AI answers sound confident whether they're right or wrong. That's the thing I think everyone should keep in mind, especially for anything involving money, health, law or numbers. A simple habit: when an answer matters, follow up with "Where did that come from? Give me the source, and tell me which parts you're unsure about." Two things happen. Sometimes it points to a real source you can check in a minute. And sometimes it admits that part of the answer was a guess, which is exactly what you needed to know. It doesn't make AI less useful. It makes it more useful, because you know which parts to trust and which parts to double-check before acting on them. Has an AI answer ever sounded right but turned out to be wrong for you?

If you've ever asked AI to write something and it came out sounding like… AI, try flipping it around. Instead of "write a LinkedIn post about my business", say: "Interview me about my business, one question at a time. Ask follow-up questions when my answers are vague. After 8 questions, summarize the three most interesting things I said." The questions pull out real stories, numbers and opinions that a generic prompt would never know. Then you write from that summary, or ask it to draft using only what you said. It works for bios, sales pages, reflection, even planning your week. The best material was already in your head. You just needed someone to ask. What would you want AI to interview you about?

For this Hermes Agent example, sample input: A sample service stores valuable work but offers no usable bulk export. Include exit requirements in the comparison before the team becomes dependent on it. What would you need to recover if you changed tools next year?

When evaluating Hermes Agent, a fictional connector can read contacts but cannot update their status. The decision I would test: check the required operation, not merely the name of the integration. Which read-versus-write distinction has surprised your team?

For a proposed Hermes Agent workflow, what imperfect input would you bring to a vendor evaluation? A sample demonstration uses complete spreadsheets while the proposed client data has missing rows. Test a representative imperfect input before treating the demo as evidence of fit.