AI is great at widening your thinking.
It is not great at narrowing your decisions.
That sounds subtle, but it explains a lot of the “why did we ship that?” moments.
An LLM can brainstorm alternatives, surface edge cases, draft an implementation, even argue both sides. It’s a force multiplier for exploration.
But commitment requires something different: evidence.
- What requirement does this satisfy?
- What constraint does this respect?
- What test proves it works?
- What could fail in production?
If you let the tool make decisions by vibe, you get fast output and slow rework.
If you use it to widen the solution space, then use checks to narrow the final choice, you get something better: speed you can trust.
A practical pattern
- Explore wide with the model (options, tradeoffs, failure modes).
- Decide narrow with artifacts (acceptance criteria, diffs, tests, review).
Use AI to widen thinking.
Use evidence to narrow decisions.