Chatbots for Customer Engagement

AI in Customer Support isn’t new. I’ve been rethinking how we actually use it. Customer Support is moving past basic "faster replies" and learning to implement Claude as a core part of our workflow. The goal? Shifting from reactive firefighting to structured, scalable systems. It’s a work in progress, but here is the blueprint we’re using to turn Claude into a true CX reasoning engine: 1️⃣ It’s not about speed. It’s about structure. Yes, you can draft replies faster. But the real value comes from setting it up properly: → align it with your tone and guidelines → connect it to your knowledge base → define clear boundaries (what it can and can’t say) → train it to understand context, not just keywords That’s how you get consistent, reliable output across the team. 2️⃣ It helps move Support from reactive → proactive Used well, it’s not just answering tickets. It’s helping you: → detect sentiment and urgency → identify recurring friction points → surface gaps in self-service → spot early churn signals That’s where Support starts influencing the whole customer experience. 3️⃣ It fits into your existing workflows (not replaces them) The most effective setups I’ve seen are simple: → Claude + Zendesk → ticket analysis → Claude + Zapier → automate workflows → Claude + Gong→ review calls → Claude + Intercom → inbox support → Claude + n8n → workflow automation → Claude + Notion → knowledge management No complex rebuilds. Just better use of what you already have. 4️⃣ The quality of output = quality of input Small things make a big difference: → assign a role (support agent, CX lead, analyst) → provide context (customer, goal, constraints) → iterate with examples (good vs bad responses) Without this, you get generic answers. With it, you get something your team can actually use. From a leadership perspective, this isn’t about “adding AI.” It’s about designing how your Support team operates at scale. Because the goal isn’t to answer more tickets. It’s to build a system where fewer things break, and when they do, the experience still feels consistent. If you’re already using AI in Support, what’s actually working for you? 👇

A few weeks ago I told my team that AI needs to do 92% of their work or they'll get left behind. Here’s how we're doing it (and why): Step 1: Get ChatGPT Plus/Pro Step 2: Create your master prompt • Tell AI: "I'm [your role] at [company type]. Create a master prompt for me. Ask me every question you need to give me the most context possible." • Spend 30-45 minutes answering everything it asks • Save the output as a PDF • Upload this to every new chat so AI knows your full context Step 3: Build system prompts Master prompts tell AI who you are. System prompts tell AI HOW to work. Here's the process: • Ask AI to create any output (email, ad, report) • Keep refining until it's perfect (3-6 iterations) • Then ask: "Write the system prompt that would have generated this output" • Save that prompt - it's now your intellectual property Now you have the exact formula to get that quality every time. Step 4: Use project folders  Think of these like rooms in your office with all context on the walls. • Create a project for each major area of your life/business • Upload your master prompt + all relevant documents • Every conversation builds on previous context • Share folders with your team for instant knowledge transfer I use this for investment decisions, business strategy, even family planning. Step 5: Set your custom instructions This makes AI remember how you like outputs formatted. Go to Settings → Personalization → Custom Instructions: • Tell it your communication style (short, bullet points, no fluff) • Remove AI language like "delve" and "moreover"  • Set your default tone and format preferences Never repeat formatting requests again. Step 6: Turn everything into custom GPTs These are your AI employees that do specific tasks consistently. • Take your best system prompts • Create custom GPTs for each repeatable task • Share them with your team • Update once, everyone gets the improvement I have custom GPTs for: emails, content creation, financial analysis, hiring, strategy docs. Step 7: Refine and improve Use AI to teach you AI. • Ask it to create your master prompt • Ask it to write your system prompts  • Ask it to suggest custom instructions • Ask it to help you build better prompts Here's what 92% actually looks like: - Content: AI does research, outlines, first drafts. You edit and add your voice. - Operations: AI creates SOPs, analyzes processes, suggests improvements. You decide. - Finance: AI analyzes reports, creates models, finds insights. You make decisions. - Strategy: AI processes information, suggests options. You choose direction. The 8% that stays human: Vision, taste, final decisions, and emotional intelligence. My team went from thinking AI was "kind of helpful" to saying it's their most valuable employee. It could be yours too. -DM P.S. If you want my complete prompting template and the 7 system prompts that save me 15+ hours per week, MESSAGE ME the word "AI" and I'll send it over. My gift to you 👊

