Five lessons on turning a vague idea into a buildable specification.
Key takeaways
Most scoping goes wrong when it starts with a feature list instead of a clear problem statement. The course begins by having teams write, in plain language, exactly what task is slow or error-prone today.
This single step prevents a huge share of scope creep later, because every proposed feature can be tested against whether it actually solves that original problem.
Just as important as what the assistant should do is what it should never do — approve a payment, send an external message without review, or make a legal claim, for example.
Writing these boundaries down early gives the developer a concrete guardrail to build against, rather than leaving safety to be inferred.
Instead of abstract requirements, the course pushes teams to gather ten to twenty real examples of the task being done well, and a handful of edge cases where it goes wrong.
These examples become the test set the prototype is measured against, which is a far more reliable signal than a written description alone.
A rough working version that handles the core case well is worth more than a comprehensive spec document that's never been tested against real data.
The course closes by having teams build and test a narrow prototype before expanding scope, so problems surface while they're still cheap to fix.
A 30-minute call is enough to tell you whether AI pays for itself here.
