Bill W:
Chad G:
#16 of 21 in a series: Building SOAPSzone
Story Idea #108 – What I’ve Learned About Working With ChatGPT and Other AI Resources
The biggest lesson from working intensively with AI is that the quality of the relationship matters almost as much as the quality of the model.
AI is extraordinarily good at expanding capacity. It can research, organize, draft, code, brainstorm and help keep track of sprawling projects. It can also misunderstand a direction, over-polish something that should stay rough, confidently follow a bad assumption or become so eager to help that it creates work nobody asked for.
Learning to work with it therefore means learning when to direct, challenge, stop, narrow and ignore it.
One useful pattern is to assign a clear role. If the human is the editor and the AI is the reporter, the division of responsibility becomes easier to understand. The reporter can gather evidence and propose structure. The editor decides which question matters, what angle survives and when the work is ready.
Another lesson is that disagreement is productive. “Are you sure?” “That is not what I meant.” “Go back to the source.” “Do not analyze this yet.” Those are not signs the collaboration failed. They are normal quality control.
Context matters too. AI becomes far more useful when it understands the project’s terminology, standards, workflows and prior decisions. But accumulated context can also make it too eager to infer what the user wants. Explicit instructions still win.
The article will include concrete examples from building SOAPSzone: times AI accelerated months of work, times it made a confident mistake, and times a casual conversation produced an idea neither side was aiming for at the start.
The central lesson is simple: AI works best here neither as a servant nor as an oracle.
It works as a set of powerful collaborative tools with a human still responsible for where the work is going.