"Conversational Agents as Catalysts for Critical Thinking" Now this is good use of LLMs. A conversational AI acting as a devil’s advocate can improve group decision-making by subtly reshaping social dynamics, challenging dominant opinions and enabling more inclusive perspectives. There is great potential in AI "nudging" more useful human group collaboration, in everything from student work through board discussions. There has been some interesting work and research in the space, but it is limited and there needs be more. This research study (link in comments) showed: 🧠 AI enhances decision quality and process satisfaction. The AI-generated counterarguments led to significant improvements in how participants rated the decision-making process (5.10 to 5.55) and outcomes (5.31 to 5.89) on a 7-point scale. These gains came without significantly increasing cognitive workload, suggesting AI can enrich discussions without overburdening participants. 😊 Juniors felt more heard, seniors stayed satisfied. Junior (minority) members saw the biggest boost: process satisfaction rose by 0.76 and outcome satisfaction by 0.88. Meanwhile, senior (majority) members maintained high satisfaction across both conditions, indicating the AI helped juniors speak up without alienating others. 🙅♂️ AI reduced pressure to conform. The system’s devil’s advocate role legitimized dissent, encouraging minority opinions and mitigating groupthink. Juniors reported feeling “less isolated,” with the AI helping to shift group norms toward more inclusive deliberation. 🛠️ Success depends on timing, tone, and adaptability. The system worked best when its counterarguments were well-timed, empathetic, and contextually aware. Its greatest impact was not in changing decisions, but in enabling more open, balanced, and confident dialogue—especially from those with less power in the room.

60% of support tickets are repetitive. And, customers expect immediate responses. That creates pressure on teams and frustration for customers. This is why support is one of the most practical and now proven places to apply AI. AI can handle common, repeat questions instantly, in your tone, using your knowledge base and CRM data. That frees up humans to focus on situations that require judgment, empathy, and creativity. One of our customers, The Knowledge Society (TKS) Society, did exactly that. Every enrollment season, they saw a surge of messages across email, Facebook Messenger, and WhatsApp. The busiest time of year was also the most overwhelming for their team. They implemented the Customer agent to answer common enrollment questions around the clock. Today, close to 80% of inquiries are handled automatically. Their team now spends more time on complex conversations and less time copying and pasting the same answers. The (ISSA) International Sports Sciences Association also scaled with Customer Agent. They were managing multiple support channels across different tools. The experience was fragmented for their team and inconsistent for customers. By introducing an AI agent to handle repetitive questions across channels, they cut response times in half and created a more consistent experience. Over 8,000 companies are already using HubSpot’s Customer Agent, with resolution rates above 67%. This is the real opportunity with AI in support.

🧔🏽♂️ Design Patterns For AI Chat Interfaces. With practical guidelines on how to designing more useful, and less annoying AI chat ↓ 🚫 Nothing erodes trust more than disguised AI. 🤔 Often users dismiss AI chats almost instinctively. ✅ Users expect an option to “speak to human”. ✅ Be transparent about who users speak to. ✅ Wait for users to end a chat on their terms. ✅ Use separate avatars for AI bot and humans. ✅ Context changes over time: collapse older chats. ✅ Support pinning chats + highlight useful bits. ✅ Let users adjust granularity of reasoning traces. ✅ Allow users to restore iterations of canvases. ✅ Allow users to collapse chat without ending it. ✅ For long, complex tasks → full page screen. ✅ For multi-tasking, co-creation → side panel. ✅ For short, momentary tasks → chat widget. ✅ On mobile, full page AI chat works best. As many product teams race to not fall behind AI features in their products, we see AI chat interfaces becoming almost second nature every time AI initiative is launched. However, people have difficulty articulating intent in a chat, and good old UI controls — buttons, presets, radio buttons could help there. Nothing erodes trust more than an AI that desperately pretend to be a human. We might not be able to distinguish AI-generated content from human-crafted content, but human conversations differ significantly from AI chats — and there people spot the difference almost immediately: – People talk in quick bursts of text → AI is verbose (by default). – People can respond with 1–2 words → AI respond with sentences. – People never receive unfinished text → AI “streams” output live. – Messages can arrive unprompted → AI responds to prompts. – People have strong opinions → AI is apologetic, overcorrects itself Knowing that AI is on the other side isn’t really a problem — it’s what is required to build trust. And when people realize they talk to a chatbot, they are more direct, use “keyword” language, and avoid politeness markers. But when service does provide access to humans, it shows that company cares about customers. Human answer might not be accurate or swift, but it builds a much stronger relationship with the brand, especially when things go unexpected ways. 💎 Useful resources: Visa Design System: Chat UI Patterns https://lnkd.in/ewVjfr86 The Quest For Usable AI, by Michael Gower https://lnkd.in/eeq83btK Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 UX Guidelines For Chat UX, by Raluca Budiu https://lnkd.in/e7-RErGE #ux #design

