If you read nothing else in this guide, read this section. It is the foundation for everything that follows, and it is enough to keep you out of the most common trouble.
1. You are talking to a system, not a mind. The model generates an answer one token at a time. At each step it calculates which tokens are plausible next, based on patterns learned during training and the context in front of it. That process can produce remarkably good explanations. It does not come with a built-in guarantee that the explanation is true. When the model is wrong, the writing may sound just as confident as when it is right.
2. Do not assume it knows you, your company or today unless the product supplies that context. A model's built-in knowledge comes from its training and subsequent updates. It should not be assumed to contain today's information. Everything else, from your documents and policies to this morning's news, arrives only if the application supplies it: a file you attach, a search it runs, a connector you enabled. If the required information is not available to the system, the model may still produce a plausible answer rather than reliably telling you that the evidence is missing.
3. Fast is not the same as finished. The tool gets you to a plausible draft in seconds. Checking whether the draft is right still takes human time, and that time does not disappear. It moves. AI changes where the work happens: drafting effort falls while review, evidence checking, exception handling and accountability become the larger part of the job. Professionals who skip the review are not faster. They are wrong sooner, with more confidence.
4. Anything you paste crosses a data boundary. Depending on the service, account type, settings, contractual terms and feature in use, your input may be retained, logged, processed by additional systems, reviewed under defined circumstances, or used for service improvement or model training. Before pasting anything from work, know three things: what classification the material carries, which account you are logged into, and whether your employer has approved that account for that material. If you cannot answer all three, do not paste it. Test the idea with a public or made-up example instead.
5. The product changes under you. The same product name may run a different model next month, gain memory, switch search on or off, or add a connector. Treat every consequential use as if it were the first time.
6. Start with one task you can judge. Not a course. Not a certificate. Pick one piece of work you already do well enough to know when it is wrong, try the tool on it, keep the failures, and measure whether it improved. That single experiment will teach you more than a week of tutorials, because you can tell when the output is wrong.
7. Your profession is the asset. The scarce skill is not prompting. It is knowing what a correct answer looks like in your field, what evidence would change it, and who is accountable for the decision. AI extends that judgement. The people who let it atrophy will find they have less to offer, not more.