AI-Powered Virtual Assistants

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

Gmail’s AI email assistant writes like a committee of lawyers designed it. Pete Koomen’s recent post Horseless Carriages explains why: developers control the AI prompts instead of users. In his post he argues that software developers should expose the prompts and the user should be able to control it. He inspired me to build my own. I want a system that’s fast, accounts for historical context, & runs locally (because I don’t want my emails to be sent to other servers), & accepts guidance from a locally running voice model. Here’s how it works: 1. I press the keyboard shortcut, F2. 2. I dictate key points of the email. 3. The program finds relevant emails to/from the person I’m writing. 4. The AI generates an email text using my tone, checks the grammar, ensures that proper spacing & paragraphs exist, & formats lists for readability. 5. It pastes the result back. Here are two examples : emailing a colleague, Andy (https://lnkd.in/gtjt3BPp), & a hypothetical founder (https://lnkd.in/gDwM4f22). Instead of generics, the system learns from my actual email history. It knows how I write to investors vs colleagues vs founders because it’s seen thousands of examples. The point isn’t that everyone will build their own email system. It’s that these principles will reshape software design. - Voice dictation feels like briefing an assistant, not programming a machine. - The context layer - that database of previous emails - becomes the most valuable component because it enables true personalization. - Local processing, voice control, & personalized training data could transform any application, not just email, because the software learns from my past uses We’re still in the horseless carriage era of AI applications. The breakthrough will come when software adapts to us instead of forcing us to adapt to it. Centered around a command line email client called Neomutt (https://neomutt.org/). The software hits LanceDB, a vector database with embedded emails & finds the ones that are the most relevant from the sender to match the tone. The code is here (https://lnkd.in/gZ-AaAWa).

Anthropic just shipped Skills, Microsoft 365 integration, and enterprise search for Claude. After talking to dozens of enterprise companies this year, I think they're solving the right problems. 💰Context tax is killing enterprise AI adoption. Most AI tools require you to manually gather information before asking useful questions. You're copying emails, uploading documents, explaining organizational context. The AI might be smart, but you're doing all the integration work. Claude's Microsoft 365 connector changes this. Direct access to SharePoint, Outlook, Teams, and OneDrive means the AI already knows what your organization knows. Ask about Q3 strategy, and it pulls from the actual discussions, documents, and decisions. They also launched Skills — reusable instruction bundles that work across Claude's web app, API, and command-line tool. Think of these as expertise packages—instructions, scripts, and resources Claude loads on-demand. And lastly, the new Enterprise search is a shared project that searches multiple connected tools simultaneously. One query pulls information from HR docs in SharePoint, email discussions in Outlook, and team guidelines from various sources—then synthesizes it into a single answer. Model providers like Anthropic and OpenAI are realizing that enterprise AI needs to be operational, not just conversational. Less chatbot, more sidekick that accesses your actual systems and takes action.

Some AI assistants are like eager interns. Others are like PhDs in growth. The difference? Context. Soon, everyone in go-to-market will have an AI assistant. It’ll answer questions, give recommendations, and even do work. The potential is huge! But for an assistant to help you deliver results, it needs context on your company, customer, and role. Imagine a marketer asking an AI assistant to create a newsletter. Without context, it will create a generic email, like something any company could’ve written. But with context, it will craft a newsletter in their brand voice, with relevant information for each segment, and a subject line informed by past campaigns. American City Business Journals saw the importance of context firsthand. With their expert marketing team at the helm, they used Breeze Assistant to create and refine personalized emails based on each contact's location, industry, and pipeline stage. Open rates and conversion rates quickly improved. Breeze was also able to tell them which contacts not to email, allowing them to take a more targeted approach. Any AI tool could have churned out 1 million generic emails in seconds. But with human-led strategy and testing, Breeze Assistant helped them send fewer, better emails and increase results. Our team has been making great progress with Breeze Assistant, adding more context and training it on everything from HubSpot’s Academy videos to Loop marketing. GTM leaders, how are you using AI assistants in your role today?