Two weeks ago I said AI Agents are handling 95% of our sales and support and I replaced $300k of salaries with a $99/mo Delphi clone. 25+ founders DM’d me… “HOW?” Here’s the 6 things you MUST do if you want to run your entire customer-facing business with AI: 1. Create a truly excellent knowledge base. Your AI is only as good as the content you feed it. If you’re starting from zero, aim for one post per day. Answer a support question by writing a post, reply with the post. After 6mo you have 180 posts. 2. Have Robb’s CustomGPT edit the posts to be consumed by AI. Robb created a GPT (link below) that tweaks posts according to Intercom’s guidance for creating content for Fin. The content is still legible to humans, but optimized for AI. 3. Eliminate recursive loops - because pissed off customers won’t buy If your AI can’t answer a question but sends the customer to an email address which is answered by the same AI, you are in trouble. Fin’s guidance feature can set up rules to escalate appropriately, eliminate loops, and keep customers happy. 4. Look at every single question every single day (yes, EVERY DAY). Every morning Robb looks at every Fin response and I look at every Delphi response. If they aren’t as good as they could possibly be, we either revise the response, or Robb creates a support doc to properly handle the question. 5. Make sure you have FAQs, Troubleshooting, and Changelogs. FAQs are an AI’s dream. Bonus points if you create FAQ’s written exactly how your customers ask the question. We have a main FAQ, and FAQs for each sub section of our support docs. Detailed troubleshooting gives the AI the ability to handle technical questions. Fin can solve 95% of script install issues because of our Troubleshooting section. Changelogs allow the AI to stay on top of what’s changed in the app to give context to questins about features and UI as it changes. 6. Measure your AI’s performance and keep it improving. When we started using Fin over 1y ago, we were at 25% positive resolutions. Now we’re above 70%. You can actively monitor positive resolutions, sentiment, and CSAT to make sure your AI keeps improving and delivering your customers an increasingly positive experience. TAKEAWAY: Every Founder wants to replace entire teams with AI. But nobody wants to do the actual work to make it happen. Everybody expects to flip a switch and have perfect customer service. The reality? You need to treat your AI like your best employee. Train it daily. Give it the resources it needs. Hold it accountable for results. Here’s the truth that the LinkedIn clickbait won't tell you… The KEY to successfully running entire business units with AI? Your AI is only as good as the content you feed it. P.S. Want Robb's CustomGPT? We just launched 6-part video series on how RB2B trained its agents well enough to disappear for a week and let AI run the entire business. Access it + get all our AI tools: https://www.rb2b.com/ai

