Evaluation — judging whether a model’s output is correct, safe and useful — is now an entry-level job category on its own, and it is the fastest on-ramp we have found into the wider AI economy.
We teach it as a rubric, not a vibe: define the criteria before you see the output, score against the criteria, then write one sentence justifying the score a stranger could audit. That single habit is what separates a hobbyist from someone an employer will pay.
Every graduate of our AI track leaves with a portfolio of scored evaluations, not just a certificate.