Data professional moving into AI

AI product development is the practice of building product features whose behaviour depends on a machine-learning model, usually a language model. It differs from ordinary product work in three ways: output is probabilistic, quality must be measured with evaluations, and launch plans need guardrails for wrong answers.

Build an evaluation set of at least 50 real inputs with expected outputs, grade responses with a mix of exact checks, model-based graders and human review, and set a pass threshold before launch. Run the same set on every prompt or model change so regressions are caught before users see them.