Generative AI 101

Your AI chatbot thinks your ideas are brilliant. That might be the problem. In this episode of Generative AI 101, host Emily Laird examines AI sycophancy, the tendency of chatbots to agree with users even when they're wrong, and how that false validation can undermine critical thinking. Learn how to turn AI into a constructive critic using a simple pre-mortem technique that challenges your assumptions before they become expensive mistakes.
 

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Your chatbot thinks your idea is brilliant, and that should worry you. In this episode of Generative AI 101, host Emily Laird explains AI sycophancy: why models trained on thumbs-up ratings learn to agree with you, and why the people most convinced the machine is objective get hurt the most. A tool that applauds your business plan, your pricing, or your pitch is an echo, not an advisor. You’ll leave with a seven-step playbook (ask instead of tell, run a pre-mortem, convene a skeptic panel, and more) for teaching the yes-machine to say no.
 

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http://www.linkedin.com/in/meet-emily-laird

Ask AI to build a workshop and it will hand you a polished agenda without knowing who’s attending, how much time you have, or what success looks like. In this episode, host Emily Laird turns the usual advice about writing better prompts on its head and argues for making AI interview you first, drawing on research from Berkeley, Google, and Stanford on clarifying questions, hidden preferences, and AI sycophancy. You’ll walk away with the Five-Question Interview, a simple technique that makes you define your objective, name your constraints, and weigh trade-offs before AI generates a single word. The catch: a thoughtful conversation can still produce a wrong answer, so the thinking (and the verifying) is still your job.
 

🎯 JOIN THE AI WEEKLY MEETUPS

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One AI model can win gold at the International Mathematical Olympiad while another leading model can only read an analog clock about half the time, so what do those record-breaking benchmark scores actually tell you? In this episode, host Emily Laird explains AI evals: how researchers test these systems, why leaderboards go stale within months, and why an AI agent saying “task complete” doesn’t prove the task got done. She also looks at the gap between feeling faster with AI and actually being faster. Then she gives you a simple way to run your own evaluation, so you can decide whether a tool is reliable enough for the work you actually do.
 

🎯 JOIN THE AI WEEKLY MEETUPS

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📩 EMAIL REMINDERS FOR THE MEETUPS

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💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

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Bigger context windows were supposed to solve everything, but research on “lost in the middle” and context rot shows that AI can have the right answer in front of it and still miss it. In this episode, host Emily Laird explains context engineering, which means deciding what information, examples, and history an AI system actually needs to do the job. She covers why one model’s accuracy fell from over 99 percent to roughly 70 percent as its inputs grew, why more examples can still help, and why memory is not the same as context. You also get the Context Audit, a four-step method for cutting the clutter and testing whether your AI is actually using what you give it.
 

🎯 JOIN THE AI WEEKLY MEETUPS

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📩 EMAIL REMINDERS FOR THE MEETUPS

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💬 CONNECT WITH EMILY LAIRD ON LINKEDIN

http://www.linkedin.com/in/meet-emily-laird