AI is fast, capable, and sometimes confidently wrong. Five minutes here on what to trust, what to double-check, and what's safe to put in, so it saves you time instead of costing you.
The most useful way to hold it: you've just hired a brilliant, impossibly fast intern who has read almost everything and will hand you a confident answer to any question in seconds. That intern is right most of the time and wrong some of the time. Here's the catch that trips people up: it sounds exactly the same either way.
Everything on this page is how you get the upside without getting burned by the downside. It's short on purpose. Learn these three things once and you can stop worrying about the rest.
Most of what you'll ask lives in the safe zone. The trouble only starts when you make it the source of truth on something specific and verifiable.
Exact figures, dates, and names. Recent events (it may simply not know them). Math and calculations. Quotes and citations. And anything where being confidently wrong costs you real money or real trust.
Here's the part most people get wrong. When the AI doesn't know something, it doesn't stop or shrug or say "I'm not sure." It fills the gap with the most plausible-sounding answer and hands it over with the same confidence as a fact it's certain of.
That's not lying, and it's not a bug you can turn off. It's how the tool works: it predicts likely-sounding text, and a wrong name or an invented statistic can be very likely-sounding text. The industry calls this a "hallucination." A plainer word is confabulation: filling a blank with something that fits, and believing it.
Ask it, "how sure are you about this, and what's your source?" It will often flag its own shaky spots when you push. But for anything that matters, confirm against a real source. Don't treat its answer as the source.
One rule of thumb keeps you out of almost all the trouble: if you wouldn't put it in an email to a company you don't fully control, don't paste it into a consumer AI tool.
On a personal plan, your chats may be used to improve the models unless you switch that off in settings. Business and enterprise plans generally don't train on your data and add real protections. If you'll be using genuine company information, get on the right plan and check the data settings. We cover the account tiers in Getting Set Up.
None of this should scare you off. The vast majority of your work, the drafting and thinking and organizing and explaining, has nothing sensitive in it at all. This is just the short list of what to hold back.
Four things true, and you can move fast without getting burned.
That's the whole foundation: the mindset, the models, the setup, and the guardrails. From here on it's skill, not setup. First up, the one that changes everything: getting dramatically better results from anything you ask.
Next: Getting Better Results →