I've built 67+ AI agents in n8n. At first, I thought adding nodes and optimizing connections was what mattered. But I never really trusted them. Every output felt like a gamble. The bottleneck wasn't my architecture. It was my instructions. Avoid my mistakes and: 1. Separate static facts from inputs. Mixing them makes the agent guess context it should already know. → Example: Static = “Store opens at 9 AM.” Dynamic = “Order ID: 48281.” 2. Make the agent call out missing info. Guessing is the #1 source of silent failures. → Example: MISSING_FIELD: customer_email. 3. Force it to plan before acting. Step-planning stabilizes reasoning and reduces randomness. → Example: Plan internally. Output only the final result. 4. Give a fallback for impossible tasks. Without a fallback, the agent hallucinates a solution. → Example: ERROR_REASON: date_format_invalid. 5. Define “If X → Do Y” rules. Deterministic branching kills unpredictability. → Example: If date can’t be parsed → ask for a new one. 6. Allow creativity only where needed. Uncontrolled creativity = guaranteed hallucinations. → Example: Creative only in “Rewrite.” Everything else literal. 7. Limit the agent’s memory. Too much history makes the agent drift off-task. → Example: Use only the last 2 messages to determine intent. 8. Make it restate the task first. Repetition confirms the agent understood the request correctly. → Example: Task summary: extract the invoice number. 9. Validate inputs before generating outputs. Output built on bad inputs = guaranteed bad outputs. → Example: Invalid date: expected YYYY-MM-DD. 10. Require a termination signal. Your workflow needs a clear signal that the task is complete. → Example: End with “TERMINATE.” 11. Test your instructions with ugly inputs. If it only works on “happy path,” it’s not reliable - it’s lucky. → Example: Missing fields, malformed dates, weird formats. 12. Run a 10–20 sample eval before shipping. You can’t improve what you don’t measure. Vibes ≠ validation. → Example: Score each output: accuracy, format, tone, stability. 13. Iterate based on failures, not feelings. One word in your instructions can double your success rate. → Example: 2 outputs broke the format → tighten output rules. This is how you get from 30% to 80% success rate. Better instructions beat complex architecture. What's been your biggest challenge getting agents to behave consistently?

Most people are learning AI agents in the wrong way! They jump straight away to n8n, Lang-graph, or Relay.app. Here is what to do instead ⬇️ Step 1: Understand the workflows that agents replace Before touching any tool, map the “old way vs new way.” Deep research → Coding → Contract review → Customer support → Onboarding → Analytics → Compliance. If you can’t articulate the workflow, the tool won’t save you. (See the table in the image, that’s the real starting point.) Step 2: Identify the opportunities hidden inside these workflows Where is time wasted? Where does mental fatigue happen? Where does shallow thinking creep in? Agents only create leverage where the underlying workflow is broken. Step 3: Convert the workflow into a structured agent behavior Intent → Actions → Tools → Memory → Output. This is where most people go wrong: They build flows without defining why the agent exists or what success looks like. Step 4: Only now you bring in n8n / LangGraph / Relay Tools are just implementation details. Agents are product decisions. If you skip the thinking → you build brittle toys. If you start with thinking → you ship durable automations. Step 5: Validate with evals before scaling Don’t trust vibes. Test for errors, hallucinations, latency, and failure modes before calling anything “production ready.” If you understand workflows, opportunities, and failure modes, your agents will outperform 99% of what people are posting today. Don't build agents for creating beautiful LinkedIn posts, create agents for solving real problems!

7 mistakes in building AI agents. After launching dozens of AI agents, these are the patterns I keep seeing. The technology is rarely the bottleneck. Most issues appear long before launch. 1️⃣ Dumping files and calling it knowledge Files are not a knowledge base. Messy inputs create messy answers. Fix: Clean the data. Remove duplicates. Create one source of truth. 2️⃣ Starting with documents, not questions Teams often start with what exists. Users start with what they need. Fix: Map real questions first. Build from actual use cases. 3️⃣ No data owner Everyone uses the data. Nobody owns the update. That is how answers become wrong. Fix: Assign owners. Set review cycles. Make updates someone's job. 4️⃣ Weak governance AI exposes inconsistencies fast. Old versions appear. Conflicting numbers appear. Missing context appears. Fix: Define approved sources. Track changes. Make ownership visible. 5️⃣ Assuming users know how to prompt Most people do not need theory. They need examples. Fix: Teach AI literacy. Show good prompts. Show bad prompts. Repeat often. 6️⃣ Treating access as an afterthought Useful data is often sensitive data. Fix: Design permissions early. Separate audiences. Involve IT before launch. 7️⃣ Launching without feedback loops Launch is not the finish line. It is the beginning. Fix: Track errors. Collect feedback. Continuously improve the source. Most AI projects do not struggle beause of AI. They struggle beause of: * poor data * unclear ownership * weak governance * low AI literacy The model is often the easiest part. If I took away your AI tool tomorrow, Would your underlying data survive the test?

