Which AI role should take this task?
Not every AI is good at the same kind of work.
Start with what the task needs, then hand it to the AI role that is stronger at that kind of work—instead of asking every role to work the same way.
Writing code, analyzing research, breaking down requirements, making project decisions, organizing information, and checking results can all look like work you could simply “give to AI.” In practice, they ask for different strengths. A role that is great at producing code quickly may not be the best one for untangling a long, messy context; a role that is strong at analysis and judgment does not need to own every execution task.
Start with the task, then choose the role
FinchStory puts more weight on matching the work to the talent. Look at what the task actually needs, then give it to the role that fits. You should not have to study a long list of model specs first; what the role is good at, and what the task needs now, are usually the more useful signals.
Division of work matters more as projects grow
The longer a project runs, the more useful those differences become. One role can shape the requirements and plan, another can implement, and a third can review and tidy up the final result. More roles are not automatically better—the point is to let each one spend more time on the work it does best.
The goal is not to spend less time “choosing a model.” It is to spend less time redoing work because the wrong role took the task.
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