Most of the risk in an AI project is settled before any code is written. Scoping is where we decide what the tool must do, what it must not, and how we'll know it worked. We spend real time here because it is cheaper than discovering the answer halfway through the build.
We start with the problem in its own terms, not as a feature list. What decision or task is this tool meant to support? What does a good outcome look like, concretely? Where is AI actually adding leverage, and where would simpler logic do?
Then we prototype the AI approach against real examples before committing to it. AI behavior is empirical — it has to be tried, not assumed. A small prototype tells us more than a long specification.
By the time we build, the uncertainty is mostly gone. The result is a tool that does what was agreed, because the agreement was grounded in evidence rather than optimism.
—How Skynarc thinks about scope.