Can You Tell the Difference Between AI and Human-Written Emails? I had the opportunity to serve as an expert judge for a Washington Post feature by Geoffrey Fowler, evaluating five emails written by different AI tools—Claude, DeepSeek, ChatGPT, Copilot, and Gemini—plus one from a human writer. The challenge? Figure out which email was the best. The results? Claude won - (it even beat the human writer!) Here’s why—and what it tells us about the future of AI-powered communication: -AI Emails Are Improving—But Not All Are Created Equal Each AI-generated email had a distinct style. Some were overly formal, others were robotic, and a few completely missed the mark. Claude’s writing stood out for being the most natural, structured, and persuasive. But here’s the catch: AI-generated emails don’t all sound the same. Each tool has its own strengths and weaknesses:   •  Claude excelled at clarity, nuance, and sounding the most human.   •  ChatGPT was engaging but sometimes too wordy.   •  Copilot was direct but lacked warmth.   •  Gemini and DeepSeek struggled with context and precision. The takeaway? Choosing the right AI assistant matters. - The Best AI Emails Feel Personal—Without Overdoing It One of the biggest issues with AI-generated emails is tone mismatch. Many sound overly polished—so much so that they feel fake or even manipulative. For professionals using AI to write emails, this is key: Your AI assistant should enhance your tone, not erase it. - AI Is Great at Writing—But Terrible at Judgment One thing was clear from judging these emails: AI is brilliant at sentence structure but still struggles with discernment. Some AI tools misread the email’s context, making unnecessary recommendations. None of the AI-generated emails fully adapted to the emotional nuances of the request. This reinforces what I always tell executives: AI can help draft, but humans must decide. Before sending an AI-assisted email, ask yourself: -Does this actually answer the question? -Does it sound like me? -Would I feel good receiving this? The best professionals don’t just use AI—they train it to reflect their judgment, communication style, and emotional intelligence. Can you detect the difference between AI-written and human emails? Share your thoughts in the comments! (and full article in comments!)

The era of AI tools is over. Welcome to AI teammates. We’re now building autonomous agents that operate like team members. These agents are more than personas. They're modular, trained, role-specific assistants that can: - Execute repeatable workflows - Interpret and adapt based on uploaded data - Hold persistent memory of your style, tone, or SOPs - Integrate with APIs, tools, and automation stacks Here’s how to leverage them strategically — not just play with them: ✅ 1. Treat your agent like you're hiring an ops lead Think in terms of delegation, not automation. Write a role description. Define its scope. Explain what “done well” looks like. The clearer the initial “onboarding,” the better the performance. ✅ 2. Build with process, not just prompts Upload reference documents (templates, decks, SOPs). Guide it through your systems and workflows. Remember: AI needs context to become competent. ✅ 3. Anchor it to a specific business function General assistants give general outputs. But an “Investor Memo GPT” or “Weekly Analytics GPT” gets to business faster. Function > title. ✅ 4. Use feedback loops aggressively Agents improve with structured input. Keep a running log of breakdowns, weak spots, and edge cases. Update your instructions like you would a knowledge base or playbook. ✅ 5. Operationalize with real stakes Move beyond play. Deploy agents where they reduce real friction: Client onboarding, lead follow-ups, performance reports, etc. Start with low-risk, high-frequency tasks. Then scale. This isn’t another toy. This is the beginning of a new interface between leadership and execution. 💡 Want to see the full framework I use to deploy GPT agents across sales, content, and research ops? 📩 Subscribe here to get it → https://lnkd.in/gCV3_Raw

Built an AI-powered onboarding assessment tool you can use for free. It isn't like a typical AI, you've chatted to. I've configured this one to be extra sassy. How it works: 1. You paste any knowledge base (training materials, product docs, policies), it extracts the key knowledge areas, then an AI voice agent conducts a natural 5-minute conversational quiz with the new hire. 2. At the end of call, you get an instant scorecard with grades per area and personalised feedback. The voice bit uses ElevenLabs conversational AI which is surprisingly good at handling 'I don't know' gracefully and moving on (instead of getting stuck in a loop like most chatbots). (Warning: I’ve set up the agent to be fairly militant with not letting you get away with weaselly answers) No signup required. Paste content, start quiz, get results. Built it for onboarding assessment but reckon it works for any knowledge transfer verification... Most companies still do this manually (if at all). HR folk scheduling 30-minute sessions to quiz new hires on the employee handbook. Dave from compliance reading out policy questions like it's 1995. Meanwhile, the new starter's pretending to listen while secretly wondering if they can expense lunch… Let me know what you think in the comments. Give it a whirl (no API keys or sign-up required): https://lnkd.in/ekf2y2-5