You are right. There is no secret sauce. There are no magic bullets. Careful requirements engineering and careful testing is absolutely what you need.
However -- many of these techniques do transfer to the AI field -- albeit with some tweaking and careful thought.
Requirements are still utterly critical. Phrasing the requirements right is important and requires more than a passing thought -- particularly as concerns testability.
A lot of it boils down to requirements that get placed on the training and validation data sets; and the statistical tests that need to be passed: how much data is required and how you can demonstrate that the test data provides sufficient coverage of the operating envelope of the system to give you confidence that you understand how it behaves.
The architecture is critical also -- how the problem is decomposed into safe, testable and understandable subsets -- which has much more to do with how the system is tested than how it solves the primary problem.
However -- many of these techniques do transfer to the AI field -- albeit with some tweaking and careful thought.
Requirements are still utterly critical. Phrasing the requirements right is important and requires more than a passing thought -- particularly as concerns testability.
A lot of it boils down to requirements that get placed on the training and validation data sets; and the statistical tests that need to be passed: how much data is required and how you can demonstrate that the test data provides sufficient coverage of the operating envelope of the system to give you confidence that you understand how it behaves.
The architecture is critical also -- how the problem is decomposed into safe, testable and understandable subsets -- which has much more to do with how the system is tested than how it solves the primary problem.