One of the clearest ways I have found to explain working with AI is through LEGO.
My son loved LEGO when he was younger, like a lot of kids do. He loved the feeling of putting the small pieces together himself and seeing the thing become real in his hands. There is a real pride in that.
Free building is one kind of play. You make whatever comes to mind. But building a LEGO set from the box is different. The goal is not just to make something. The goal is to understand the system well enough to complete it.
When I was teaching him how to build a set, I kept coming back to a few simple habits.
First, sort the pieces.
Then follow the directions step by step.
Do not rush ahead.
Check the work as you go.
And when something looks wrong, do not panic. Go back through the directions, find where the mistake happened, correct it, and keep moving forward.
That is also one of the best ways I have learned to work with AI.
The sorting stage is planning.
Before I ask AI to build, write, design, code, or research anything, I need to understand the pieces on the table. What kind of work is this? Who normally does this work? What would they need to know? What steps would they usually take? What materials, references, tools, examples, or constraints matter?
That planning step matters because AI does not remove the need for structure. It actually makes structure more important.
Once the pieces are sorted, the next part is following the directions.
In AI work, that means moving through the process one step at a time. Ask for the plan. Check the assumptions. Build the first piece. Test it. Then move to the next piece. The more complex the project, the more important it becomes to slow down and verify each step before stacking more work on top of it.
And when something breaks, the LEGO analogy still holds.
You work backwards.
You look at the instructions. You compare the expected result to what is actually in front of you. You find the step where things drifted. Maybe the prompt was unclear. Maybe the goal changed. Maybe the wrong file was edited. Maybe a small assumption got carried forward until it became a bigger problem.
Then you correct that piece and continue.
That is the part of AI that feels most misunderstood. Good results do not usually come from one perfect prompt. They come from a working process. You sort. You build. You check. You backtrack. You rebuild.
AI can move incredibly fast, but the human role is still direction, judgment, verification, and repair.
At its simplest, that is how I think about building with AI.
It is not magic.
It is a box of pieces, a set of directions, and the patience to notice when something does not fit.