AI Literacy: Understand What the System Can Actually Do
AI literacy is not memorizing a list of tools. It is learning how to ask what a system did, what it did not do, and what evidence would change your mind.
Separate capability from reliability
A model may produce an impressive answer once and still be unreliable for repeated work. Test the task across ordinary cases, edge cases, and cases where the correct response is to ask for help or refuse.
Keep a receipt
For important work, record the input, the output, the model or tool used, and the human review that followed. A receipt turns an anecdote into something another person can inspect.
Measure the boundary
The useful question is rarely whether AI is good or bad in general. Ask where it helps, where it adds review burden, and where a simpler process is better. That boundary is the beginning of responsible adoption.
Learn by doing
Build In Public University connects ideas to experiments. Try the AI ROI audit or explore the Arcade.