Lessons / Intermediate

Second opinions on what's expensive to get wrong

Acting on a single AI's "looks good" for something expensive to get wrong — a board email, a contract clause, a pricing change, an investor update — is a coin flip dressed up as confidence.

The move the best operators make: for anything costly to get wrong, get a second and third opinion automatically, and trust where they agree. A skeptical review pass catches the unclear ask, the tone problem, the commitment you didn't mean to make, before it goes out.

Try it now

Take one real high-stakes thing you're about to send and run it through a critical review first:

Before I send this, review it for anything that could backfire — unclear asks, tone problems, commitments I didn't mean to make, or facts that might be wrong. Be skeptical, not encouraging. Then tell me what you'd change.

[paste the draft]

If you have a tool that fans the same question out to several models (multi-model review), even better — agreement across independent reviewers is real signal.

You've got it when…

The review surfaced at least one real issue you then fixed before sending — or credibly confirmed it was clean. Either way, you acted on more than one opinion for the thing that mattered.

Quiz — did it land?

Your tutor checks these before marking the lesson complete:

1. Why is acting on one model's "looks good" risky for high-stakes work?
2. Route one real artifact through a skeptical review — what did it catch that a single read wouldn't?

That's the intermediate track. You've connected your stack, saved your repeated work, handed the AI a decision rule, and started getting second opinions on what counts. If you want to push it hard — parallel agents, building your own tools — the Power track is next.