The Iterative Build Loop
Because AI output is unpredictable (Lesson 2), you can’t plan the whole thing up front. You build AI systems by iterating: build → inspect → decide what to try next.
Build, Inspect, Decide
Each turn of the loop is three moves — small on purpose, so you learn something before you commit to the next change.
- Build — the smallest thing that actually produces output.
- Inspect — look at the real outputs, not your assumptions about them.
- Decide — pick the next change most likely to help.
Reliable From Unreliable Parts
This is the whole point. You don’t get a reliable system by planning perfectly up front — you get it by looping tightly and steering.
Deciding What’s Next
Every intermediate result heavily influences the next step. So the skill that separates good AI engineers is reading results and choosing the highest-value next move — not writing the perfect first prompt.
Not Spec, Then Build
Traditional software says: spec everything, then build. AI work says: build a little, learn, adjust. Short loops beat long plans when the output can surprise you.
Build It
How to implement: next time you use an AI tool, work in one-change loops — make ONE change, look at the output, decide the next change from what you saw. Don’t write one giant prompt and hope.
- Weekly AI Tasks tracker — build the message→task parser in loops: try an example, see what it got wrong, adjust the prompt or schema.
- Personal brand site — generate one section, read it critically, refine — don’t generate the whole site blind.
What you learned
You build AI systems by looping: build → inspect → decide. Tight loops turn unreliable parts into a reliable whole, and choosing the next step is the skill that matters most.