Why foundation models still need domain expertise.
A general-purpose model gets you most of the way. The last, hardest mile still belongs to people who understand the domain.
General-purpose foundation models are remarkably capable out of the box, and it's tempting to treat that capability as the finish line. In practice, the gap between a demo that works on curated examples and a system that holds up on real, messy, domain-specific data is where most of the actual engineering happens.
In our own coursework and research, students consistently find that the hardest part of an applied AI project isn't prompting a model or fine-tuning weights, it's understanding the domain well enough to know when the model is wrong. A clinical, legal or agricultural application has failure modes a generic benchmark will never surface.
The programs that produce graduates who can actually ship these systems are the ones that pair AI coursework with deep domain immersion, not just API fluency. That's the model we build our capstone and research placements around: real data, a real domain expert in the room, and a system that has to work outside the lecture hall.