happens if the AI returns bad information? • How can we reduce the number of steps/actions the person must take? • How can we help the person trace their footprints through an interaction? • If the interaction starts to go down a weird path, how does the person reset? • How can someone understand where the AI's responses are coming from? • What if the user wants to have it reference other things instead? • Is AI necessary in this moment? • If not, why am I including it? • If yes, how will I be sure? • What business incentive or goal does this relate to? • What human need does this relate to? • Are we putting the human need before the business need? • What would this experience look like if AI wasn't in the mix? • What model are we using? • What biases might the model introduce? • How can the experience counteract that? • What additional data and training does the AI have access to? • How does that change for a new user? • How does that change for an established user? • How does that change by the user's location? Industry? Role? • What content modalities make sense here? • Should this be multi-modal? • Am I being ambitious enough against the model's capabilities? • Am I expecting too much of the users? • How can I make this more accessible? • How can I make this more transparent? • How can I make this simpler? • How can I make this easier? • How can I make this more obvious? • How can I make this more discoverable? • How can I make this more adaptive? • How can I make this more personalized? • How can I make this more transparent? • What if I'm wrong? ------------ ♻️ Repost if this is helpful 💬 Comment with your thoughts 💖 Follow if you find it useful Visit shapeofai.substack.com and subscribe! #artificialintelligence #ai #productdesign #aiux #uxdesign

Here’s the secret to AI-first products: If your AI isn’t where your users already work, it’s just a cool tool they’ll never adopt. Too many teams build standalone apps for developer convenience, only to see low adoption because they disrupt user workflows. Want to create AI that feels like a co-pilot, not a detour? Too many teams treat AI like an add-on instead of designing around how people actually work. If you want your tool to stick, start by testing where and how users will reach for it—not just which feature they like. 1. Watch before you wireframe Shadow your users for days. Note which apps they open first, what data they reference, where they pause. When you map their natural workflow, you can slot your AI into it—rather than forcing them onto a new path. 2. Make the channel your core hypothesis Is the right interface a sidebar in your CRM, a chatbot in Teams, a Slack app, or a push notification on mobile? Instead of asking “is lead-scoring useful?”, test “will sales reps use this inside their CRM?” Show partners quick sketches in each context and see which one they instinctively click. 3. Decouple logic from presentation Build one robust AI engine that powers a chat widget, a browser extension or a simple web view. When someone asks for a new capability, ask “What decision are you making?” and “Where do you need to make it?” You avoid duplicate work and can adapt fast to new platforms. 4. Capture data as part of the flow The best way to train your model is to let users work as usual. If your AI suggests optimal campaign parameters, log every tweak automatically. Don’t make marketers export logs or fill out extra forms—that creates gaps and biases your training set. 5. Earn trust through real-time dialogue In a conversational UI, let the AI ask clarifying questions (“I see you’re about to launch the summer campaign—should we include last quarter’s top keywords?”) and explain its suggestions inline (“These three segments drove 18% more conversions last month”). Then package the output in a ready-to-send summary or email draft. 6. Shift from one-off tasks to continuous value If your tool only fires during project kick-off, users will forget it. Surface a lightweight insight each week—like an alert when support ticket volume spikes or when a key metric drifts. Those small, correct nudges build confidence and prime users for the big recommendations they’ll need later. Validate your assumptions about channel, data capture, trust and engagement before you write a line of production code. When your AI lives inside the tools people already use, it becomes part of their daily routine—and that’s when it becomes indispensable. The Big Takeaway: AI-first products must be invisible, conversational, and proactive, living inside users’ existing tools. Don’t build a standalone app for control—tackle the engineering to embed your AI where it belongs. That’s how you build a platform, not a feature.

When it comes to integrating AI into our projects, the key to success is clear: communication is everything. I often get asked, "How do you make AI understand and execute your vision effectively?" The answer? Over communicate as if you’re interacting with a highly intelligent engineer who's just a bit socially awkward. Here’s how I ensure our AI systems not only understand but excel in delivering precisely what we need: ✅ Clarify and Confirm: Always make your instructions clear and then verify understanding. Ask the AI, "Do you understand?" or "Does this make sense?" This step ensures you're both on the same page. ✅ Proactive Inquiries: Encourage the AI to ask you questions. This can be pivotal in defining the scope and specifics of your project. Ask, "What more do you need to know?" to help it gather all necessary details. ✅ Define the Audience and Objectives: Be explicit about who your message is targeting and what you want your audience to take away from it. Understanding the audience’s needs helps tailor the AI’s output effectively. ✅ Set Clear Expectations: Explain the ultimate goal of your communication. If your project is a multi-stage one, clarify this to the AI. Setting the context right from the start is crucial for continuity and relevance. ✅ Continual Onboarding: Think of AI as a new team member. Just like any employee, AI needs proper onboarding, training, and time to adjust. The more effort you put into this process, the more productive the AI will become. ✅ Generosity Leads to Gains: With AI, the more you put in, the more you get out. Overgive, overshare, and always seek the optimal way to provide instructions. This ensures the results you receive aren’t just good; they’re phenomenal. Integrating AI isn’t just about using a tool; it’s about fostering a relationship where clear, continuous communication opens the doors to unmatched efficiency and innovation. Are you ready to change how you interact with AI and see the difference it makes in your projects? #AICommunication #TeamIntegration #Innovation #BusinessStrategy #ZeevWexler #Leadership #ai #gpt #chatgpt