1. Expect accurate facts without verification
AI confidently produces plausible text, but can make mistakes and make things up. Blindly trusting facts, numbers and links is a direct path to problems. What to do: check important facts from the source, and connect documents based on your data for answers. Think of AI as a fast but imprecise assistant.
2. Vague requests
“Write something about marketing” gives a vague answer. The model does not read minds - it responds to what you wrote. What to do: Set the role, context, desired format and restrictions. The more precise the request, the closer the result. The quality of the answer largely depends on the prompt.
3. Passive consumption instead of practice
Watching videos and reading ready-made answers is pleasant, but it creates the illusion of knowledge: “I understand” does not equal “I can.” What to do: practice on real problems, decide for yourself before asking, explain the topic in your own words. Skill is born only in practice.
4. Race for every news
AI changes quickly, and trying to keep track of everything is exhausting and futile. What to do: stick to the foundation (the principles of prompting and working with AI change slowly), choose 2-3 reliable sources and study for a specific task, and not for future use. Basics become obsolete slowly, wrappers quickly.
5. Try to learn everything at once
The AI is wide, and the dispersion slows down progress. What to do: choose one direction for your goal (no-code work, development or career) and go deeper. One completed skill is more valuable than ten started ones.
6. Trust blindly and not think for yourself
If you ask AI to think for you, your own skill does not grow, and model errors go unnoticed. What to do: Use AI as an amplifier, not a replacement for thinking. Check the logic, make the final decisions yourself. You are an editor, not a performer.