Selected Articles.

When I look at an AI product, I start with a quiet question: after a person completes the first task, why would they open it again? A polished demo can avoid ambiguous input, wrong answers and changes of mind. Real work cannot. If every session asks people to explain the context again, guess what the system has done and repair the result somewhere else, the promised time saving has simply moved the work out of frame.

I would rather see a complete path through a task. How does it begin? What evidence or source material does the system show? How can someone correct a mistake? Can the result be saved, handed off or edited further? People also need to know which parts are ready to use and which still require their judgment. Better models raise the ceiling of a demo; these less glamorous details decide whether anyone comes back.

So when an AI product is presented, I want to see more than a one-line prompt. Show me an imperfect input, a human revision and the final deliverable. A product that holds up through all three is closer to real use.

I do not think a general-purpose prompt box should carry the whole experience. It may be fast for an experienced user. For someone arriving for the first time, a blank field asks them to guess how to describe the task and then guess whether to trust the answer. Design does not have to expose every step, but it should give the important choices a place: what problem am I solving, what material am I providing, which direction am I choosing and when do I need to confirm?

Explanation should not become a technical manual either. I prefer to start with a concrete task, offer a clear way in and leave the outcome editable. People who want to look deeper should be able to find sources, limitations and the process behind the result. That gives someone outside the field a way to begin, while giving a specialist enough to assess the work.

A good interface does not announce that “AI has finished” on a person's behalf. It shows what was completed and which decisions remain with the user. As capability grows, clear boundaries become part of how trust is earned.

I do not think the advantage of a small team is squeezing ten people's work into one person. That often creates more half-finished pieces, more checking and faster exhaustion. AI is most useful for gathering and organizing material, exploring variations and drafting, so people can focus on direction, judgment and final review.

The workflow I aim for is simple. Define the deliverable and what would make it usable. Give the tool the material and constraints already known. Have a responsible person check facts, experience and risks, and retain source files that can still be edited. At the end of a task, keep the useful prompts, failed approaches and standards that made the judgment possible. The next task should not begin at zero.

I care more about whether one person can deliver reliably than how much they can produce in a night. Speed can produce an output; a sound method makes quality repeatable. A small team should be able to decide when to accelerate and when to stop and rethink.