You’re in an AI engineer interview. Interviewer: Your RAG chatbot starts giving outdated answers as documents change daily. How would you keep it fresh without reprocessing everything? If your documents change but your embeddings don’t, your system is already outdated. Here’s how you fix that in a production setup: 1. Don’t rebuild - detect change Track updates using timestamps, checksums, or versioning. Only reprocess what actually changed instead of re-indexing everything. 2. Go chunk-level, not document-level If a small section changes, update only those chunks. This keeps updates fast, cheap, and scalable. 3. Event-driven ingestion (real-time freshness) Use Apache Kafka to capture document update events in real time. How it helps: 📍Every document change becomes an event (no missed updates) 📍Consumers automatically trigger parsing + embedding pipelines 📍Decouples your system -> ingestion scales independently from updates Result: your RAG system stays continuously updated, not batch-dependent. 4. Clean your vector store actively Use upserts and deletions to replace outdated embeddings. Otherwise, stale chunks will still show up during retrieval. 5. Make retrieval freshness-aware Store metadata like last_updated or version. Filter or boost recent chunks so the model sees the latest information first. 6. Cache carefully Include document version or timestamp in cache keys. Without this, you’ll serve fast but outdated answers. 7. Add observability (this is where most systems fail silently) Use MLflow to trace your entire pipeline. How it helps: 📍Track which document version and chunks were retrieved per query 📍Monitor when embeddings were last updated 📍Debug issues like stale retrieval or hallucination despite fresh data Result: you don’t just update data, you prove your system is using the latest data. #ai #llm #datascience #rag #chatbot #aiengineering #kafka #mlflow #interview Follow Sneha Vijaykumar for more...😊

By now, most of us use AI tools daily. As an experience designer, here is my observation: the shift from task-based to intent-based design is fundamentally changing our discipline. The Interface Paradox Look at any conversational AI, ChatGPT, Claude, Grok, Gemini and more. They’re nearly identical. A text input field. A waiting state. An output response. Yet we have clear preferences. We favor one over another. Why? It’s not the visual design. It’s the quality of output. This is the critical insight: in AI-driven experiences, we’re no longer designing for tasks. We’re designing for intent and outcome. The GUI elements between input and output are minimal, almost invisible. What matters is relevance and accuracy. The Responsibility Gap Users rarely acknowledge poor prompts. When results disappoint, they blame the tool. “This AI sucks.” Never “My prompt sucked.” This is human nature, user psychology 101. The user is never wrong, the system always is. Whether deterministic or non-deterministic, we designers must account for this. We build padding around human error and input quality issues because that’s our job. The New Design Imperative Stop obsessing over visual representation. Start obsessing over output quality. In the age of AI, the experience isn’t what users see between input and output. It’s what they get as a result. That’s where differentiation lives. That’s where user experience is won or lost. #ai #design

Recently, I’ve seen posts like: 💬 “I built my own recruitment chatbot in minutes!” 💬 “AI handles all my candidate conversations now!” 💬 “It's really easy to build a Whatsapp chatbot with one prompt” While I appreciate the enthusiasm, let’s not oversimplify what it takes to build a truly effective recruitment chatbot. Here’s the reality: deploying a chatbot isn’t as simple as connecting it to an LLM and hoping for the best. Without proper architecture, conversation design, and guardrails, you’re likely to end up with: ❌ Inaccurate or misleading responses ❌ Frustrated candidates stuck in dead-end conversations ❌ Non-compliance with legal and ethical standards Creating a chatbot that genuinely adds value requires: 1️⃣ Conversational AI architecture: Mapping candidate journeys, understanding intents, and designing flows that feel seamless and intuitive. 2️⃣ Conversation design: Crafting dialogues that are clear, empathetic, and aligned with your brand voice and customer/user. This isn’t just scripting out a process map, it’s an art and a science. 3️⃣ Guardrails for LLMs: Ensuring the AI doesn’t “hallucinate” inaccurate answers, at risk of prompt injections or violate candidate trust. This means carefully curated prompts, fallback mechanisms, and automated/constant monitoring. 4️⃣ Governance and compliance: Ensuring your chatbot adheres to legal frameworks (GDPR etc.) and doesn’t perpetuate bias or discrimination. 5️⃣ Iterative learning: Chatbots are never “finished.” They need ongoing testing, feedback loops, and training to stay relevant and accurate. So yes, an off-the-shelf or DIY solution might work for basic FAQs, but if you want a chatbot that handles nuanced candidate queries, assesses fit, or aligns with your employer brand? That takes serious expertise, collaboration, and investment. To those of us who’ve spent years perfecting the craft of conversational AI: our work deserves more credit than a “5-minute chatbot” headline can convey. #ConversationalAI #RecruitmentChatbots #AIinHR #RespectTheCraft #TalentExperience