Introduction

You’re past the beginner stage with AI. You use it several times a week for anything from research and content to strategy, client work, and admin. But lately, you’ve been getting more frustrated with the amount of time you spend fixing output: a statistic that didn’t hold up, a recommendation that missed something obvious about your market, an idea AI called strong that a colleague later questioned.

AI delivers wrong answers and poor recommendations with the same confident tone as correct ones

Your usual way of judging human answers relies on cues that AI will never give you. With a person, you can hear the uncertainty, the pause, the "let me check." AI sounds the same whether it's sure or guessing. Push back, and a person will defend a good answer. AI can cave to the challenge and agree with you, whether it was right or not. So, the burden shifts to you: if you want to know whether an answer is usable, you must test it.

How You Decide Right Now

Every time AI gives you an output, you have to make a decision about its quality and relevance. It might read well and match what you expected, possibly with a bit more refinement. Or something seems off, so you drop the whole thing and do it yourself. Both come down to the same thing: a gut reaction to the output versus testing what's in it. That back-and-forth of leaning on AI one day and distrusting it the next takes away the productivity potential you were hoping for in the first place.

What You’ll Build in This Workshop

In this workshop, you’ll build a short, repeatable set of checks matched to the specific ways AI fails and then test it on a real AI output from your own business. You’ll have a system you’ve already tried out once, not a set of notes you’ll never open again.

To show each piece of this workshop in action, you'll follow Maya, a marketing consultant who uses AI to size up competitors, write client-facing copy, and work through her own ideas. Most of the time, she acts on what AI gives her, with no reliable way to distinguish between good outputs and risky ones.

What You’ll Learn in This Workshop

By the end of this workshop, you'll know how to test AI output before you act on it, using an example from your own business. You'll be able to:

• Know within minutes whether an AI output is safe to act on, or whether it needs a closer look first.

• Spot the three specific ways AI misleads you (false confidence, sycophancy, and missing context) before they shape a decision you can't easily undo.

• Get AI to lay out its own reasoning and name what it assumed about your business, instead of taking a polished answer at face value.

• Make AI argue against its own answer, so you catch the weak spots before a client or a customer does.

• Scan any AI output in about two minutes with a checklist that tells you whether to trust it, adjust it, or verify it.

• Walk away with your own Personal Evaluation Protocol, a written rulebook for exactly how much checking each type of AI task needs.

Action Steps:

1. Look through your AI chat history and choose one output you used or relied on in the past week.

2. Write down how you decided it was good enough to use.